Operational vs. Experiential Metrics
Report #1 - Why Decision-makers Must Include Experience Metrics
Executive Summary
“Nothing happens until somebody sells something.”
– Anonymous Dun & Bradstreet Executive
A sale is the catalyst that turns ideas into action, resources into revenue, and potential into progress. Without it, even the best products or services remain dormant and unutilized.
The Secret: People buy to fulfill emotional needs or desires, seeking solutions that resonate with their feelings. Once the emotional connection is made, they use logic and reason to justify their decision.
The Question: How can we afford to ignore the emotional pulse that drives every buying decision?
This report highlights the crucial differences between Operational Metrics and Experiential Metrics and emphasizes the need for their integration into modern dashboards for forward-thinking Executives and Product Leaders. As AI, Journey Analytics, and Affective Computing redefine Experience Management (XM), the evolution of metrics and moving beyond traditional measurement practices becomes essential.
Concepts like Journey Economics are set to revolutionize how Operational and Experiential data are integrated into decision-making at every level of the organization, equipping CX Practitioners and C-Suite executives with innovative tools to deliver more value to customers and maximize ROI for the business. This balanced approach ensures products not only function efficiently but also resonate deeply with customers and users, fostering engagement, adoption, and sustained business growth. Embracing this dual metric strategy empowers organizations to align short-term operational goals with long-term customer-centric success, driving better decision-making and business outcomes across the board.
Key Takeaways
Operational vs Experiential Metrics: Experiential metrics capture qualitative insights into customer emotions, which is the #1 driver of business outcomes, user satisfaction, and long-term business value.
Predictive Power: Experiential metrics offer stronger predictive capabilities regarding customer behavior, providing insights into why users act a certain way and enabling more customer-centric decision-making at every level across the entire organization.
Journey Economics: A revolutionary concept that links revenue, costs, and profitability across customer journeys, allowing businesses to optimize customer experiences and drive profitability.
Holistic Product Success: Successful products not only perform well functionally but also meet or exceed customer expectations emotionally, which is crucial for long-term engagement and retention.
Driving Innovation and Long-Term Value: By understanding unmet customer needs through experiential metrics, businesses can drive innovation, prioritize features that enhance user satisfaction, and focus on building lasting customer relationships that contribute to sustainable financial growth.
Metrics Overview
Operational Metrics: AKA Product KPIs or Business metrics, focus on the functionality, performance, and efficiency of a product or service, providing quantitative, real-time insights crucial for maintaining day-to-day operations and ensuring the product or service works as intended and returns value to the business.
Experiential Metrics: Encompassing EX, CX, and UX, are vital for capturing the full spectrum of human emotions, perceptions, and overall satisfaction, particularly in the context of how people accomplish their jobs. These metrics, a powerful fusion of qualitative and quantitative data, offer deep tactical and strategic insights that can solve current customer friction and predict long-term customer behavior and loyalty, making them indispensable for shaping future strategies and driving sustained success.
By integrating both operational and experiential metrics into their dashboards, Product Managers, CX Practitioners, HR teams, and Corporate Insights Groups can achieve a holistic view of employee, customer, product, service, and overarching customer relationship and brand health.
This dual approach ensures that products not only perform efficiently but also deeply resonate with users, driving stronger engagement, adoption, and retention. Ultimately, this aligns an organization’s operations with real customer needs, which fuels sustained business growth. Embracing this balanced strategy empowers better decision-making across organizational silos, while seamlessly aligning short-term operational objectives with long-term, people-centric success.
