Choosing the Best Idea

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The 8-Box Framework for Innovation

Choosing the Best Idea

GUEST POST from Mike Shipulski

We have too many ideas, but too few great ones. We don’t need more ideas, we need a way to choose the best one or two ideas and run them to ground.

Before creating more ideas, make a list of the ones you already have. Put them in two boxes. In Box 1, list the ideas without a video of a functional prototype in action. In Box 2, list the ideas that have a video showing a functional prototype demonstrating the idea in action. For those ideas with a functional prototype and no video, put them in Box 1.

Next, throw away Box 1. If it’s not important enough to make a crude physical prototype and create a simple video, the idea isn’t worth a damn. If someone isn’t willing to carve out the time to make a physical prototype, there’s no emotional energy behind the idea and it should be left to die. And when people complain that it’s unfair to throw away all those good ideas in Box 1, tell them it’s unfair to spend valuable resources talking about ideas that aren’t worthy. And suggest, if they want to have a discussion about an idea, they should build a physical prototype and send you the video. Box 2, or bust.

Next, get the band together and watch the short videos in Box 2, and, as a group, put them in two boxes. In Box 3, put the videos without customers actively using the functional prototype. In Box 4, put the videos with customers actively using the functional prototype.

Next, throw way Box 3. If it’s not important enough to make a trip to an important customer and create a short video, the idea isn’t worth a damn. If you’re not willing to put yourself out there and take the idea to an important customer, the idea is all fizzle and no sizzle. Meaningful ideas take immense personal energy to run through the gauntlet, and without a video of a customer using the functional prototype, there’s not enough energy behind it. And when everyone argues that Box 3 ideas are worth pursuing, tell them to pursue a video showing a most important customer demonstrating the functional prototype.

Next, get the band back together to watch the Box 4 videos. Again, put the videos in two boxes. In Box 5 put the videos where the customer didn’t say what they liked and how they’d use it. In Box 6, put the videos where the customer enthusiastically said what they liked and how they’ll use it.

Next, throw away Box 5. If the customer doesn’t think enough about the prototype to tell you how they’ll use it, it’s because they don’t think much of the idea. And when the group says the customer is wrong or the customer doesn’t understand what the prototype is all about, suggest they create a video where a customer enthusiastically explains how they’d use it.

Next, get the band back in the room and watch the Box 6 videos. Put them in two boxes. In Box 7, put the videos that won’t radically grow the top line. In Box 8, put the videos that will radically grow the top line. Throw away Box 7.

For the videos in Box 8, rank them by the amount of top line growth they will create. Put all the videos back into Box 8, except the video that will create the most top line growth. Do NOT throw away Box 8.

The video in your hand IS your company’s best idea. Immediately charter a project to commercialize the idea. Staff it fully. Add resources until adding resources doesn’t no longer pulls in the launch. Only after the project is fully staffed do you put your hand back into Box 8 to select the next best idea.

Continually evaluate Boxes 1 through 8. Continually throw out the boxes without the right videos. Continually choose the best idea from Box 8. And continually staff the projects fully, or don’t start them.

Choosing the Best Idea Infographic

Image credits: Gemini

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Voice of Customer

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A Complete Guide to Building VoC Programs That Drive Action

Voice of Customer

by Braden Kelley and Art Inteligencia

Most organizations have a voice of customer program. Most of those programs are not working as well as they think they are.

The evidence is clear: organizations are collecting more customer feedback than ever before — surveys after every interaction, NPS scores, CSAT measurements, review monitoring, social listening — and yet customer experience scores across most industries are declining, not improving. Forrester’s CX Index reached a new low after four consecutive years of decline. The volume of customer feedback is going up while the quality of experience is going down.

The problem is not that organizations are not listening. The problem is what they are listening to, how they are interpreting it, and most importantly what they are doing — or not doing — with what they hear.

This guide addresses all three: what voice of customer actually is, how to build a program that produces genuine insight rather than noise, and how to connect that insight to the experience improvements that protect revenue and build loyalty.

What is Voice of Customer (VoC)?

Voice of Customer (VoC) is the systematic process of capturing, analyzing, and acting on what customers say, feel, and expect about their experience with your organization — across every channel where feedback exists, solicited or not.

The definition matters because each component is frequently missing in practice:

  • Capturing — Most programs capture some feedback. The best programs capture it across all channels where customers express themselves, including the unsolicited channels (reviews, social media, support transcripts) that contain the most honest signal
  • Analyzing — Collecting feedback without meaningful analysis produces data, not insight. Analysis requires making sense of patterns across sources, segments, and time — not just reporting average scores
  • Acting — The most common VoC failure is not acting on what is heard. Common challenges include collecting feedback but failing to act on it, feedback being siloed in different departments, a lack of ownership, or treating VoC efforts as one-off projects rather than ongoing initiatives. A VoC program that produces reports nobody reads or insights that don’t change decisions is an expensive exercise in organizational theater

The global VoC customer analytics market reached USD 1.7 billion in 2024 and is projected to grow to USD 4.7 billion by 2030 at a CAGR of 18.8% — driven by organizations recognizing that customer understanding is a competitive advantage. But the investment in VoC technology is outrunning the organizational capability to use it well.

Why Voice of Customer Programs Fail

Before addressing how to build a VoC program that works, it is worth understanding why so many don’t. The failure modes are consistent:

Listening to what customers say rather than what they mean
The gap between what customers say in surveys and what they actually experience is one of the most important and underappreciated problems in VoC. Customers are unreliable reporters of their own experience — they rationalize, forget, and moderate their responses based on social context. A customer who gives a service interaction 4 out of 5 may have found the interaction frustrating but felt it would be unfair to give a low score. A customer who gives a product 5 stars on first use may churn six months later when the value realization gap becomes apparent. Survey scores are a filtered, lagged, incomplete signal of the actual experience. A true voice of customer strategy goes beyond collecting data points — it is about understanding the emotions, motivations, and context behind customer behavior.

Measuring moments rather than journeys
Most VoC programs are built around transactional touchpoints — surveys after a support interaction, NPS at renewal, CSAT after purchase. These measurements capture how customers feel at specific moments, but they miss the cumulative experience across the full journey that actually determines loyalty. A customer can give 5-star ratings at every measured touchpoint and still churn — because the unmeasured journey between those touchpoints was frustrating enough to produce a departure decision that the measurements never captured.

Siloing feedback by function
When product feedback goes to product, service feedback goes to support, and NPS scores go to marketing, each function hears the part of the customer voice that touches them and misses the rest. The result is a fragmented picture of the customer experience that reflects organizational structure rather than customer reality. The most important insights often live at the intersections — the connection between a broken onboarding experience (product) and the support contacts it generates (service) and the churn it eventually drives (revenue) — which are only visible when feedback is integrated across functions.

Confusing feedback collection with insight generation
Volume of feedback is not a proxy for quality of insight. Organizations that survey every interaction and monitor every review channel are drowning in data while starving for understanding. The measure of a VoC program is not how much feedback it collects — it is how reliably it produces specific, actionable insights that change decisions and improve the experience.

The action gap
Companies with mature VoC programs spend 25% less to retain customers and see 15–20% higher cross-sell and upsell success. But maturity requires closing the gap between insight and action — which most programs fail to do. Insights that are not connected to specific improvement owners, timelines, and success metrics consistently fail to produce change.

The Three Types of VoC Data

Effective VoC programs collect feedback across three distinct types, each providing different and complementary signal:

Direct feedback — Feedback customers intentionally provide when asked: surveys (NPS, CSAT, CES, post-purchase, post-service), interviews, focus groups, and advisory boards. Direct feedback is the most structured and easiest to analyze quantitatively, but it captures only the customers who respond, at the moments you choose to ask, about the topics you choose to cover. Response rates for most surveys are below 20%, and the customers who respond systematically differ from those who don’t.

Indirect feedback — Feedback customers provide without being directly asked: online reviews, social media mentions, community forums, app store ratings, and media coverage. Indirect feedback is unsolicited and therefore often more honest than direct feedback — customers are expressing opinions they chose to share rather than responding to your questions. It is also harder to analyze at scale and requires text analysis and sentiment tools to make meaningful.

Inferred feedback — Behavioral data that reveals customer experience quality without customers explicitly saying anything: product usage patterns, support contact rates, churn behavior, renewal rates, expansion purchasing, referral activity, and digital journey analytics. Inferred feedback is the most objective signal available — customers vote with their behavior more honestly than they do with survey responses — but it requires the most analytical sophistication to interpret and connect to specific experience drivers.

The most mature VoC programs integrate all three types, using each to validate and enrich the others. Direct feedback tells you what customers say. Indirect feedback tells you what they feel strongly enough to volunteer. Inferred feedback tells you what they actually do. Together they provide a much more complete picture than any single source alone.

VoC Collection Methods: Choosing the Right Approach

NPS surveys — The Net Promoter Score question (“How likely are you to recommend us?”) is the most widely used VoC instrument. Its strength is simplicity and benchmarkability — a single number that can be tracked over time and compared against industry benchmarks. Its limitation is that it measures a single dimension of the relationship at a single moment, and the score alone provides no guidance on what to improve.

CSAT surveys — Customer Satisfaction Score surveys measure satisfaction at specific touchpoints — typically after a service interaction, purchase, or onboarding event. CSAT is most useful for evaluating specific touchpoint performance over time and identifying where particular interactions are falling below acceptable thresholds.

CES surveys — Customer Effort Score measures how easy it is for customers to accomplish what they are trying to do. CES is particularly predictive of loyalty in service contexts — research by Gartner/CEB found that reducing customer effort is more strongly correlated with loyalty than delighting customers. A single CES question after support interactions (“How easy was it to resolve your issue today?”) often provides more actionable insight than a longer CSAT battery.

Customer interviews — Structured or semi-structured conversations with customers that go beyond survey scores to understand the reasoning, emotions, and context behind their experience. Interviews are the richest qualitative VoC method available — they surface insights that no quantitative instrument can capture. The limitation is scale: interviews are resource-intensive and typically reach a small sample.

Exit interviews — Conversations with customers who have churned or chosen not to renew. Exit interviews are the most underused and most valuable VoC instrument in most organizations — they provide direct access to the actual reasons customers left, unfiltered by the diplomatic moderation that shapes most feedback from current customers.

Support interaction analysis — Mining support tickets, chat logs, and call transcripts for patterns in what customers contact you about, how they describe their problems, and what emotions they express. Support contact patterns are a direct window into the experience failures driving the highest volume of customer effort.

Review and social listening — Monitoring what customers say about you on review platforms, social media, and community forums. Unsolicited public feedback is often the most honest signal available — customers expressing strong opinions they chose to share rather than responding to questions you designed.

Building a VoC Program That Drives Action

Step 1: Define what you need to learn before choosing how to collect
Define what you need to learn before choosing how to learn it. The most common VoC program design mistake is selecting collection methods based on what is easiest or most familiar rather than what will answer the specific questions that most need answering. Start with the business decisions your VoC program needs to inform — then design the collection approach that provides the evidence needed to make those decisions confidently.

Step 2: Map feedback to the customer journey
Rather than collecting feedback at operationally convenient moments (after every support ticket, at every anniversary), design your VoC program around the customer journey — collecting feedback at the moments that matter most for understanding loyalty and retention. This requires a journey map as the foundation for VoC design, ensuring that measurement is aligned with the experience touchpoints that drive the outcomes you care about.

Step 3: Integrate across sources
Build or adopt a central feedback integration infrastructure that brings direct, indirect, and inferred feedback together in a single view. VoC isn’t just relevant for customer support — share product feedback with the R&D team, marketing insights with the marketing team, and service issues with the support team to make the entire organization customer-centric. Siloed feedback produces siloed insight and siloed action.

Step 4: Analyze for patterns, not just scores
Move beyond reporting average scores to identifying patterns — the segments, touchpoints, journey stages, and time periods where the experience is systematically better or worse, and the specific experience factors most correlated with the loyalty outcomes you are trying to influence. This is where text analysis, journey analytics, and correlation modeling add genuine value beyond what score reporting provides.

Step 5: Close the loop with customers
Once you’ve made a change — whether it’s fixing a bug or introducing a requested feature — communicate it to your customers. Close the feedback loop and show that you’re listening. Customers who receive no response to feedback they provide stop providing it. Closing the loop — at both the individual level (responding to specific feedback) and the program level (communicating what you have changed based on what you heard) — is what builds the trust that makes VoC programs sustainable over time.

Step 6: Connect insights to improvement ownership
Every significant VoC insight should be connected to a specific owner responsible for acting on it, with a defined timeline and success metric. Insights without owners are ideas, not improvements. The measure of a VoC program’s effectiveness is not the quality of its reports — it is the rate at which its insights produce specific, measurable experience improvements.

VoC Program Maturity: Where Are You on the Curve?

A mature VoC program unifies feedback from every customer channel, applies AI to automate analysis, and connects insights directly to financial outcomes like revenue growth and retention. Evaluate your program across eight key dimensions: signals coverage, data quality and governance, time-to-insight, time-to-action, closed-loop coverage, AI/text/speech depth, operational integration, and financial linkage.

Most organizations are at an early to intermediate maturity level — collecting direct feedback from multiple channels but lacking the integration, analysis sophistication, and action infrastructure needed to translate that feedback into systematic experience improvement. The gap between early and mature VoC programs is not primarily a technology gap — it is an organizational capability gap: the ability to act on what is heard, consistently and at scale.

How a Customer Experience Audit Complements Your VoC Program

VoC programs tell you what customers are saying about their experience. A customer experience audit tells you what the experience actually is — including the dimensions that customers don’t say, because they don’t complain, because they don’t know how to articulate the friction, or because they have already left.

The two are complementary, not competitive. VoC provides continuous monitoring — a stream of customer feedback that tracks experience quality over time and signals emerging problems. An experience audit provides deep diagnosis — a systematic, evidence-based assessment of the full experience landscape that VoC programs typically cannot provide on their own.

The most important things an experience audit reveals are often the things customers don’t tell you: the friction they work around without complaint, the competitive experiences they compare you to unfavorably without mentioning it in your surveys, and the journey stage failures that drive churn six months later without ever generating a negative survey response.

Organizations that combine a well-designed VoC program with periodic experience audits have both the continuous monitoring needed to detect problems early and the deep diagnostic capability needed to understand and fix them before they compound into significant revenue impact.

Frequently Asked Questions About Voice of Customer

What is Voice of Customer (VoC)?

Voice of Customer (VoC) is the systematic process of capturing, analyzing, and acting on what customers say, feel, and expect about their experience with your organization — across every channel where feedback exists, solicited or not. An effective VoC program collects three types of feedback: direct feedback (surveys, interviews), indirect feedback (reviews, social media, community forums), and inferred feedback (behavioral data, usage patterns, churn behavior). The measure of a VoC program is not how much feedback it collects but how reliably it produces actionable insights that improve the customer experience and drive measurable business outcomes.

What are the most common Voice of Customer methods?

The most widely used VoC methods are NPS surveys (measuring likelihood to recommend), CSAT surveys (measuring satisfaction at specific touchpoints), CES surveys (measuring customer effort), customer interviews (qualitative conversations that surface context and reasoning), exit interviews (conversations with churned customers), support interaction analysis (mining tickets and transcripts for patterns), and review and social listening (monitoring unsolicited public feedback). Each method provides different signal — quantitative methods provide scale and benchmarkability, qualitative methods provide depth and context. The most effective VoC programs combine multiple methods rather than relying on any single source.

Why do Voice of Customer programs fail?

VoC programs most commonly fail for four reasons: collecting feedback but failing to act on it (the most prevalent failure); siloing feedback by department so no one sees the complete customer picture; measuring moments rather than journeys, missing the cumulative experience that drives loyalty; and confusing feedback volume with insight quality. The organizations that get the most value from VoC programs are those that treat closing the loop — acting on insights, communicating changes to customers, and measuring whether improvements worked — as the primary measure of program success, not the volume or scores of feedback collected.

What is the difference between NPS, CSAT, and CES?

NPS (Net Promoter Score) measures how likely customers are to recommend your organization on a 0–10 scale, producing a score from -100 to +100. It measures the overall relationship and is most useful for tracking loyalty trends over time. CSAT (Customer Satisfaction Score) measures satisfaction at specific touchpoints — typically after interactions — on a scale that is converted to a percentage of satisfied customers. It measures transactional quality and is most useful for evaluating specific touchpoint performance. CES (Customer Effort Score) measures how easy it is for customers to accomplish what they are trying to do, typically on a 1–7 scale. It is most predictive of loyalty in service contexts — Gartner research found that reducing customer effort is more strongly correlated with loyalty than delighting customers. All three are useful signals; none is sufficient alone.

How does a customer experience audit relate to a VoC program?

A VoC program and a customer experience audit are complementary, not competing tools. A VoC program provides continuous monitoring — a stream of customer feedback that tracks experience quality over time and signals emerging problems. A customer experience audit provides deep diagnosis — a systematic, evidence-based assessment of the full experience landscape, including the friction customers don’t report, the competitive gaps they don’t articulate, and the journey stage failures that drive churn without generating a negative survey response. Organizations that combine ongoing VoC monitoring with periodic experience audits have both the early warning system and the diagnostic capability needed to understand and fix experience failures before they compound into significant revenue impact.

Want to go beyond what customers say to understand what they actually experience? Learn more about the Experience Audit →

Content Authenticity Statement: The topic area, key elements to focus on, etc. were decisions made by Braden Kelley, with a little help from Claude and Google Gemini to clean up the article, add images and create infographics.

Image credits: Google Gemini

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Making Every Customer Feel Special

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Making Every Customer Feel Special

GUEST POST from Shep Hyken

This article answers the question: What is the difference between personalization and individualization, and why does it matter to the customer experience?

The concept of personalization is gaining increased attention. My annual customer experience research found that nearly eight out of 10 customers (79%) in the U.S. feel a personalized experience is important. So, what is a personalized experience?

It’s simple. Using a customer’s data and information (with their permission, of course), which could include preferences they’ve shared with you, past behaviors, purchasing patterns, notes from interactions they’ve had with you and more, allows you to tailor interactions, offers, and communications to the customer based on what you know about them.

It also allows you to group customers into segments. For example, if you sell shoes and a customer has bought three pairs of golf shoes in the past year, you wouldn’t recommend running shoes. However, you might inform the customer, and customers like him, about the latest golf shoe technology and suggest other golf-related products. This personalized experience results in customers feeling recognized and valued, rather than just being treated as a generic transaction.

Now, there’s a higher level of personalization, and that’s individualization. Personalization makes customers feel recognized. Individualization makes them feel truly understood. This next level of personalization comes from the amount of data that can be collected from an individual customer, combined with AI’s ability to interpret that data with uncanny accuracy. The best way to describe the difference is that it’s no longer about customer segmentation. It’s about providing truly individualized experiences tailored to each customer.

Why is this important to the customer experience? If you thought personalization made a customer feel recognized and valued, this is that on steroids.

Old-fashioned individualization before AI was the amazing salesperson who always recognized you, remembered what you bought, knew what you liked, could predict what you’d want to buy and might even call you to let you know that your favorite brand had something new that you’d love.

Modern individualization is when you log into Amazon and the website welcomes you, not just promoting the brand of toothpaste you’ve bought in the past, but also reminding you that you may be running low on toothpaste.

And even though AI is making individualization easier, you don’t need expensive AI software to do this. You can start by paying attention. One of my clients is a master at sending out birthday cards with hand-written, individualized messages. And when you call him, he remembers details about you. It’s not magic or AI software. It’s just asking questions, listening to the answers and taking notes so he remembers the details the next time he talks to the client.

The goal is to make every customer feel like they are your only customer. Whether you’re using AI or just old-fashioned attention to detail, the result is the same. Done the right way, customers feel valued and appreciated and respond by saying, “I’ll be back!”

Image Credit: Pixabay, Shep Hyken

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Customer Journey Mapping

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A Complete Guide to Building Maps That Drive Decisions

Customer Journey Mapping

by Braden Kelley and Art Inteligencia

Customer journey mapping is one of the most powerful tools available to experience leaders — and one of the most frequently misused. Organizations create journey maps in workshops, hang them on walls, and then make the same experience investment decisions they would have made anyway. The map becomes a deliverable rather than a diagnostic, a picture of the experience rather than a catalyst for improving it.

Done well, customer journey mapping is the foundation of every significant customer experience improvement. It creates the shared organizational understanding of what customers actually experience — not what internal teams assume they experience — and translates that understanding into a prioritized roadmap of improvements with measurable revenue and retention implications.

The customer journey analytics market is valued at USD 17.91 billion in 2025 and is projected to reach USD 47.06 billion by 2032, growing at a CAGR of 14.8%. And 47% of businesses now use customer journey maps to identify and improve touchpoints — up sharply from a decade ago when this was niche UX work. The organizations investing in this capability are pulling ahead. This guide explains how to do it in a way that actually drives decisions.

What is Customer Journey Mapping?

Customer journey mapping is the process of creating a visual representation of every step, interaction, emotion, and decision a customer makes across their entire relationship with an organization — from first awareness through purchase, use, service, renewal, and advocacy.

A good journey map doesn’t just describe the customer journey — it guides it. It helps teams decide what to fix next, and why it matters. It integrates data, direct observation, and customer research to surface the gap between the experience you believe you are delivering and the experience customers are actually having.

Journey mapping is distinct from process mapping. A process map describes what your organization does. A journey map describes what the customer experiences — including the emotions, expectations, and frustrations that process maps systematically exclude. This distinction is why journey maps surface insights that internal process reviews consistently miss.

Why Customer Journey Mapping Matters

The business case for journey mapping is grounded in a simple reality: 52% of customers will switch to a competitor after a single negative interaction. Organizations that don’t systematically understand where their experience is falling short are making decisions about experience investment without the information needed to make them well.

Journey mapping delivers four specific organizational benefits:

Cross-functional alignment — Journey maps create a shared understanding of the customer experience across marketing, sales, support, and product teams. This shared understanding is a prerequisite for the cross-functional collaboration that experience improvement requires — you cannot fix a broken onboarding experience if product, marketing, and customer success are all looking at different parts of it.

Prioritized investment decisions — Maps highlight where to invest resources for the greatest return on customer experience improvements. Without a journey map, experience investment decisions are driven by whoever advocates most loudly, whatever the most recent customer complaint was, or whatever the current quarter’s metric is underperforming.

Proactive churn prevention — By identifying friction points before they cause churn, you can proactively address issues that drive customers away. Most churn is visible in the journey map long before it shows up in retention metrics.