Key Differences Between Operational Metrics and Experiential Metrics
1. Focus:
Operational Metrics: The primary focus is on the product or the business, not the customer. Product-led organizations put the product at the center of everything they do. The orientation for delivering value tends to be more focused on Value-to-Business than Value-to-Customer. Thus, Product Dashboards tend to reflect how value is returning to the business, and not how value is being created for the customer. Look at a typical Product Dashboard and you will find KPIs such as, NNR (Net Revenue Retention), MRR (Monthly Recurring Revenue), OKRs (Objectives and Key Results), Growth, PES (Product Engagement Score), Retention, and Adoption. The primary focus is squarely on KPIs that measure value being returned to the business. You’ll also find KPIs that measure how well the product functions. I.e., does it work as intended? These metrics are concerned with business processes, product quality, the efficiency and performance of a product or service. You might also find KPIs that measure tangible aspects of Product (technical) performance like system uptime, page load times, error rates, user activity, feature usage. The only KPI that comes close to measuring an actual human experience on a Product Dashboard might be customer sentiment, which measures the emotional tone behind a body of text. It aims to determine whether the sentiment expressed in a text is positive, negative, or neutral. So, it’s significantly lacking the depth and context of real human emotions.
Experiential Metrics: The primary focus is on customers and users. These metrics focus on the user's emotional and psychological response to the product. Customer-centric organizations put the customer at the center of everything they do, and that informs what product they need to build so that it delivers Value-to-Customer and returns Value-to-Business. This runs counter to Product-led, where too often the focus is more on agile process and sprints than on gathering contextual insights that clarify customer needs. Experiential metrics capture how users feel about the product in the context of how they’re trying to get their jobs done. They also capture the overall experience customers have at the higher-level touchpoints with a business and their brand. Beyond sentiment, Experiential metrics measure signals of human emotion and how that translates into product perceptions, which determines user actions and customer behavior.
2. Data Type:
Operational Metrics: Typically, these metrics are quantitative and objective, often derived from Business or Sales systems, survey tools, usage data, system logs, support cases, customer success, performance analytics, and numerous sources of customer and operational data.
Experiential Metrics: These data are often qualitative or a blend of qualitative and quantitative data, gathered through contextual interviews, user research and testing, customer feedback, surveys, attitudinal and behavioral research, sentiment analysis, and more recently, LLMs (Large Language Models) used by AI-powered systems. Affective Computing is another emergent source of data and analytics, which empowers machines to perceive and respond to human emotions, voices, facial expressions, and other sources of human biofield or electromagnetic signals. We’re seeing new IP for devices that capture these types of human data.
3. Time Horizon:
Operational Metrics: Most are lagging metrics that look at “What Happened” after a product release. E.g., NPS and NRR are backward looking surveys and financial data that report on a monthly or quarterly basis. By the time the results are analyzed and rolled-up for consumption, executives are “looking in the rearview mirror” at what happened weeks or months ago. Conversely, some of these Operational metrics can provide real-time feedback on products at various stages of release, such as Beta, and are crucial for day-to-day operations and short-term decision-making post-release (e.g., GA). On balance, Operational metrics include more lagging metrics than real-time metrics. Few provide leading or predictive metrics that inform what actions a customer will take next, why or how they will act, and offer insights on what should be done next by reflecting on the cumulative impact of multiple interactions with the product.
Experiential Metrics: Most are leading metrics that are essential for understanding the attitudinal and behavioral outcomes of human interactions with products and services before they are released. They capture customer perceptions at the Journey and Touchpoint levels of Experience Management, helping to predict user and customer behaviors before a product is built or shipped. Additionally, some of these Experiential metrics, such as CSAT, can serve as lagging composite “Relationship” metrics, measuring satisfaction and brand loyalty based on the overall customer experience over time.
4. Impact on Business:
Operational Metrics: Measures the in-market success of a product or service by assessing user adoption, core feature usage, and how quickly users find value. It evaluates customer retention versus churn, user engagement, and habit formation with the product. These metrics directly reflect the product's functional performance, influencing short-term business outcomes like operational efficiency and cost management. This is typically the responsibility of today's Product Manager, from an R.A.C.I. perspective. Product Leaders are setting the OKRs and KPIs for their business, then ‘cascading’ the accountability down through the ranks of their organization. Operationally speaking, this approach ensures that the entire organization is aligned with the business objectives (i.e., “O”) and accountability for the key results (i.e., “KR”) that the business must achieve together. It’s all about results, Value-to-Business, and winning market share.