Data-driven decisions — Journey maps replace intuition and assumption with evidence — creating an organizational baseline of what the experience actually is, against which investments can be evaluated and progress can be measured.

The Five Stages of the Customer Journey

While every organization’s customer journey has unique characteristics, most follow a common structural framework. A common model defines the key stages as: Awareness, Consideration, Purchase, Service, and Loyalty. Understanding what happens at each stage — and what can go wrong — is the foundation of effective journey mapping.

Stage 1: Awareness
The customer first discovers your organization exists. This may happen through search, social media, word of mouth, advertising, or a direct referral. The experience at awareness sets the first impression — the expectations that every subsequent touchpoint will be measured against. Common failure modes: unclear value proposition, inconsistent brand messaging across channels, poor search visibility for the queries that signal buying intent.

Stage 2: Consideration
The customer evaluates your organization against alternatives. They read reviews, compare features, visit your website, and may request a demo or trial. The experience at consideration determines whether interest converts to intent. Common failure modes: friction in the evaluation process (hard-to-find information, complex trial setups, slow response to inquiries), lack of social proof, and messaging that doesn’t address the specific concerns driving the evaluation.

Stage 3: Purchase
The customer makes the buying decision and completes the transaction. The experience at purchase either reinforces the confidence that drove the decision or introduces the first seeds of doubt. Common failure modes: complex purchase processes, unexpected fees or complications, hand-off failures between sales and implementation teams, and onboarding experiences that immediately disappoint the expectations set during the sales process.

Stage 4: Service and Use
The customer uses your product or service and encounters your support and service processes when needed. This is the longest stage of the journey and the one that most determines whether loyalty is built or eroded. Common failure modes: poor onboarding that prevents value realization, difficult-to-use products that generate avoidable service contacts, service interactions that resolve problems adequately but fail to rebuild confidence, and lack of proactive communication at high-risk moments.

Stage 5: Loyalty and Advocacy
The customer becomes a repeat buyer, expands their relationship, and ideally becomes an active advocate — recommending you to others. The experience at this stage determines whether customers are loyal because they genuinely prefer you or retained because switching is inconvenient. Common failure modes: transactional renewal conversations that don’t reinforce the relationship value, failure to recognize and reward loyal customers, and insufficient advocacy programs that leave willing promoters with no channel to express their support.

The Core Components of a Customer Journey Map

A complete customer journey map captures both the functional and emotional dimensions of the customer experience. Core elements to include are: Personas (general groups of customers based on demographics and psychographics), Actions (what the customer does at each touchpoint), and Timeline (the process of going through the touchpoints and phases of the journey). A fully developed map also includes:

Customer goals and expectations — What is the customer trying to accomplish at each stage? What do they expect from the experience? Understanding goals and expectations is what separates a journey map from a touchpoint list — it provides the context needed to evaluate whether the experience is actually serving the customer’s purpose.

Emotional journey — How does the customer feel at each touchpoint? Where is confidence building or eroding? A journey map without the emotion and pain-point layer is just a flowchart. Emotions are what connect functional experience data to loyalty outcomes — they are the mechanism through which experience quality translates into retention and advocacy.

Pain points and friction — Where is the experience creating unnecessary effort, confusion, or frustration? Pain points are the specific, actionable findings that make a journey map investable rather than decorative.

Moments of truth — The high-stakes touchpoints where the quality of the experience has a disproportionate impact on loyalty — typically first use, first service incident, and renewal. Moments of truth deserve particular attention in journey mapping because they are where trust is built or broken most rapidly.

Opportunity areas — Where are the specific improvements that would have the greatest impact on customer loyalty and revenue? These are the findings that translate a journey map into a business investment case.

Current-State vs Future-State Journey Mapping

A current-state journey shows how customers experience your brand right now — capturing real behavior, real friction, and real gaps between expectations and delivery. This creates a shared baseline where teams can see where customers hesitate, where effort piles up, and where trust is quietly lost.

A future-state journey map describes the experience you are designing toward — the ideal customer journey that addresses the pain points and gaps identified in the current state. Future-state mapping is where journey mapping connects to organizational strategy: it defines the experience standard you are building toward and provides a framework for evaluating whether specific improvements are moving you toward it.

The most effective journey mapping programs maintain both: using current-state maps to identify where to invest, and future-state maps to define what you are building toward. Customer journeys evolve constantly — which means journey maps must be treated as living documents rather than one-time deliverables.

How to Build a Customer Journey Map: A Practical Process

Step 1: Define scope and purpose
Before mapping, define which customer segment you are mapping, which stage of the journey you are focusing on (or whether you are mapping the full end-to-end journey), and what specific business question the map is designed to answer. Start with a clear purpose and scope — define which customer segment, journey stages, and key touchpoints you want to map. This focus creates a journey map that is specific and meaningful.

Step 2: Build evidence-based personas
Effective journey mapping requires genuine understanding of the customers being mapped — not internal assumptions about what customers want, but research-grounded profiles of who they actually are, what they are trying to accomplish, and what they experience today. Gathering data from customer feedback, behavioral data, demographic and persona details, and operational metrics ensures the personas reflect reality rather than organizational wishful thinking.

Step 3: Map the current-state journey
Document every touchpoint in the customer journey from the customer’s perspective — not the organization’s process map, but the sequence of interactions and experiences the customer actually encounters. For each touchpoint, capture what the customer is doing, what they are thinking and feeling, and where friction, confusion, or disappointment is occurring.

Step 4: Validate with real customers
The most common and most consequential journey mapping failure is building maps entirely from internal knowledge — documenting what employees believe customers experience rather than what customers actually experience. If you are still building journey maps from internal whiteboards and a few CSAT scores, you are mapping what your team thinks the customer feels — not what they actually feel. Direct customer research — interviews, observation, and journey walking — is essential for maps that produce genuine insight.

Step 5: Identify pain points and moments of truth
With the current-state journey documented and validated, identify the specific touchpoints where the experience is falling below customer expectations, creating unnecessary friction, or failing at high-stakes moments. Prioritize by frequency (how many customers encounter this pain point), severity (how significantly it affects loyalty and retention), and fixability (how much organizational effort and investment is required to address it).

Step 6: Translate insights into investment priorities
Identify the most important takeaways from your journey map, such as major pain points, customer expectations, or opportunities for delight. Translate these insights into concrete action items by assigning ownership to specific team members or departments. A journey map that doesn’t produce specific, owned actions with defined timelines is a decorative document, not a management tool.

Step 7: Build the future-state map
Define the experience you are designing toward — the journey that addresses the identified pain points, meets customer expectations at moments of truth, and delivers the consistency and emotional quality that builds genuine loyalty. Use the future-state map to evaluate proposed improvements against the standard you are working toward.

Common Journey Mapping Mistakes to Avoid

Mapping from the inside out — Building journey maps from internal process knowledge rather than customer research produces maps that describe what the organization does, not what customers experience. The gap between these two views is where the most valuable insights live.

Ignoring the emotional layer — Functional interactions matter, but emotions drive decisions. Include sentiment analysis at every touchpoint. A map that captures what customers do without capturing how they feel is missing the dimension that connects experience quality to loyalty outcomes.

Creating static maps — Customer journeys evolve constantly. A journey map created once and never updated quickly becomes a historical document rather than a current management tool. Build a process for regular review and update.

Mapping without clear ownership — Journey maps that are shared as organizational artifacts without specific improvement ownership consistently fail to produce action. Every pain point identified in the map should have an owner and a timeline.

Optimizing components in isolation — Improving individual touchpoints without considering their role in the full journey can produce local improvements that don’t translate to loyalty gains. Journey mapping is most valuable when it maintains the full customer perspective — evaluating each touchpoint in the context of the overall experience it contributes to.

Journey Mapping and the Experience Audit

A customer experience audit takes journey mapping to its fullest expression — combining the visual mapping of the customer journey with direct experience walking, competitive benchmarking, and quantitative data analysis to produce a complete, validated picture of where the experience is strong and where it is failing.

Where an internal journey mapping exercise is limited by organizational knowledge and assumptions, an experience audit brings external perspective — walking the journey with genuinely fresh eyes, comparing it against competitive alternatives, and applying practitioner experience from across industries to identify gaps that internal teams cannot see.

The result is a journey map that is not just accurate but prioritized by revenue impact — giving leaders a clear, actionable roadmap for experience investment that is grounded in competitive reality rather than internal benchmarks alone.

Frequently Asked Questions About Customer Journey Mapping

What is customer journey mapping?

Customer journey mapping is the process of creating a visual representation of every step, interaction, emotion, and decision a customer makes across their entire relationship with an organization — from first awareness through purchase, use, service, renewal, and advocacy. A journey map captures both the functional dimensions (what customers do) and the emotional dimensions (how customers feel) at each touchpoint, and uses this complete picture to identify where the experience is creating friction, falling below expectations, or missing opportunities to build loyalty. Done well, a customer journey map is a prioritized investment roadmap, not a decorative artifact.

What are the stages of the customer journey?

The most widely used customer journey framework defines five stages: Awareness (first discovery of the organization), Consideration (evaluation against alternatives), Purchase (the buying decision and transaction), Service and Use (ongoing use of the product or service and service interactions), and Loyalty and Advocacy (repeat purchasing, relationship expansion, and recommendation). Each stage has distinct customer goals, expectations, and common failure modes. A complete journey map examines all five stages and identifies the specific touchpoints within each where the experience is strengthening or undermining customer loyalty.

What is the difference between a customer journey map and a process map?

A process map describes what an organization does — the sequence of internal activities and handoffs that deliver a product or service. A customer journey map describes what the customer experiences — the sequence of interactions, emotions, and decisions the customer encounters from their perspective. The gap between these two views is often significant and revealing: process maps consistently omit the friction, confusion, and emotional reactions that determine whether customers are loyal or churning. Journey maps are most valuable precisely because they surface what process maps systematically miss.

How do you create a customer journey map?

Creating an effective customer journey map involves seven steps: define the scope and purpose of the map; build evidence-based customer personas from research rather than assumptions; map the current-state journey from the customer’s perspective; validate the map with direct customer research — interviews, observation, and journey walking; identify pain points and moments of truth prioritized by their impact on loyalty and revenue; translate insights into specific, owned improvement actions; and build a future-state map defining the experience you are designing toward. The most common mistake is building maps from internal knowledge alone — journey maps that aren’t validated against real customer research describe what organizations think happens, not what customers actually experience.

What is a moment of truth in customer journey mapping?

A moment of truth is a high-stakes touchpoint in the customer journey where the quality of the experience has a disproportionate impact on customer trust and loyalty. Common moments of truth include first product use (does it deliver on the sales promise?), first service incident (how does the organization respond when something goes wrong?), and renewal conversations (does the organization treat me as a valued customer or a transaction?). Moments of truth deserve particular attention in journey mapping because they are where trust is built or broken most rapidly — and where experience investment generates the greatest loyalty return.

How is customer journey mapping related to a customer experience audit?

Customer journey mapping is the foundation of a customer experience audit — but an experience audit takes mapping further by adding direct experience walking, competitive benchmarking, and quantitative data analysis to produce a complete, externally validated picture of where the experience is strong and where it is failing. An internal journey mapping exercise is limited by organizational knowledge and assumptions. An experience audit brings external perspective — walking the journey with fresh eyes, comparing it against competitive alternatives, and quantifying the revenue impact of identified gaps. The result is a journey map that is not just accurate but prioritized by competitive and financial impact.

Want a complete, validated map of your customer journey — with competitive benchmarks and prioritized improvement opportunities? Learn more about the Experience Audit →

Content Authenticity Statement: The topic area, key elements to focus on, etc. were decisions made by Braden Kelley, with a little help from Claude and Google Gemini to clean up the article, add images and create infographics.

Image credits: Google Gemini

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The Coming Tribal Fragmentation

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Another AI Soft Landing Scenario Exploration — City-States and the Patchwork Polity

LAST UPDATED: May 31, 2026 at 5:32 PM

The Coming Tribal Fragmentation - Patchwork Policy

by Braden Kelley and Art Inteligencia

When historians look back at the twilight of the Western Roman Empire, they don’t point to a single afternoon when the lights went out. Instead, they chart a long, uneven devolution. As the administrative center in Rome grew too slow, too rigid, and too broke to manage its sprawling frontiers, the legions pulled back. The roads decayed, centralized currency lost its teeth, and power withdrew into localized fiefdoms, fortified towns, and monastic communities.

A similar fracturing is quietly underway today, but the invading force isn’t the Visigoths — it is the sheer velocity of exponential technology.

For years, debates around an “AI Soft Landing” have operated under a flawed assumption: that the transition will be managed by a singular, top-down policy framework. We wait for a grand federal directive — a national UBI, a sweeping regulatory bill, a unified retraining initiative. But the federal apparatus is simply not built for this speed. While centralized governments paralyze themselves with partisan gridlock and bureaucratic inertia, the economic and social fabric of the country is mutating in real-time. To understand the full context of this journey, you can explore the previous hypotheses here:

The center cannot hold, and it won’t. But its failure to manage a uniform landing does not mean a catastrophic crash is inevitable. Instead, we are entering an era of political speciation — a tribal fragmentation highly reminiscent of the Italian peninsula in the 15th century.

When the overarching authority of the Holy Roman Empire and the Papacy fractured, Italy did not slide into a dark age. Instead, it gave rise to a brilliant, chaotic patchwork of city-states. Venice became a maritime commercial powerhouse; Florence established itself as a financial capital fueled by a humanistic cultural Renaissance; Milan thrived on military manufacturing. Each city-state constructed a radically different social contract, economy, and political structure to survive a shifting world.

We are on the cusp of the Patchwork Polity. As the nation-state loses its capacity to cushion the disruptions of machine intelligence, power is devolving to cities, regional compacts, and ideological enclaves. The future of the AI Soft Landing will not be a single blanket rolled out from Washington D.C., but a mosaic of localized experiments. Americans, and global citizens at large, are about to sort themselves into communities organized around their preferred relationship with AI, capital, and human labor.

Welcome to the new map.

The Drivers of Speciation: Why the Center Cannot Hold

Biologists use the term speciation to describe the process by which a single evolutionary lineage splits into distinct, isolated species due to environmental pressures. In the context of the AI transition, social and political speciation is driven by a stark reality: exponential technology has completely decoupled from the linear pace of centralized governance.

This geographic and cultural fracturing is accelerated by three primary systemic forces:

1. Regulatory Paralyzation

While Washington debates committees, definitions, and jurisdictional boundaries, AI capabilities double every few months. This structural inertia creates a massive governance vacuum. Because a uniform, federal “cushion” isn’t coming in time, local municipal leaders, governors, and regional coalitions are forced to invent their own survival strategies to handle local labor market displacement.

2. Infrastructure and Capital Decoupling

AI is not distributed equally. It requires immense physical infrastructure: hyper-scale data centers, robust electrical grids, and close proximity to top-tier technical talent. Regions anchored by tech corridors naturally pull away from rural or legacy-industrial areas. This economic divergence creates distinct localized biomes, making a one-size-fits-all economic policy functionally impossible.

3. The Ideological Sorting Effect

The cultural divide over AI is profound. Some view automation as ultimate liberation from toil; others see it as an existential threat to human meaning, dignity, and livelihood. As these views harden, citizens will increasingly migrate — physically and digitally — toward communities that reflect their core values. We will see people vote with their feet, actively choosing social contracts based on how those regions balance or restrict machine labor.

“Just as the breakdown of Roman infrastructure forced medieval populations to cluster around local lords or fortified monasteries for safety, the legislative paralysis of the federal government forces modern communities to cluster around localized economic models for survival.”

When the macro-environment becomes too volatile and the centralized state fails to provide security, safety becomes a local initiative. The result is the fragmentation of a uniform society into distinct, localized ideological tribes.

Mapping the Patchwork Polity: Archetypes of the New Commons

As centralized frameworks dissolve, the political landscape reshapes itself into distinct, specialized ecosystems. If we were to map this new world, we wouldn’t see traditional red and blue states, but rather a complex mosaic of ideological and economic models. Three primary archetypes will dominate this fragmented future, each representing a fundamentally different social contract with machine intelligence.

1. The AI New Deal City-States (The Tech-Communes)

The Vibe: Ultra-modern, highly automated, post-labor optimization.

Centered around existing technology hubs and deep-pocketed metropolitan corridors, these city-states lean entirely into the curve of automation. Rather than fighting algorithmic efficiency, they aggressively tax the productivity gains of hyper-scale AI systems, autonomous infrastructure, and robotic labor to fund a robust local safety net.

In these enclaves, traditional human work is optional. Citizens receive a combination of Universal Basic Income and Universal Basic Services — including free municipal transit, automated healthcare, and civic housing. The social contract is simple: surrender the concept of labor-driven identity in exchange for machine-provided abundance and abundant leisure.

2. The Human-Premium Renaissance Zones (The Neo-Guilds)

The Vibe: Florence in the 1400s — high culture, premium handmade goods, human-to-human connection.

Standing in stark ideological opposition to the tech-communes are the Human-Premium Renaissance zones. These regions — often wealthy cultural capitals, university towns, or scenic coastal enclaves — intentionally legislate machine intelligence out of core human experiences. They enact strict “Human-Premium” labeling laws and certification metrics, ensuring that fields like education, therapy, law, artisanal manufacturing, and hospitality remain strictly the domain of flesh and blood.

Like the craft guilds of medieval Europe, these zones protect human mastery. While living here is highly expensive due to the lack of automated efficiency, the economy thrives on a premium marketplace where wealthy outsiders pay a massive surplus for the luxury of authentic, unfiltered human interaction and craftsmanship.

3. The Neo-Victorian Hierarchies (The Corporate Enclaves)

The Vibe: Strict stratification, private governance, efficiency above equity.

Where public local governments fail entirely to manage displacement, massive technology conglomerates and private equity cartels step in to fill the void. These are privatized corporate enclaves — gated geographic zones entirely owned, policed, and optimized by proprietary AI networks.

For the non-elite citizens living within these borders, the social contract mirrors nineteenth-century company towns. Individuals trade their behavioral data, sovereign privacy, and continuous gig-labor in exchange for access to privately managed infrastructure, drone-enforced security, and basic corporate-subsidized sustenance. Wealth is strictly bifurcated between the algorithmic asset owners and the vast underclass of human edge-case handlers who keep the machines fed.

“Just as fifteenth-century Venice, Florence, and Milan developed entirely incompatible political structures to navigate the shifts of their era, these three modern archetypes will create wildly divergent definitions of what it means to live a successful human life.”

The Dynamics of the Patchwork: How They Coexist and Clash

A map fractured into radical ideological experiments cannot remain static. Just as the Italian city-states were locked in a perpetual dance of shifting alliances, economic espionage, and low-grade warfare, the archetypes of the Patchwork Polity do not exist in isolation. They are deeply codependent, inherently suspicious of one another, and constantly forced to navigate the friction of their incompatible social structures.

This macro-relationship is defined by three main geopolitical and economic pressure points:

1. Data Tariffs and Algorithmic Friction

Trade between these zones looks nothing like traditional commerce. When a Human-Premium Zone trades with an AI New Deal City-State, the friction is cultural and technical. The Neo-Guilds protect their local markets by slapping massive “compute tariffs” on imported goods or services generated by automated systems. Conversely, the Tech-Communes demand unfettered access to behavioral data streams from anyone wishing to plug into their hyper-efficient logistics networks. Economic warfare is no longer fought over physical borders, but over data privacy boundaries and algorithmic access.

2. The Border Paradox and Refugee Flows

Borders in the Patchwork Polity are strictly monitored, yet highly porous to specific human talent. We are witnessing a unique, modern brain drain:

  • Artists, educators, and artisans flee the hyper-automated Tech-Communes, seeking asylum and high wages in the Human-Premium Renaissance zones where their humanity is valued as an economic asset.
  • Displaced gig-workers and data-serfs trapped in the Neo-Victorian Hierarchies risk everything to cross into AI New Deal territories, searching for the safety net of a machine-funded basic income.

Managing these highly specialized refugee flows requires a complex web of immigration protocols, digital identity tracking, and ideological vetting.

3. The Condottieri of the Digital Age

In Renaissance Italy, city-states relied on condottieri — highly professional, mercenary military captains who sold their strategic skills to the highest bidder. In the Patchwork Polity, we see the rise of the digital condottieri: elite squads of prompt engineers, cybersecurity syndicates, data scientists, and systems architects.

These highly mobile cognitive specialists hold no allegiance to any single ideology or municipality. They sell their optimization services to the highest bidding corporate enclaves, build the automated defensive networks for the tech-communes, or help human-premium zones develop sophisticated firewalls to keep out illicit, unverified AI tools. They are the true fluid elite of a fragmented world.

“Peace in this fragmented landscape is never permanent; it is a dynamic equilibrium maintained by mutual economic dependence and a mutual recognition that no single zone can entirely destroy the others without destroying the supply chains that keep itself alive.”

This isn’t a story of a world completely breaking down — it is a story of a world breaking apart into hyper-focused specialized zones. The true test of the patchwork landing is not whether these regions can learn to love each other, but whether their structural codependency can prevent localized friction from escalating into systemic collapse.

Conclusion: Embracing the Mosaic

When the Western Roman Empire dissolved into a fragmented tapestry of localized rule, it felt to those living through it like the end of civilization. But viewed through the long lens of history, it was simply the messy, chaotic birth of a new political and economic landscape. The decay of centralized authority gave way to localized experiments that eventually birthed the modern world.

We must apply that same historical perspective to the AI transition. The dream of a uniform, centrally managed “AI Soft Landing” orchestrated by federal policy is dead. The sheer velocity of machine intelligence has outrun the slow, linear machinery of national governance. But as the macro-structure fractures, we are discovering that the absence of a singular nationwide cushion does not guarantee a nationwide crash.

Instead, the landing is happening in pieces. It is a mosaic of micro-landings, some softer and more elegant than others. The future belongs to the agile, the local, and the community-driven. Survival in this new era requires a profound shift in mindset: we must stop waiting for a grand national compromise that will never come, and instead start focusing on the local social contracts we can actively shape.

The Core Truth of the Patchwork Polity:

You can no longer choose whether or not the AI revolution happens. But as the nation-state devalues and power devolves, you will increasingly get to choose your tribe. You will choose whether you want to live in a world of machine-funded leisure, human-centric craftsmanship, or hyper-efficient corporate optimization.

The political map of the mid-twenty-first century is being redrawn before our eyes, shifting away from massive, contiguous geopolitical blocs and toward a vibrant, volatile, and highly competitive patchwork. It will be chaotic, it will be unequal, and it will require unprecedented levels of regional agility. But it will also be a period of immense social creativity.