Experiential Metrics: Measures the Value-to-Customers and how that returns Value-to-business. Emotions are the #1 driving force behind business success. Human emotions shape perceptions, which in turn fuel all economic behavior. As a result, experiential metrics hold immense power—they not only influence but also elevate operational metrics. Senior Sales Leadership understands this dynamic, encapsulated in their time-honored mantra, "Nothing happens until somebody sales something," acknowledging that customers make decisions emotionally and justify their decisions rationally. Experiential metrics directly impact critical business outcomes, including customer retention, satisfaction, and brand reputation, all of which are vital for long-term success and maximizing customer lifetime value. These metrics also drive user adoption, accelerate time-to-value, enhance product stickiness, foster growth, engagement, and loyalty, making them a far more reliable KPI for predicting business impact than traditional metrics like NPS, which merely estimate potential future recommendation behavior. Unlike proxy metrics, Experiential metrics capture authentic human experiences, offering a more trustworthy and actionable insight into predicting human behaviors that result in measurable business outcomes.
Decision-makers Must Include Experience Metrics
Customer-Centric Decision Making: While operational metrics help ensure the product works well, Experiential metrics ensure it works well for the customer. By including Experiential metrics in addition to Product KPIs, Product Managers can make decisions that align more closely with customer needs and preferences, leading to improved product-market fit and business impact.
Predictive Power: Experiential metrics are stronger predictors of future customer behavior. While proxy metrics like NPS can signal the likelihood of recommendations and potential churn, Experiential metrics measure real human behavior and why that behavior happened. Experiential metrics are crucial for understanding Customer Value. Deterministically, you’ll have all the data necessary to predict how customer value aligns with their needs, both functionally and emotionally. These metrics reveal whether the value sought by a customer is based on economic cost or symbolic affiliation. Without understanding the emotional connection users have with a product or brand, other metrics lose their predictive power. This understanding allows for predicting the economic impact of the customer experience on product success and the company's bottom line.
Journey Economics: This is the "Holy Grail" of Experience metrics, as it reveals the relationship between revenue, costs, and profitability across different customer journey segments. It allows the business to proactively “pull levers” to steer the customer toward their goals—increasing goal completion and customer satisfaction. By analyzing and visualizing these costs, you can assess journey profitability and directly link customer centricity to business outcomes. This empowers Experience Management practitioners to optimize customer journeys for maximum business impact.
Holistic Product Success: A product can perform well operationally but still fails to meet customer expectations. Experiential metrics offer a fuller picture of product success by identifying gaps in user experience, even when operational performance is on track. For example, a feature may work as intended but still falls short in helping the user achieve their goals more effectively, with less effort, and greater satisfaction.
Driving Innovation: Understanding how users feel about a product reveals unmet needs and pain points that rational operational data can't capture. This insight drives innovation and empowers Product Managers to prioritize features and improvements that will significantly boost user satisfaction, ultimately elevating other adjacent operational KPIs as well.
Long-Term Business Value: Experiential metrics are directly linked to long-term business outcomes like customer loyalty, brand reputation, and customer lifetime value. By including these experience metrics in Dashboard KPIs, Product Managers can focus on building lasting customer relationships that contribute to sustainable growth and profitability.
User Experience: Operational metrics are vital for maintaining product functionality, but Experiential metrics are key to understanding and improving the user experience. Product Managers who incorporate Experiential metrics into their KPIs are better positioned to create products that resonate with users, driving stronger adoption, growth, retention, and overall business success.
Influence Behavior: Product Managers may have different views on which metrics matter most, but it's crucial to recognize that Experiential metrics are the only ones that truly capture how users feel about a product or service. Understanding the role of human emotions in shaping customer perceptions is vital, as these emotions directly influence purchasing, adoption, usage, and continued engagement.
What Product Managers Monitor
Product Managers typically track three categories of metrics, including: Product Usage, Product Quality, and Product-Market Fit. Each has its own unique benefits and drawbacks. Let's explore them further.
1. Product Usage
Product Usage refers to the various ways in which users interact with a product, including how often they use it, which features they use the most, the duration of their sessions, and the overall engagement patterns. It encompasses both quantitative data (like the number of users, frequency of use, and specific actions taken within the product) and qualitative insights (like user satisfaction and feedback). Monitoring product usage helps Product Managers ensure that the product meets users' needs, remains competitive in the market, and continues to evolve in line with user expectations and business goals.
Drawbacks: Tracking Usage data provides valuable insights into how users interact with a product, but it has its drawbacks. While it captures quantitative metrics like frequency, feature usage, and engagement patterns, it may overlook deeper qualitative factors such as user satisfaction and specific pain points. This can lead to a narrow focus on surface-level interactions, potentially missing the underlying reasons behind user behavior, and failing to address critical issues that impact long-term product success.
Why Product Managers Monitor Product Usage:
Understanding User Behavior: By analyzing how users interact with the product, Product Managers (PMs) can gain insights into what features are most valuable, which parts of the product may be confusing or underutilized, and overall user satisfaction.
Identifying Areas for Improvement: Monitoring usage can highlight areas of the product that might need refinement or additional features. If certain features are rarely used, it might indicate that they are not intuitive, not needed, or not marketed well to users.
Driving Product Development: Product usage data helps PMs make informed decisions about future updates or new features. By understanding what users value most, PMs can prioritize development efforts to focus on high-impact areas.
Measuring Success: Tracking usage metrics allows PMs to measure the success of a product or a specific feature. Metrics like daily active users (DAU), monthly active users (MAU), retention rates, and churn rates are all important indicators of a product's health and success in the market.
Optimizing User Experience: By analyzing product usage patterns, PMs can identify pain points or friction in the user experience. This enables them to optimize the product to make it more user-friendly, which can lead to higher user satisfaction and retention, while lowering UX Debt.
Forecasting and Strategy: Product usage data is crucial for forecasting future growth, setting strategic goals, and making data-driven decisions. It helps in identifying trends and predicting future user behavior, which can be critical for long-term planning.
Supporting Marketing and Sales: Usage data can also inform marketing and sales strategies. Understanding which features resonate most with users can help in crafting more targeted messaging and improving campaigns.
Improving Customer Retention: By monitoring how often and in what ways customers use the product, PMs can identify potential drop-off points and intervene to improve retention. This could involve enhancing certain features, providing more user education, or offering targeted incentives to keep users engaged.
2. Product Quality
Product Quality refers to the overall standard or grade of a product in terms of its design, functionality, reliability, durability, and user experience. It encompasses how well the product meets the needs and expectations of users and how consistently it performs over time. Monitoring product quality is crucial for ensuring that a product meets customer expectations, performs reliably, and upholds the brand's reputation. It enables Product Managers to proactively manage risks, drive continuous improvement, and maintain a competitive edge in the market.
Drawbacks: While Product Quality data is essential for maintaining high standards and meeting customer expectations, it also has some drawbacks. One key limitation is that it often focuses on quantifiable aspects, such as features and functionality, potentially overlooking subjective factors like user experience and emotional satisfaction. Additionally, relying heavily on past performance data can hinder innovation by reinforcing existing standards rather than encouraging new approaches. Moreover, the complexity of consistently measuring and interpreting quality across different markets and user segments can lead to fragmented insights, making it challenging to address quality effectively.
Why Product Managers Monitor Product Quality:
Satisfaction: High product quality is directly linked to customer satisfaction. If a product performs well, meets user needs, and is free of defects, customers are more likely to be satisfied, leading to positive reviews, repeat business, and word-of-mouth referrals.
Brand Reputation: The quality of a product significantly impacts the reputation of the brand. Consistently high-quality products build trust and loyalty among customers, while poor quality can lead to negative perceptions, damaging the brand's image in the market.
Competitive Advantage: Monitoring and maintaining high product quality can provide a competitive edge. In a crowded market, quality often differentiates one product from another, influencing customers' purchasing decisions.