The centralized state is giving way to the mosaic. It is time to find your place on the map — or start building the community that can chart its own way down.

Frequently Asked Questions

Q: Will the federal government have any role left in a fragmented “Patchwork Polity”?

A: Yes, but its role will shrink to structural baseline management. The federal government will likely focus on basic national defense, broad interstate commerce guardrails, and managing the fundamental infrastructure layers (like the national power grid). Direct economic cushions, labor laws, and social contracts will be almost entirely driven by local city-states and regional compacts.

Q: How can a Human-Premium Renaissance zone survive economically against hyper-efficient AI cities?

A: By treating scarcity as a luxury asset. Just as fine art, handmade mechanical watches, and live musical performances command immense price premiums today, these zones thrive on the deliberate lack of automation. They export highly valued human-certified expertise and luxury goods, pulling in massive capital from wealthy citizens in automated zones who are starved for authentic human connection.

Q: What is the biggest risk of this geographic and political sorting?

A: Extreme friction and inequality. If people sort themselves strictly by their philosophical and economic relationship with AI, we risk creating regions that cannot communicate or trade smoothly with one another. This deepens the “Cognitive Divide,” making economic and physical mobility incredibly difficult for citizens trying to move between incompatible regional ecosystems.


EDITOR’S NOTE: This is a visualization of but one possible future. I will be publishing other possible futures as they crystallize in my mind (or as you suggest them for me to explore).

Image credits: Google Gemini

Content Authenticity Statement: The topic area, key elements to focus on, etc. were decisions made by Braden Kelley, with a little help from Google Gemini to clean up the article, add images and create infographics.

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Customer Service vs Customer Experience

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What’s the Difference and Why It Matters

Customer Service vs Customer Experience

by Braden Kelley and Art Inteligencia

Customer service and customer experience are used interchangeably in most organizations. They are not the same thing — and the confusion between them is costing organizations significant competitive ground.

When leaders conflate customer service with customer experience, they make a predictable set of investment mistakes: they pour resources into contact center optimization while ignoring the upstream experience failures that are generating the contacts; they measure satisfaction at service touchpoints while missing the cumulative journey experience that determines loyalty; and they try to compensate for poor product, onboarding, and process experiences with better service recovery — an expensive and ultimately losing strategy.

Understanding the difference between customer service and customer experience is not semantic. It determines where you look for problems, where you invest for improvement, and how you measure whether you are winning or losing on the dimension that drives customer retention and revenue growth.

What is Customer Service?

Customer service is the direct assistance and support an organization provides to customers before, during, and after a purchase — the interactions where customers seek help, ask questions, resolve problems, or make requests. It is reactive by nature: a customer has a need or a problem, and customer service responds to it.

Customer service touchpoints include:

  • Support calls and chat interactions
  • Technical help desk and troubleshooting
  • Billing inquiries and disputes
  • Returns and complaints handling
  • In-store associate interactions
  • Onboarding assistance and training
  • Account management touchpoints

Customer service is critically important — 99% of consumers say customer service influences their buying decisions, with 74% rating it “very important or essential.” But it is one component of the total customer experience, not a synonym for it.

What is Customer Experience?

Customer experience (CX) is the sum total of every interaction, perception, and emotion a customer has with an organization across the entire relationship — from first awareness through purchase, use, service, renewal, and advocacy. It is the holistic impression customers carry of your organization, shaped by every touchpoint they encounter, whether those touchpoints involve a human being or not.

Customer experience encompasses:

  • How easy it is to discover and evaluate your product or service
  • How smooth and confidence-building the purchase process is
  • How effective onboarding is at helping customers achieve value quickly
  • How intuitive and reliable the product or service is in daily use
  • How well the brand communicates proactively — not just when something goes wrong
  • How effectively customer service handles the moments when problems arise
  • How renewal and expansion conversations feel — transactional or relational
  • The cumulative emotional impression that determines whether a customer recommends you

Customer service is a component of customer experience — a critically important one, but only one. 81% say customer service is the #1 decision factor, ahead of brand image and ethical commitments — but that figure reflects how much service recovery matters when things go wrong, not that service alone constitutes the full experience.

The Key Differences: Customer Service vs Customer Experience

Customer Service Customer Experience
Scope Specific touchpoints where customers seek help The entire relationship across all touchpoints
Nature Primarily reactive — responding to customer needs Proactive and reactive — designing the full journey
Ownership Customer service / support team Entire organization — every function contributes
Measurement CSAT, FCR, handle time, resolution rate NPS, CLV, churn rate, share of wallet, advocacy
When it matters When something goes wrong or a customer needs help At every moment of the relationship
Investment focus People, training, tools, processes for support Journey design, product, onboarding, culture, service
Goal Resolve issues efficiently and satisfactorily Build lasting loyalty and advocacy

Why the Distinction Matters in Practice

Mistake 1: Investing in service to compensate for experience failures

The most expensive and common mistake organizations make is treating customer service as the primary lever for improving customer satisfaction — throwing more people, better training, and faster response times at problems that are being caused upstream by poor product design, broken onboarding, or friction-laden processes.

56% of customers leave quietly without filing a complaint — meaning the majority of customers who have poor experiences never reach your service team at all. They simply leave. A world-class service organization cannot retain customers whose experience has already failed them at touchpoints service never sees.

The organizations that achieve the lowest service volumes are not those with the best service teams — they are those with the best-designed experiences. When the product works reliably, onboarding is effective, and processes are frictionless, the service team handles exceptions rather than managing a continuous flow of avoidable contacts.

Mistake 2: Measuring service satisfaction as a proxy for experience quality

CSAT scores at service touchpoints measure how well a specific interaction was handled. They do not measure whether the customer’s overall experience is building loyalty, driving advocacy, or protecting revenue. A customer can give a service interaction a 5-star rating and still churn — because the experience that led them to need service was frustrating, because the product isn’t delivering the value they expected, or because a competitor’s experience simply requires less effort overall.

Companies that prioritize customer experience generate 4–8% higher revenue than competitors — not companies with the best service scores. The financial return is in the total experience, not the service component alone.

Mistake 3: Assigning experience ownership to the service team

Customer experience is everyone’s responsibility — product, marketing, sales, operations, technology, and service all contribute to it. When experience ownership is assigned to the customer service team, two things happen: the service team gets blamed for experience failures they didn’t cause and can’t fix, and the functions that actually cause those failures have no accountability for them.

Excellent customer experience requires cross-functional alignment around the customer journey — a shared understanding of where the experience is strong and weak, and shared accountability for improving it. This cannot be owned by a single team.

How Customer Service and Customer Experience Work Together

The relationship between customer service and customer experience is not competitive — it is hierarchical. Customer experience is the broader strategic objective; customer service is one of its most important execution components.

When customer experience is designed well, customer service operates in a context that supports excellent outcomes:

  • Fewer contacts because the experience is designed to prevent avoidable problems
  • More context because the service team has visibility into the customer’s full journey (via Customer Journey Mapping), not just the current interaction
  • Higher recovery rates because a strong positive experience baseline means a single service failure is easier to recover from
  • Greater loyalty impact because excellent service within an already-excellent experience reinforces commitment rather than merely repairing damage

Over 85% of customers say they’re more loyal to a company if customer service is consistently improved, and 87% say they’re more loyal with fast, effective customer service. These numbers represent the ceiling of what excellent service can contribute to loyalty — and they are only achievable when service operates within a well-designed overall experience, not in isolation from it.

The Role of Each in a Complete Customer Strategy

Customer service strategy should focus on: speed and accessibility of support across channels; first contact resolution rates and escalation reduction; agent empowerment to resolve issues without unnecessary process friction; proactive outreach at high-risk moments in the customer journey; and service recovery processes that go beyond adequate resolution to genuine relationship repair.

Customer experience strategy should focus on: mapping and designing the full customer journey across all touchpoints; identifying and closing the experience gaps that generate avoidable contacts, drive churn, and suppress loyalty; aligning all functions around shared experience standards and accountability; building the measurement infrastructure to track experience quality continuously; and investing in the specific moments of truth that have the greatest impact on customer loyalty and revenue.

The two strategies are most powerful when they are integrated — when the experience strategy defines the journey that the service strategy supports, and when service insights inform the experience improvements that reduce contact volume and improve overall satisfaction.

How an Experience Audit Addresses Both

A customer experience audit examines both dimensions — evaluating the full customer journey to identify the experience failures generating service contacts and driving churn, while also assessing how well service touchpoints are performing within the broader journey context.

This dual lens is what distinguishes an experience audit from a service quality review. A service review evaluates how well the service team is performing. An experience audit evaluates whether the experience your customers have with your organization — including but not limited to service — is competitive, loyalty-building, and revenue-protecting.

The result is a complete picture of where the experience is falling short of competitive standards, prioritized by revenue impact — giving leaders the insight they need to invest in the right improvements rather than optimizing one component of the experience while missing the failures that matter most.

Frequently Asked Questions: Customer Service vs Customer Experience

What is the difference between customer service and customer experience?

Customer service is the direct assistance and support an organization provides to customers at specific moments — typically when customers seek help, ask questions, or resolve problems. It is reactive and owned by a specific team. Customer experience is the sum total of every interaction, perception, and emotion a customer has with an organization across the entire relationship — from first awareness through purchase, use, service, renewal, and advocacy. Customer service is one component of customer experience. Investing in excellent customer service while neglecting the broader experience is one of the most common and expensive mistakes in customer strategy.

Is customer service part of customer experience?

Yes — customer service is one component of customer experience, but not a synonym for it. Customer experience encompasses every touchpoint a customer has with an organization, including product and service quality, onboarding, digital and physical channel interactions, communications, billing, renewal conversations, and service recovery. Customer service specifically refers to the assisted support interactions where customers seek help or resolution. Excellent customer service contributes significantly to overall customer experience quality, but a strong service team cannot compensate for experience failures in other parts of the journey.

Which is more important — customer service or customer experience?

Customer experience is the broader strategic objective of which customer service is a critical component — so the question is less about which is more important and more about understanding that they operate at different levels. That said, organizations that invest in improving the overall customer experience — not just the service component — consistently generate greater financial returns. Companies that prioritize customer experience generate 4–8% higher revenue than competitors. The organizations that achieve the best results treat customer service excellence and customer experience design as complementary investments, not competing priorities, and competitive experience benchmarking can help you measure your performance.

Who owns customer experience in an organization?

Customer experience should be owned by the entire organization — every function that touches the customer journey contributes to it. In practice, accountability is often assigned to a Chief Customer Officer, Chief Experience Officer, or Chief Marketing Officer, with cross-functional governance to ensure that product, operations, technology, and service teams are all aligned around shared experience standards. Assigning experience ownership exclusively to the customer service team is one of the most common organizational mistakes — it holds the service team accountable for failures they didn’t cause and can’t fix alone, while allowing other functions to operate without accountability for their contribution to the customer experience.

How do you measure customer experience vs customer service?

Customer service is typically measured through transactional metrics: Customer Satisfaction Score (CSAT) at service touchpoints, First Contact Resolution (FCR) rate, average handle time, and escalation rates. Customer experience is measured through relationship metrics: Net Promoter Score (NPS), customer lifetime value (CLV), churn rate, share of wallet, and advocacy rates. The key distinction is that service metrics measure how well specific interactions are handled, while experience metrics measure the cumulative relationship outcome that determines revenue and retention. Both are necessary — but organizations that only measure service metrics are missing the broader experience signals that predict revenue performance.

Want to understand how both customer service and customer experience are performing in your organization? Learn more about the Experience Audit →

Content Authenticity Statement: The topic area, key elements to focus on, etc. were decisions made by Braden Kelley, with a little help from Claude and Google Gemini to clean up the article, add images and create infographics.

Image credits: Google Gemini

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Markets Don’t Build Themselves, You Must Engineer Them

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Markets Don't Build Themselves, You Must Engineer Them

Exclusive Interview with Bruce Cleveland

In a business landscape increasingly cluttered by “feature wars” and fleeting viral trends, true market leadership isn’t just about who builds the best product — it’s about who defines the problem. In his groundbreaking work, Market Engineering, Bruce Cleveland argues that successful companies don’t just enter markets; they architect them. By blending rigorous systems thinking with the art of category design, Cleveland provides a blueprint for moving beyond commodity status to become a dominant force that sets the rules of the game.

In this insightful Q&A, Cleveland breaks down why “Market Engineering” must be foundational from day one rather than a secondary thought for the marketing department. From the evolution of Chief Storytellers to the strategic distinction between a market and a category, he explores how leaders can steer through the noise — especially in the age of AI — to create a resonant narrative that sticks.

Today we dive deep into the characteristics and necessities of market engineering with our special guest.

Markets Don’t Build Themselves

Bruce ClevelandBruce Cleveland is a former venture capitalist and engineering and product executive at Apple, C3 AI, Oracle, and Siebel Systems. As founder of Traction Gap Partners, he has helped hundreds of startups, scale-ups, and enterprises to transform innovation into impact. His previous book, Traversing the Traction Gap, is taught in universities and used by investors and founders worldwide. Cleveland’s frameworks blend analytical discipline with creative storytelling — empowering leaders in companies of all sizes and industries to transform technology into traction and markets into movements. He lives in Bend, Oregon.

Below is the text of my interview with Bruce and a preview of the kinds of insights you’ll find in Market Engineering presented in a Q&A format:

1. When does it make sense for a company to engage in Market Engineering?

Market Engineering isn’t something you save for later: it’s foundational from the moment you decide to bring a new product or company to life. The earlier you start intentionally defining or redefining your category, shaping positioning, and setting the narrative, the more leverage you have. If you wait until after a product launch or when you’re trying to scale, you’re forced to play by definitions set by incumbents or competitors, which makes differentiation and leadership much harder.

2. Why is it so important for a company to shape the market reality?

If you don’t shape your market’s reality, someone else will, often in a way that disadvantages you. Shaping market reality means you control how problems are defined, which features or metrics matter, and what the buying criteria look like. Market leadership is rarely awarded to the objectively “best” product; it’s achieved by those who frame the market in terms they can win.

3. Why must all leaders intimately understand the difference between a category and a market?

A market is the overarching territory: the set of buyers, sellers, and needs. A category is a specific frame or context you create and own within that market. If you only compete in the market, you become a commodity; if you define and then dominate a category, you set the standards and leave competitors playing catch-up. Leaders must understand this distinction so they can move from playing the existing game to rewriting the rules.

4. What do you think about the Chief Storyteller roles we see appearing in companies?

It’s a positive development; as long as the role goes beyond polished campaign stories and becomes architect and keeper of the full-market narrative. The best Chief Storytellers aren’t just marketers; they’re narrative engineers who unite product, category vision, customer proof, and internal culture into a coherent, resonant story that attracts and aligns stakeholders. Think Steve Jobs: one of the best storytellers ever.

5. Many see Thought Leadership as a combination of messaging and storytelling, what makes it a standalone tenet?

Thought Leadership stands alone because it’s about setting the agenda (leading the conversation) rather than just communicating your point of view. It requires original insight, provocation, and the courage to propose new models, not just synthesize existing ones. When done well, it changes the direction of the market; others start to echo your terminology and frameworks.

6. Why is it so hard for most new products to get traction?

Most new products fail to get traction not because of weak tech, but because of unclear value, undifferentiated positioning, or market confusion. Teams overfocus on features and under-invest in the story, category, and proof. Without clear market engineering, no one knows why the product matters or how they should think about it compared to everything else.

7. Where do companies go wrong with category design?

The most common mistake is either not designing a category at all (just trying to out-feature incumbents) or making it a “naming exercise” disconnected from authentic customer need and business reality. Category design isn’t branding; it’s systems thinking. it should be rooted in a real problem, codified with relentless clarity, and validated with influential customers and analysts.

8. How does the leadership team recognize they got the positioning wrong and how do they fix it?

Market Engineering Book CoverYou’ll know you have a positioning problem if deals stall in the pipeline, you get slotted into the wrong RFP bucket, or media/analysts lump you with solutions you don’t respect. Fixing it starts with honest investigation: talking directly to customers/prospects, auditing every touchpoint, and rigorously re-testing your Messaging Matrix. It’s usually about clarity, not cleverness.

9. What are the biggest pitfalls of message ownership and management and how can leaders avoid them?

The biggest pitfalls are lack of internal discipline and message drift: where every functional group tells the story a bit differently, or the narrative morphs with each campaign. Leaders must treat the messaging as a living, central artifact (like the Messaging Matrix), ensure frequent training, and make every update explicitly cross-functional. Messaging must be owned at the top.

10. What are some of the keys to great storytelling that every leader should master?

Great storytelling starts with empathy: a deep understanding of customer pain and aspiration. Then, it follows with clarity (no jargon), specificity (real data, real outcomes), and tension (what’s at stake in the market). Too often, stories become “laundry lists”. The key is to focus on a single arc: What’s broken in the world, what new future you’re inviting them into, and social proof that it’s real.

11. What are the keys to creating effective thought leadership?

You must have a strong point of view and the willingness to challenge conventional wisdom. Effective thought leadership is not just more content; it’s original, actionable ideas presented consistently across channels and validated with real-world outcomes, not just theory. Authenticity and a learning mindset are critical: the market rewards those who teach, not just those who promote.

12. Does AI make Market Engineering easier or more difficult and why?

AI makes Market Engineering both easier and much harder. Easier, because it democratizes access to research, market signals, and rapid content generation. Harder, because it amplifies noise and makes it much more difficult to stand out unless your positioning, messaging, and insight are precise and differentiated. The bar for clarity and originality rises: those who do Market Engineering well will thrive; those who don’t will be commoditized instantly.

13. Is there anything you wish I had asked so that you could speak to it?

I wish more people asked, “How do you maintain momentum and discipline in Market Engineering after the initial category launch?” Winning the first lap is one thing; evolving category leadership into true market leadership and dominance over the years is another. It’s not a one-time event: it’s ongoing narrative, data, partner ecosystem, and customer proof work. The companies that endure are those that outlearn, outevolve, and outlast, not just outlaunch their competition.

Conclusion

Thank you for the great conversation Bruce!

I hope everyone has enjoyed this peek into the mind of the man behind the insightful new title Market Engineering!

Image credits: Bruce Cleveland, Google Gemini

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Customer Experience Benchmarking

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How Do You Actually Compare?

Customer Experience Benchmarking

by Braden Kelley and Art Inteligencia

Most organizations benchmark their customer experience against themselves. They track NPS month over month, monitor CSAT scores quarter over quarter, and celebrate when the numbers move up. What they rarely do is answer the question that actually matters for competitive survival: how does our experience compare to what our customers can get elsewhere?

Customer experience benchmarking — the systematic comparison of your experience performance against competitors, industry standards, and best-in-class exemplars — is one of the most underused tools in the CX practitioner’s toolkit. It is also one of the most important. CX leaders generate 6x the revenue growth of bottom-quartile peers, per the Forrester CX Index 2026. The gap between leaders and laggards is widening, not narrowing. Organizations that don’t know where they stand relative to that gap are making investment decisions in the dark.

What is Customer Experience Benchmarking?

Customer experience benchmarking is the process of systematically measuring your organization’s experience performance against external reference points — competitors, industry standards, and best-in-class organizations — to understand where you lead, where you lag, and where investment will generate the greatest competitive return.

It is distinct from customer experience measurement, which tracks your own performance over time. Benchmarking adds the external context that transforms a metric from a number into a signal. A Net Promoter Score of 35 means nothing in isolation. A Net Promoter Score of 35 in an industry where the average is 22 means you are performing above average. A score of 35 in an industry where leaders are at 60 means you have a significant competitive gap to close.

Without benchmarking, organizations routinely invest in improving metrics that are already competitive while ignoring gaps that are costing them customers and revenue.

Why Most CX Benchmarking Falls Short

The most common form of CX benchmarking — comparing NPS, CSAT, and CES scores against published industry averages — is useful but severely limited. CSAT is typically based on how consumers feel about a service or product on a sliding scale, and CES measures how effortless it is for customers to interact with an organization. These are legitimate signals, but they have three critical limitations as benchmarking tools:

They measure what customers say, not what they experience. Survey-based metrics capture customer perceptions at a moment in time, filtered through whatever prompted them to respond. They systematically miss the silent majority — customers who had mediocre experiences but didn’t feel strongly enough to complete a survey — and they overrepresent the emotional extremes.

They measure aggregate outcomes, not specific experience drivers. Knowing your NPS is below industry average tells you that you have a problem. It doesn’t tell you where in the journey the problem lives, what is causing it, or what to fix. Benchmarking aggregate scores without diagnosing the specific experience gaps producing them leads to unfocused investment that improves the score without improving the underlying experience.

They don’t capture the full competitive experience landscape. Published industry benchmarks aggregate across organizations with very different models, customer bases, and experience investments. Your real competitive benchmark is not the industry average — it is the specific alternatives your customers are comparing you to, evaluated on the specific dimensions they care about most.

The Four Levels of Customer Experience Benchmarking

Effective customer experience benchmarking operates at four levels, each providing different and complementary insight:

Level 1: Internal Benchmarking

Comparing your own experience performance across time periods, customer segments, channels, geographies, or business units. Internal benchmarking establishes your baseline, identifies where performance is improving or declining, and surfaces the internal variations that indicate what better is possible — if your highest-performing region or channel is significantly outperforming others, the gap represents an internal benchmark that can be studied and replicated.

Best tools: NPS, CSAT, CES trend analysis; journey analytics; complaint and escalation rate tracking; customer effort mapping across channels.

Level 2: Competitive Benchmarking

Comparing your experience performance directly against the specific competitors your customers are most likely to consider as alternatives. This is the most commercially important form of benchmarking and the most underinvested. Analyzing competitor reviews on platforms like Google and Trustpilot and looking for patterns in customer feedback — recurring praise or common complaints — is a starting point. But the most valuable competitive benchmarking requires actually walking the competitor’s experience firsthand — going through their onboarding, calling their support line, submitting a service request — to understand the experience your customers are comparing you to.

Best tools: Mystery shopping of competitors; competitor review analysis; win/loss interview research; shared customer feedback analysis; direct experience walking.

Level 3: Industry Benchmarking

Comparing your performance against published industry standards and research benchmarks. Tools like Contentsquare’s 2026 Digital Experience Benchmark, built from 99 billion web sessions across 6,500+ websites in 9 industries, provide cross-device behavior data spanning traffic, engagement, frustration, conversion, and retention. Forrester’s CX Index, the ACSI (American Customer Satisfaction Index), and industry-specific research provide standardized benchmarks across NPS, CSAT, and CES by sector.