Reducing Costs: Poor product quality can lead to increased costs due to returns, repairs, and customer support needs. By monitoring quality, Product Managers can identify and address issues early, reducing the costs associated with defects and inefficiencies.
User Retention: High-quality products tend to have lower churn rates. When a product consistently delivers value and performs as expected, users are more likely to continue using it and less likely to switch to a competitor.
Compliance and Safety: In many industries, products must meet specific regulatory standards and safety requirements. Monitoring product quality ensures that these standards are consistently met, reducing the risk of legal issues and ensuring the product is safe for users.
Feedback Loop for Improvement: Quality monitoring provides valuable feedback for continuous improvement. By tracking quality metrics, Product Managers can identify recurring issues, understand their root causes, and work with the development team to make necessary improvements.
Market Positioning: A high-quality product can be positioned as a premium offering that appeals to the symbolic value being sought by customers, allowing the company to charge higher prices and target a more discerning customer segment. Monitoring quality ensures that the product consistently meets the symbolic value expectations of this target market.
Support for Marketing and Sales: Product quality is a key selling point. When quality is consistently high, it can be leveraged in marketing and sales efforts to attract new customers and justify the product's price point.
Long-term Sustainability: Ensuring high product quality contributes to the long-term sustainability of the product in the market. It helps in building a loyal customer base, reducing customer acquisition costs, and ensuring a steady revenue stream over time.
Common Metrics for Monitoring Product Quality:
Defect Rate: The percentage of units with defects out of the total produced.
Customer Complaints: The number and type of complaints received from users.
Return Rate: The percentage of products returned by customers due to quality issues.
Mean Time Between Failures (MTBF): The average time between product failures.
Net Promoter Score (NPS): A measure of customer loyalty and satisfaction, often correlated with product quality.
First Pass Yield (FPY): The percentage of products that pass quality checks on the first attempt.
3. Product-Market Fit
Product-Market Fit (PMF) refers to the point at which a product satisfies the demands and needs of a specific target market. It's the point where the product's value proposition aligns closely with the market's needs, whatever that market’s maturity may be. PMF results in strong customer uptake, retention, and growth. Achieving Product-Market Fit means that the product has found its audience, and there is clear evidence that the market wants and values the product. Product-Market Fit is a critical metric for Product Managers because it signals that a product has successfully met market demand, is ready for scaling, and has the potential for long-term success. Monitoring PMF ensures that the product remains aligned with market needs and continues to provide value to its target audience.
Drawback: The primary drawback of Product-Market Fit (PMF) data is its potential to create a false sense of security once initial market alignment is achieved. Relying too heavily on PMF data can lead to complacency, where companies may overlook evolving market dynamics, maturity, or emerging customer needs, ultimately stifling innovation. Additionally, PMF data often focuses on short-term indicators like customer uptake and retention, which may not fully capture long-term sustainability or the depth of market demand. This narrow focus can result in scaling prematurely or missing opportunities to refine the product for broader or future markets.
Why Product Managers Monitor Product-Market Fit:
Validation of Market Need: Monitoring PMF helps Product Managers validate whether the product solves a real problem for a sufficiently large segment of the market. Achieving PMF indicates that the product meets the needs of its target audience effectively.
Guiding Product Development: Before achieving PMF, a product may undergo several iterations based on user feedback and market research. PMF acts as a milestone, signaling that the product's core value proposition is solid, allowing the team to focus on scaling and refinement rather than fundamental changes.
Resource Allocation: PMF is a key indicator for determining how resources should be allocated. Once PMF is achieved, it makes sense to invest more heavily in marketing, sales, and scaling the product. Before PMF, the focus might be more on research, development, and user testing.
Customer Retention and Growth: Strong Product-Market Fit is usually reflected in high customer retention rates and organic growth. Customers who find real value in a product are more likely to continue using it, recommend it to others, and help drive growth through word of mouth.
Investor Confidence: For startups and growing companies, demonstrating Product-Market Fit is crucial for attracting investment. Investors look for signs of PMF as it indicates that the product has the potential for sustainable growth and profitability.