Best tools: Forrester CX Index; ACSI scores by industry; Contentsquare Digital Experience Benchmark; J.D. Power studies; industry association research.

Level 4: Best-in-Class Benchmarking

Comparing your experience against the best experiences your customers encounter anywhere — not just in your industry, but across the categories they interact with most frequently. This is the most ambitious and most valuable form of benchmarking, because customers don’t evaluate your experience against your direct competitors alone. They evaluate it against every excellent experience they have — Amazon’s delivery reliability, Apple’s onboarding simplicity, Ritz-Carlton’s service recovery. When an experience falls below the best available standard in any category, it registers as inadequate regardless of industry norms.

Best tools: Cross-industry experience research; direct walking of best-in-class exemplars; customer interviews that explicitly ask “what’s the best experience you’ve had with any company in any category, and what made it great?”

Four Levels of Customer Experience Benchmarking Infographic

Key Customer Experience Benchmarks by Metric

Net Promoter Score (NPS) Benchmarks

NPS ranges from -100 to +100. General interpretation: above 0 is good, above 20 is favorable, above 50 is excellent, above 70 is world-class. Industry averages vary significantly:

  • Technology/SaaS: 35–45 average; leaders 60+
  • Financial Services: 30–40 average; leaders 55+
  • Retail: 40–50 average; leaders 65+
  • Healthcare: 25–35 average; leaders 50+
  • Telecommunications: 15–25 average; leaders 40+
  • Hospitality: 50–60 average; leaders 75+

Customer Satisfaction Score (CSAT) Benchmarks

CSAT is typically measured on a 1–5 or 1–10 scale and converted to a percentage of satisfied respondents. Industry averages cluster around 75–85% across most sectors, with leaders consistently achieving 90%+. ACSI data for 2025–2026 shows overall US customer satisfaction at approximately 77.4 out of 100 across industries.

Customer Effort Score (CES) Benchmarks

CES measures how easy it is for customers to interact with your organization, typically on a 1–7 scale. Lower effort scores are better. Research by CEB (now Gartner) found that reducing customer effort is more predictive of loyalty than delighting customers — 96% of customers with high-effort experiences become more disloyal, versus only 9% of those with low-effort experiences.

First Contact Resolution (FCR) Benchmarks

FCR measures the percentage of customer issues resolved on first contact. Industry average FCR rates cluster around 70–75%, with best-in-class operations achieving 85–90%. Every percentage point improvement in FCR drives measurable improvements in both CSAT and cost-to-serve.

How to Conduct a Customer Experience Benchmark

Step 1: Define what you are benchmarking and why
Benchmarking everything produces noise. Start with the specific experience dimensions most likely to be affecting your competitive position — the areas where you suspect you may be lagging, or where you are investing most heavily and want to validate that your performance justifies the investment.

Step 2: Select your benchmark references
For each dimension, identify the most relevant reference points: your direct competitors for competitive benchmarking, published industry research for industry benchmarking, and best-in-class exemplars for aspirational benchmarking. The most valuable benchmarks are often the ones that are hardest to obtain — direct competitor experience walking and cross-industry best-in-class research — precisely because they reveal gaps that published survey data doesn’t surface.

Step 3: Gather data across multiple methods
No single data source provides complete benchmark insight. Effective benchmarking combines quantitative measures (NPS, CSAT, CES, FCR) with qualitative research (customer interviews, journey walking, competitor experience analysis) and observational data (direct observation of experience delivery, mystery shopping). Each source surfaces different dimensions of the experience gap.

Step 4: Map gaps to their revenue implications
A benchmark gap is only useful if it is connected to a business outcome. For each significant gap identified, estimate the revenue implication: how much churn is this gap contributing to? How much expansion revenue is it suppressing? How much competitive displacement is it enabling? This translation from experience gap to revenue impact is what makes benchmarking findings actionable at the executive level.

Step 5: Prioritize investments by competitive return
Not all gaps are worth closing. Prioritize experience investments that address gaps in dimensions your customers care most about, where closing the gap would produce the largest competitive differentiation, and where the investment required is proportionate to the revenue at stake.

How to Conduct a Customer Experience Benchmark Infographic

The Role of an Experience Audit in Benchmarking

A customer experience audit is the most comprehensive benchmarking instrument available — one that combines internal experience measurement, competitive experience walking, and best-in-class gap analysis into a single, systematic assessment.

Unlike survey-based benchmarking that measures what customers say about their experience, an experience audit walks the actual experience — physically and digitally traversing every significant touchpoint across your customer journey and your competitors’ — to produce a firsthand, evidence-based comparison (customer journey mapping helps here). It identifies:

  • The specific touchpoints where your experience is measurably inferior to the best available alternatives
  • The friction gaps — moments where your experience requires more effort than competitors’ equivalents
  • The consistency gaps — channels or segments where your experience significantly underperforms your own average
  • The service recovery gaps — how your response to failures compares to competitive and best-in-class standards
  • The personalization gaps — where competitors are demonstrating deeper customer understanding than you are

The output is not a score comparison — it is a prioritized, actionable roadmap of experience improvements ranked by their estimated competitive and financial impact. This is benchmarking that produces decisions, not just data.

Frequently Asked Questions About Customer Experience Benchmarking

What is customer experience benchmarking?

Customer experience benchmarking is the process of systematically measuring your organization’s experience performance against external reference points — competitors, industry standards, and best-in-class organizations — to understand where you lead, where you lag, and where investment will generate the greatest competitive return. It differs from customer experience measurement, which tracks your own performance over time, by adding the external context needed to interpret whether your metrics represent a competitive advantage, a competitive parity position, or a competitive gap that requires urgent attention.

What metrics are used for customer experience benchmarking?

The primary metrics used for customer experience benchmarking are Net Promoter Score (NPS), Customer Satisfaction Score (CSAT), Customer Effort Score (CES), and First Contact Resolution (FCR). Published industry benchmarks for these metrics are available from Forrester, the ACSI, J.D. Power, and industry-specific research sources. However, survey-based metric benchmarking has significant limitations — it measures what customers say, not what they experience, and it measures aggregate outcomes rather than the specific experience drivers producing those outcomes. The most valuable benchmarking combines metric comparison with direct competitive experience walking and qualitative customer research.

How do you benchmark against competitors on customer experience?

Competitive customer experience benchmarking requires multiple approaches used in combination. Quantitative approaches include comparing published NPS, CSAT, and review scores across competitors; analyzing competitor reviews on platforms like Google, Trustpilot, and G2 for recurring patterns; and using win/loss interview research to understand the experience factors most frequently cited in competitive displacement. Qualitative approaches include directly walking the competitor’s experience — going through their onboarding, calling their support line, submitting a service request — to build firsthand understanding of the experience your customers are comparing you against. A customer experience audit typically includes direct competitive benchmarking as a core component.

What is a good NPS score by industry?

NPS benchmarks vary significantly by industry. In technology and SaaS, average NPS is typically 35–45 with leaders above 60. In financial services, averages run 30–40 with leaders above 55. Retail averages 40–50 with leaders above 65. Healthcare averages 25–35 with leaders above 50. Telecommunications typically averages 15–25 with leaders above 40. Hospitality averages 50–60 with leaders above 75. The most meaningful benchmark is not the industry average but the performance of the specific competitors your customers are most likely to compare you against — and the gap between your current performance and best-in-class in your sector.

What is the difference between customer experience measurement and benchmarking?

Customer experience measurement tracks your own performance over time — monitoring NPS, CSAT, CES, and other metrics to identify trends and evaluate the impact of specific investments. Customer experience benchmarking adds external context by comparing your performance against competitors, industry standards, and best-in-class organizations. Measurement tells you whether you are getting better or worse. Benchmarking tells you whether you are competitive — whether your current performance represents an advantage, parity, or a gap that is costing you customers and revenue. Both are necessary, but benchmarking is what connects experience performance to competitive and financial outcomes.

Ready to understand how your experience compares to competitors and best-in-class standards? Learn more about the Experience Audit →

Content Authenticity Statement: The topic area, key elements to focus on, etc. were decisions made by Braden Kelley, with a little help from Claude and Google Gemini to clean up the article, add images and create infographics.

Image credits: Google Gemini

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3 Cultural Shifts That Will Reignite Change in Your Organization

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3 Cultural Shifts That Will Reignite Change in Your Organization

GUEST POST from Greg Satell

On a cold November day in 2013, frustrated by recent events in Ukraine, a journalist named Mustafa Nayyem posted to Facebook, “Okay guys, let’s get serious. Who’s ready to go to the Maidan today at midnight? ‘Likes’ will not be counted. Only comments under this post with the words ‘I’m ready.’ Once there are more than a thousand, we will organize it.”

Nothing needed to be explained. Everyone knew exactly what he meant. Nine years earlier, hundreds of thousands of people flooded Independence Square in Kyiv, locally known as “the Maidan,” to protest a falsified election in a movement called the Orange Revolution. Mustafa was now calling on his fellow citizens to do the same.

It was a moment that changed history. Yet it’s not that moment we should focus on, but what came before. It was what happened in those ensuing nine years—the development of unseen networks, the learning and the cultural change—that made the moment possible. The truth is that for genuine change to take place, significant cultural shifts need to come first.

1. From Preaching To Listening

The Orange Revolution got its name because orange was the campaign color of the opposition candidate, Viktor Yushchenko. “It was not about social mobilization, it was not about political mobilization, it was mostly about the political class in Kyiv,” Mustafa would later tell me. And while it achieved its goal of putting the preferred candidate in office, it would ultimately fail to survive victory, which is what led to the call for people to revolt again nine years later.

Many organizational transformations follow a similar pattern. Convinced change has to come from the top, they start with a big kickoff campaign detailing what change will look like. In a show of force, leaders take center stage and declare their support. The goal is to create a sense of urgency and inevitability around change.

It almost always fails and it usually fails for the same reason: people resist it. The simple reality is that human beings form attachments to people, ideas and other things. When they feel those attachments are threatened, they will lash out in ways that are dishonest, underhanded and deceptive. If you are going to bring change about, that’s what you need to overcome.

There are a number of ways to overcome that kind of resistance, but in the early stages, when the idea is nascent, the simplest and most effective way is to focus on listening rather than trying to overpower with a show of force. Don’t push your idea on people or try to persuade them. Go out and find people who are enthusiastic and want it to succeed.

“You have to go where the energy is,” John Gadsby, who built a movement for process improvement inside Procter & Gamble that has grown to encompass 60,000 employees, told me. “We’ll choose energy and excitement and enthusiasm over the right position, or the person at the right leadership level, or the person whose job it is supposed to be to do that.”

2. From “Us And Them” to “We Together”

Humans are naturally tribal. In fact, decades of research has found that we will tend to form groups based on identity—even if that identity is something we are arbitrarily assigned, like a “red team” and a “blue team”—and will show loyalty to group members and hostility towards outsiders. These results have also been documented in children and even in infants.

We often trip over subtle matters of identity without realizing it. That was certainly true of the Orange Revolution, which had a regional undercurrent few appreciated at the time. Viktor Yanukovych, the thuggish politician who would trigger both the Orange Revolution and the protests that came nine years later, was associated with the Donbass region. The residents there saw an attack on him as an attack on them.

Organizational change agents commonly fall into a similar trap. In a misguided effort to gain credibility, they set themselves and their ideas apart from others. They position themselves with a credential they’ve earned or as being proponents of some school of thought, such as design thinking or agile development. Unwittingly they set up separate ”us and them” identities.

So before you can ignite change, you first need to forge a shared identity based on shared values. That’s exactly the approach Lou Gerstner took in his historic turnaround of IBM. Despite being the first CEO to come from outside the company, he made sure to explain his changes in terms of the firm’s traditional values rather than something different. His efforts led to a legendary success.

3. From Imposed Beliefs To A Co-Created Future

The Orange Revolution was a political movement with political aims. That is, in large part, why despite the initial victory it would ultimately fail in the end. The truth is that you can never base transformation on any particular person, policy or technology. It also has to be rooted in shared values. That’s the only way that you can overcome resistance, survive victory and build a common future.

When people followed Mustafa Nayem to Independence Square the protests were dubbed Euromaidan, because the proximate cause had to do with an EU Association Agreement but also because they represented a desire to adopt European Values. As things heated up, a group of prominent journalists released a video giving voice to these aspirations.

Here’s part of what they said:

There are many things that unite Rivne and Luhansk, Kyiv and Odessa. [cities in the west, east, north and south, respectively]

We want to live in an honest and fair country, where individual rights are respected, where you can freely express your views and not be afraid of the police, where courts are just and can’t be bought, where there is real competition in business and opportunity to work in an honest way.

Today, it’s common for Ukrainians to refer to the events of 2014 as the Revolution of Dignity, because as events progressed it became less about the country’s relationship with its western neighbors and more about how they saw themselves. No longer would they accept being simple pawns in the games of corrupt leaders, but would decide their own future.

For change to succeed, everybody needs to see themselves as heroes in the story. In some cases, that means that people will have to decide to seek a different journey in another place. In other cases, they will need to be shown the way out. But the possibility for them to thrive in a shared future needs to be there.

Becoming Mundane And Ordinary

Today, few would question the dignity of the Ukrainian people. In fact, they have become such an inspiration to the world that it’s hard to remember that the country used to be a very cynical place. When I first arrived there in 2002, I was struck by the apathy. There was so little hope that anything could ever change that few saw any sense in even trying.

My friend, the global activist Srdja Popović, once told me that the goal of a revolution should be to become mainstream, to be mundane and ordinary. If you are successful it should be difficult to explain what was won because the previous order seems so unbelievable. That’s certainly true of Ukraine today, but also true of successful organizational transformations.

Today, Apple is so associated with Steve Jobs and the Macintosh that it seems incredible that he was fired from the company, in large part due to tensions that resulted from its development. Lou Gerstner’s turnaround of IBM was so complete it seems crazy that most people assumed the company would be broken up and sold for parts. Artificial intelligence has become so embedded in our lives, it’s hard to remember that not long ago it seemed like science fiction.

One of the things that makes change so challenging is that when we hear about the successes—failures are rarely documented—the story is told in a way that makes everything seem inevitable. We have to remember that things start out much differently. There were failures along the way that needed to be learned from and overcome.

The successful path to transformation starts with culture, how people see themselves and those around them. That doesn’t just happen. Leaders must work intentionally to create shared values. The truth is that change that is imposed never sticks, because it asks those who must affect change to betray themselves. You must first change minds before you can change actions.

— Article courtesy of the Digital Tonto blog
— Image credit: Google Gemini

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We Need More Innovators and Scientists in Leadership Roles

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We Need More Innovators and Scientists in Leadership Roles

GUEST POST from Pete Foley

Our world is changing at an unprecedented rate. We are in an innovation driven economy. AI, genetic manipulation, energy innovation, climate, and virtually anything driving change are all highly technical and complex. And all come with high stakes pros and cons.

Scientists and innovators navigating this requires strategic leadership that understands technical complexity, uncertainty and that collectively has some knowledge of basic science and engineering. 

Politics Lacks Scientists: Today, while more than half of US Senators have a law background, only one has a science PhD.  I believe this creates a serious gap in fundamental knowledge between our strategic leaders and the innovators that are driving change.

Experts or Oracles? Of course, our leaders have access to ‘experts’ to help them with complex topics.  But when the fundamental knowledge gap between leaders and experts becomes too big, experts become oracles. They pronounce rather than persuade. When this happens we risk the determining factor in strategy becoming superior communication skills, instead of knowledge or superior ideas.  The ideas (and regulations) that win are not the necessarily best ones, but the ones championed by good communicators, salesmen scientists or smooth talking lobbyists.  It’s dangerous to follow the science blindly, and even riskier to regulate what we don’t understand. That invites dangerous unintended consequences. But increasingly, that is the path we are on.
 

Why We Need More Innovators and Scientists in Leadership Roles

Of course, our leaders don’t need to all be 160 IQ polymaths with PhD’s in quantum mechanics. But to make good decisions they do need to at least be able to understand and apply critical thinking to the inevitably conflicting opinions of experts.

Communicating Science and Technology: Now of course, much of the onus for promoting understanding of complex technology lies with us in the broader innovation and science community.  If we cannot communicate knowledge to people who own resources and executive power, then we risk that knowledge becoming redundant.

But communication is always a two way street. Bridging between leaders and experts requires some common ground.  It’s really hard to have a useful discussion with someone who does even have a basic vocabulary for a topic. As technology and innovation become increasingly important, without more technically savvy leaders we risk a disconnect between strategy, regulation and knowledge. As our leaders get older, and more disconnected from the science driving change they rely less on quality of ideas, and more on appealing framing of ideas, or perhaps familiarity with equally disconnected experts. That is a dangerous path.

Non Scientific Mindsets Facing Technical Challenges. One key danger is the tendency to view choices as binary, another is sunk cost. Binary choices are superficially easy, but in the real world most innovation is not black and white, but instead involves some form of trade off.  Whether it is AI, energy strategy, pharmaceutical development or one of the other ever growing list of emerging technologies, there are benefits, but also costs.  With AI for example, the benefits of gaining and holding global leadership of the technology are likely as economically huge as the opportunity cost of not doing so.  But with big opportunity also comes big risks, including the environmental costs of data centers, risks to societal structure, and even existential risk to humanity itself.  The stakes don’t get much higher.

The Uncertainty Principle: And this is multiplied by the sunk cost fallacy. Over commitment to an incorrect binary choice can be really risky. While we know there are going to be pros and cons to any new technology, we rarely understand them very well in advance.  Innovation is by definition a dive into the unknown, and that makes accurately predicting both upsides and downsides really difficult.  This requires flexible, agile thinking, openness to new data, and a willingness to adjust mid-flight, skills inherent to science and technology . 

But as a society, if anything we seem to be moving away from flexible thinking, and towards more rigid viewpoints that are often heavily pre-primed by affiliations, preconceptions and bizarrely, politics.  People are often passionately for or against AI, but all too often without really knowing why. ‘Green’ energy is polarizing, climate change is divisive.  But while passion and ownership have their place, often the best answer is not cheerleading for a team. Instead it’s beneficial to find a flexible balance that acknowledges the pros and cons, and that ideally identifies non zero sum answers for those contradictions. But that again typically requires nuance, and some level of technical understanding. 

Finding Non Zero Sum Answers: The good news is that once we step away from polarized and binary thinking, non zero sum solutions are sometimes not as hard to find as we think.  Just as an example, with AI, there is potential to have our cake and eat it.   If we cut out digital slop, it’s conceivable that could we achieve and maintain technology leadership, but with much lower environmental cost.  For example, using AI to solve complex medical problems may be a net benefit that is worth some damage to our wilderness, or use of our scarce resources.  But action figures, generic illustrations, mediocre music and often pointless copies of master artists not so much!  I’m sure all of the latter help advance our knowledge to some degree, and help to justify AI investment, but by being more selective, could we achieve the same or similar ends with a superior benefit/cost ratio? 


The Human Advantage: But making smart trade-off decisions like this requires flexible and creative thinking.  Ironically that is one of the things humans still do better than AI.  We just need to embrace our human strengths, but also make sure our leaders also reflect those strengths.

Innovators in Leadership Roles: This means we need a more balanced and scientific approach to leadership if we are navigate the increasingly technology driven future.  Having lawyers making laws is not bad per se, but I passionately believe we need a more diverse set of skills at our upper leadership levels if we are to effectively navigate the coming years. That means the innovation and scientific community needs to step up.  We also need to get much better, and mea culpa, at communicating complex issues.  It’s critical to be clear and simple but not simplistic.

The Tyranny of Simplicity: Simplistic answers, memes, and binary choices have a great deal of superficial appeal.  And politicians and the media exploit this very effectively. In our information overloaded, time constrained world, everybody’s cognitive bandwidth is stretched.  We often seek answers rather than understanding because that’s all we have time for.  But from a leadership perspective, we need to understand that limited cognitive bandwidth is not the same as limited intelligence. People may grasp for simplistic answers, but because they have no commitment to them based on their own knowledge or critical thinking, that grasp is tenuous. This means that being simplistic can be self defeating in the long run.  For example, take the much quoted, ‘globally agreed’ climate target; to not exceed a 1.5 degrees Celsius increase since pre-industrial times. For sure, some people will accept this without question. But other enquiring minds will ask if 1.49C OK? Is this a tipping point? Do we fall of a cliff at 1.51C. Conversely, what happens if we exceed that limit and nothing dramatic happens?  Do we discard that boundary, or move it? Then there are obvious questions around how we address that boundary. What will it take to prevent crossing it?  What are the trade offs?  Who has the sphere of influence to actually make a difference?  It’s OK to have a simplistic position, but it needs to be supported by layered reasoning.


Cry Wolf: I’m not suggesting that climate scientists who promote 1.5C don’t grasp this complexity.  But somewhere in the path from science to politicians and media the real world complexity it often gets lost in translation.  And thats not trivial, as it creates the risk of ‘cry wolf’ effects, and of leaders being perceived as manipulative.   If we overstate the importance of 1.5 C, and it proves to be wrong, or at least a softer limit than previously advertised, we risk people perceiving that they have been mislead or manipulated.  That then feeds skepticism, and even gives support to some of the wilder ‘conspiracy theories’. Once a source has become discredited on one vector, it is typically discredited on everything. 

No easy answers to this.  But I believe innovators and scientists really need to take a bigger leadership role in a world where innovation is increasingly the driving force. Politicians generally don’t get elected because they deeply understand complex issues, but because they understand how to motivate, communicate, simplify and manipulate. They often rely on peoples limited cognitive bandwidth, as this helps them to craft simple slogans, concepts, and sometimes trigger fear and division. Remember that we dislike losing something about twice as much as we like gaining it, which makes fear a very powerful manipulative tool. That brings power, but not necessarily wisdom. But limited cognitive bandwidth is not the same as limited intelligence. And simplistic concepts are vulnerable to challenge, or evolving data.

Of course, we don’t want to make every issue a PhD thesis.  But we do need to acknowledge increasing complexity and uncertainty, and at the very least develop authentic, layered narratives that acknowledge complexity and the inevitable uncertainty of an innovation driven world.  Without that, our strategies become extremely fragile, and easily shattered the first time we are proved wrong. Even if we may start from a position of intense conviction, we must also change paths in the face of compelling evidence. Scientists and innovators tend to be good at this. It’s a skill that maybe needs to be used more broadly

Image credits: Google Gemini

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