Strategic Decision Making: Monitoring PMF helps Product Managers make strategic decisions about product roadmap, market expansion, pricing, and positioning. If PMF is not achieved, it might indicate the need for a pivot or a shift in strategy.
Competitive Positioning: Understanding PMF allows a company to better position itself against competitors. A product that has achieved PMF can differentiate itself more effectively in the market, emphasizing the unique value it provides to customers.
Customer Feedback Integration: Achieving PMF often involves integrating feedback from early adopters and key customers. This iterative process helps refine the product to better meet market demands, ensuring that the product remains aligned with user needs.
Market Expansion: Once PMF is established in one segment, Product Managers can explore expanding into adjacent markets or broader audiences. Monitoring PMF helps determine when it’s appropriate to scale and which markets to target next.
Sustaining Long-Term Success: PMF is not a one-time achievement but a state that needs to be maintained. Markets evolve, and customer needs change, so continuous monitoring of PMF ensures that the product remains relevant and continues to meet the market’s needs over time.
Indicators of Product-Market Fit:
High Customer Retention: Users continue to use the product regularly over time.
Positive Customer Feedback: Customers express satisfaction and are willing to recommend the product to others.
Strong Sales and Growth Metrics: Revenue, user base, and market share are growing steadily.
Low Churn Rate: A low rate of customers leaving or discontinuing use of the product.
Organic Growth: Significant growth driven by word-of-mouth or organic referrals, rather than heavy marketing spend.
High Net Promoter Score (NPS): Indicates that customers are likely to recommend the product to others.
Experiential Metrics Matter
Experiential Metrics are powerful tools that quantify and qualify the business impact of a user's interaction with a product, service, or brand.
They delve deep into how users perceive, engage, and make economic decisions based on their experiences. By focusing on the entire user journey—capturing satisfaction, emotions, and overall experience—these metrics reveal critical insights into usability, accessibility, value, and desirability. They extend far beyond mere functionality and performance, offering a comprehensive understanding of what truly drives customer behavior, brand loyalty, and returned Value-for-Business.
Holistic Understanding of User Interaction: Experiential metrics offer a deep, comprehensive view of user interactions, capturing not only actions but also the emotions and perceptions that accompany them. This holistic insight is essential for crafting products that transcend mere functionality, delivering experiences that are not only enjoyable but also resonate with users on a symbolic and meaningful level.
Enhancing Customer Satisfaction and Loyalty: Positive user experiences directly drive higher customer satisfaction, cultivating loyalty that translates into repeat business and powerful word-of-mouth referrals. By rigorously tracking experience metrics, companies can ensure their products consistently exceed customer expectations. As a result, CX Leaders typically achieve no less than 12% greater CX Quality and Loyalty-driven revenue compared to their CX-lagging competitors.
Identifying Pain Points and Areas for Improvement: Experience metrics reveal critical issues and friction points within the user journey, enabling Product Managers to prioritize improvements that directly elevate the user experience. By embracing Journey Economics, businesses gain the ability to analyze the financial impact of frictions impacting these touchpoints, assessing journey financial impact with precision. This approach not only enhances customer satisfaction but also strategically aligns customer-centric initiatives with tangible business outcomes, driving profitability and long-term success.
Driving User Engagement and Retention: A better user experience encourages increased engagement and reduces churn. Satisfied users are more likely to explore additional features, spend more time with the product, and remain long-term customers.
Informing Design and Development Decisions: Insights from Experience metrics drive the design and development process by pinpointing what aspects of the product truly resonate with users and where improvements are necessary. This data-driven approach results in more user-centric and impactful product iterations, ultimately accelerating time-to-value and maximizing return on investment.
Differentiating in Competitive Markets: In competitive markets where products offer similar functionalities, a superior user experience becomes a critical differentiator. By closely monitoring and enhancing experience metrics, a product can not only stand out but also capture a larger share of the market, driving increased user acquisition and long-term financial growth.
Supporting Business Outcomes: Positive user experiences directly correlate with significant business outcomes, including increased revenue, lower costs, less risk, expanded market share, and higher customer lifetime value. Leveraging experience metrics provides the actionable data needed to drive strategies to achieve these profitable results, turning user satisfaction into measurable financial gains.
Validating Product-Market Fit: Experience metrics provide concrete evidence of how effectively a product meets the needs and expectations of its target market. High scores in these metrics signal a strong product-market fit, positioning the product for successful scaling and driving revenue growth.
Reducing Support and Maintenance Costs: A well-crafted user experience minimizes errors, misunderstandings, and the demand for customer support, directly cutting operational costs. By actively monitoring experience metrics, companies can address usability issues before they escalate, driving significant cost savings and improving overall efficiency.
Driving Innovation: Understanding user experiences unlocks powerful opportunities for innovation by exposing unmet needs and desires. Leveraging experience metrics can inspire the development of new features and services that not only delight users but also keep the product ahead of the competition, driving growth and increasing market share.
Align Experiential Metrics with Product Metrics
Experience metrics complement and enhance other product metrics that Product Managers already monitor, such as product usage, quality, and product-market fit. Here's how they align:
1. Complementing Product Usage Metrics
Deeper Insights: While usage metrics tell you what users are doing, experience metrics explain why they behave that way. For example, low usage of a feature may be clarified by poor usability scores indicating that users find it difficult to use.
Improving Engagement: By improving experience metrics, engagement and usage often increase as users find the product more intuitive and enjoyable.
2. Enhancing Product Quality Metrics
Perceived Quality: Experience metrics capture users' perceptions of quality, including aspects like ease of use and aesthetic appeal, which traditional quality metrics might miss.
Reducing Defects: Usability testing and related experience metrics identify design flaws that lead to errors, allowing teams to improve the product before defects impact users.
3. Supporting Product-Market Fit Metrics
User Satisfaction: High experience metrics indicate that the product meets or exceeds user expectations, reinforcing product-market fit.
Adoption Rates: Positive user experiences drive word-of-mouth referrals and organic growth, which are key indicators of strong product-market fit.
4. Informing Strategic Decisions
Prioritization: Experience metrics help prioritize features and improvements based on what will most enhance the user experience and deliver business value.
Resource Allocation: Understanding where users experience the most friction allows teams to allocate resources effectively to areas that will have the greatest impact.
5. Creating a Feedback Loop
Continuous Improvement: Regular monitoring of experience metrics creates a feedback loop where user input directly informs ongoing product development and refinement.
Adaptive Strategies: Experience data allows Product Managers to adapt strategies in real-time, responding swiftly to changing user needs and market conditions.
Common Experience Metrics
Customer Experience Quality: AKA CX Quality is a composite score that tracks customer experience in three different dimensions, including: Effective, Ease, Emotion.
User Experience Quality: AKA UX Quality is a composite score that tracks user experience in three dimensions, including: Useful, Usable, Desirable.
System Usability Scale (SUS): Assesses product usability through a standardized questionnaire, serving as an alternative composite measure of UX Quality.
Time on Task: AKA Benchmarking tracks the time users take to successfully complete tasks, reflecting efficiency and ease of use, and serves as a KPI for performance against a standard.
Error Rate: Monitors user error frequency during interactions to identify and resolve UI Traps that hinder effective UI Design Tenets.
First Contact Resolution (FCR): Assesses the ability to resolve user issues in a single interaction, reflecting both support effectiveness and product clarity.
Churn Rate: Though often used as a business metric, high churn also reflects user experience and can signal poor user satisfaction where Experience metrics explain the “Why” of churn.
Engagement Metrics: Indicates the engagement and appeal of the user experience through metrics like session duration and frequency.
Emotional Response Metrics: Collected through surveys or sentiment analysis, or cutting-edge biometric sensors, reflecting users' feelings about their interactions with the product.
Prescriptive Emotion Metrics: Leverage advanced analytics and AI to anticipate and recommend actions based on customer emotions. By analyzing revenue, costs, and profitability across journey segments, these metrics drive automation for optimizing customer experiences for maximum business impact.
Misleading Experience Metrics
Net Promoter Score (NPS): Although NPS measures a user's willingness to recommend a product, it is not a true experience metric. Instead, it serves as a proxy indicator of overall satisfaction and loyalty. Because NPS is often tied to corporate performance and executive compensation, it is subject to ‘dark patterns’ that incentivize score manipulation.
Customer Satisfaction Score (CSAT): Evaluates user satisfaction at a higher (e.g., brand) level, frequently used to correlate customer satisfaction with revenue. Because CSAT is often tied to corporate performance and executive compensation, it is also subject to ‘dark patterns’ that incentivize score manipulation.
Customer Effort Score (CES): Measures the effort users expend to complete tasks within the product, sometimes used to rank support and friction severity issues. Because CES is often tied to Customer Service performance and executive compensation, it too is subject to ‘dark patterns’ that incentivize score manipulation.
Voice of Customer (VoC): Inflated VoC feedback scores create a false sense of satisfaction, making organizations believe their CX is better than it actually is. This false confidence leads to complacency, where critical issues are overlooked or underestimated.
OKRs: Although OKRs help align large organizations around the objectives of the business over a specified time period, it is not a true experience metric. Instead, it serves to measure whether or not the business achieved the outcomes (KR) that it seeks to achieve within a specific timeframe.
KPIs: Although KPIs typically measure or monitor an Operational Target (e.g., X % Retention, or Y % Churn), it is not uncommon for KPIs to be used for Experiential Targets (e.g., Time on Task is equal or less than X minutes, Desirability Rating is greater than Y %).
Implementing Experience Metrics
Diagnose Customer Needs: Identify the fundamental desire that customers are seeking to satisfy by getting their functional and emotional jobs done.
Codify Customer Jobs To Be Done: Uncover functional, emotional, and transactional jobs that customers are trying to get done and create a database for managing data analytics.
Define Clear Objectives: Identify what aspects of the user experience are most critical to your product and business goals. Design Thinking helps align teams around the problems they will solve together.
Select Appropriate Metrics: Choose metrics that accurately reflect the user experience and can be reliably measured and tracked over time. Set Experience metrics at the Journey-level for greatest impact.
Measure Satisfaction: Quantify how well your Experience Design is meeting your customer’s needs.
Collect Data Regularly: Use surveys, user testing, analytics tools, and AI-automated feedback mechanisms to gather continuous data at scale.
Analyze and Interpret Data: Combine quantitative and qualitative analysis to gain meaningful insights into user and customer experiences.
Act on Insights: Translate findings into actionable improvements in product design, functionality, and business impact. Align the organization around the “Why” and show impact on Financial Outcomes.
Monitor Changes Over Time: Track how experience metrics evolve in response to changes and use this data to inform future iterations or new product innovation.
Integrate Across Teams: Share experience insights with all relevant teams, including design, product, development, marketing, customer success, and support to ensure a cohesive approach to enhancing user experience.
Conclusion
Experiential Metrics are vital for optimizing customer and user interactions and perceptions, offering insights that go beyond traditional KPIs and business metrics. These metrics empower Product Leaders and Experience Designers to create more user-centric and successful products. By consistently leveraging them, organizations can enhance customer satisfaction, loyalty, and overall business success, ensuring products meet functional needs while delivering exceptional experiences.
As AI and LLM-fueled analytics advance, Experiential metrics will increasingly link customer emotions to actionable business strategies through Journey Economics and Affective Computing. Prescriptive Emotion Metrics will use AI to anticipate and recommend actions based on customer emotions; effectively optimizing, personalizing, and eventually automating experiences for maximum business impact.
In this new era of AI and Affective Computing, the evolution of Journey Analytics & Orchestration metrics and Experience Management (XM) practices is pivotal. Groundbreaking concepts like Journey Economics will transform how we use Operational and Experiential data, equipping CX Practitioners, Product Leaders, and the C-Suite with powerful tools to deliver unparalleled customer value that consistently returns value to the business.
The Future Is Here, and Experiential Metrics and Management Matter!