JTBD Metrics for Strategic Product Prioritization
Building a product that customers actually want shouldn't be a gamble. Yet most product teams continue to debate features based on intuition, competitor moves, or internal assumptions rather than hard data about customer struggle. The result? Industry research shows that 70% of product features are rarely or never used – a staggering waste of development resources that could have been avoided with better prioritization.
The solution isn't another prioritization framework to add to your collection. It's a systematic approach that quantifies exactly where customers struggle most in their journey and prioritizes solutions that deliver measurable value fastest. At thrv, we've developed a methodology that integrates Jobs to be Done (JTBD) framework with Customer Effort Score (CES) metrics to help portfolio companies build roadmaps that consistently drive adoption, reduce churn, and accelerate growth.
This guide reveals how we transform subjective feature debates into objective investment decisions using data that directly reflects customer reality. Our AI-powered JTBD analysis generates these insights in hours rather than weeks, giving our portfolio companies a critical speed advantage in identifying and addressing the customer struggles that drive measurable business results.
Table of Contents
- Understanding the Foundation: Jobs to be Done Deep Dive
- Quantifying Struggle: Using Customer Effort Score at Each Job Step
- Accelerating Impact: Measuring Implementation Speed
- The Integrated Prioritization Framework: JTBD-CES Matrix
- Advanced Implementation: Overcoming Challenges & Maximizing Impact
- Real-World Impact: Implementation Examples
- Building Your Implementation Roadmap
- FAQ: Common Questions About JTBD Metrics
Understanding the Foundation: Jobs to be Done Deep Dive
Beyond Features: Uncovering the "Job" Customers Hire Your Product For
Traditional product prioritization starts with features and works backward to customer value. This approach explains why so many features go unused – they solve problems that exist primarily in product managers' minds rather than customers' real workflows.
The Jobs to be Done framework flips this dynamic. Instead of asking "What features should we build?", JTBD asks "What job is the customer trying to accomplish?" This fundamental shift reveals opportunities that feature-focused thinking misses entirely.
Every customer job has three dimensions that we analyze systematically:
Functional Job: The practical task requiring completion. For a marketing manager, this might be "analyze campaign performance" to make budget allocation decisions.
Emotional Job: The feeling the customer wants to experience. The same marketing manager wants to feel confident presenting results to executive leadership.
Social Job: How the customer wants to be perceived by others. They want colleagues to view them as data-driven and strategic.
Understanding all three dimensions prevents the common trap of building solutions that technically work but fail to satisfy the complete customer need. When we implement our JTBD method with portfolio companies, we ensure product development addresses all three dimensions to maximize customer value creation.
Mapping the Customer's Job Journey: Steps and Desired Progress
The power of JTBD emerges when you break down high-level jobs into specific, measurable steps. A typical job map includes 8-20 discrete steps that customers follow, whether consciously or unconsciously.
Consider the marketing manager's job of "analyze campaign performance." The steps might include:
- Determine analysis objectives
- Collect performance data
- Organize data formats
- Identify performance patterns
- Calculate return on investment
- Generate actionable insights
- Create performance summaries
- Present findings to stakeholders
Each step represents an opportunity to reduce customer effort or increase success likelihood. More importantly, each step can be measured to determine where customers struggle most. At thrv, we map these job steps systematically and measure Customer Effort Score at each step to identify the highest-value improvement opportunities.
The job beneficiary (the person who benefits from job completion) and job executor (the person who performs the job) may be different individuals. Understanding both roles helps us design solutions that serve the complete customer need. The purchase decision maker represents a third critical role that influences solution selection.
Quantifying Struggle: Using Customer Effort Score at Each Job Step
CES Explained: Your Direct Line to Customer Friction
At thrv, Customer Effort Score (CES) measures the percentage of customers who report that it is difficult to satisfy a given step in their Job-to-be-Done. This difficulty is based on three measurable criteria: effort required, speed of execution, and accuracy of execution.
Unlike satisfaction surveys that capture sentiment after the fact, CES identifies friction points in real-time as customers navigate their actual job steps. A high CES indicates a significant unmet need and a valuable target for growth. When we segment markets by effort score, we isolate underserved customer segments willing to pay to get the job done better.
Our AI-powered platform significantly accelerates the process of identifying these high-CES job steps by analyzing customer interactions, support tickets, and behavioral patterns. Traditional approaches might take weeks of customer interviews and analysis. Our AI identifies where customers struggle most in hours, enabling rapid prioritization and development cycles.
Research shows CES correlates strongly with customer loyalty and churn risk. More relevant for product prioritization, CES pinpoints exactly where in a workflow customers experience the most friction. Good CES scores typically show less than 20% of customers reporting difficulty. Scores above 30% indicate problematic effort levels that warrant immediate attention. Scores above 50% represent critical friction points that often drive customer churn.
Integrating CES into Your Job Map: Pinpointing High-Effort Hotspots
The breakthrough comes when you measure CES at each step of your customer's job journey rather than treating it as a general product metric. This granular approach reveals specific friction points that aggregate scores miss.
Returning to our marketing manager example, measuring CES at each job step might reveal:
- Determine analysis objectives (CES: 12%) - Low difficulty, customers know what they want to measure
- Collect performance data (CES: 58%) - High difficulty due to multiple platform logins and export processes
- Organize data formats (CES: 67%) - Very high difficulty because of inconsistent data formats
- Identify performance patterns (CES: 38%) - Moderate difficulty but manageable with current tools
- Calculate return on investment (CES: 52%) - High difficulty due to complex attribution modeling
- Generate actionable insights (CES: 28%) - Moderate difficulty, experience helps here
- Create performance summaries (CES: 41%) - Moderate difficulty but time-consuming
- Present findings to stakeholders (CES: 18%) - Low difficulty once summary is complete
This data immediately highlights the most problematic steps: collecting performance data (58% report difficulty) and organizing data formats (67% report difficulty). These become prime targets for product investment because they represent where the largest percentage of customers struggle to make progress on their job.
Interpreting CES Data for Action: What Your Scores Really Mean
High CES scores don't automatically mean you should solve every friction point. Strategic prioritization requires understanding why scores are high and what improvement would mean for customer success.
CES 0-20% (Low Difficulty): These steps work well for customers. Monitor but don't prioritize major changes unless strategic business needs require modification.
CES 21-35% (Moderate Difficulty): Acceptable effort levels but watch for trends. Small improvements might have disproportionate impact if the step is critical to job success.
CES 36-50% (High Difficulty): Strong candidates for product investment. Customers complete these steps but experience notable friction that could drive them to alternative solutions.
CES 51-100% (Very High Difficulty): Top priority for immediate attention. Customers may abandon the job entirely or complete it inadequately, leading to poor outcomes and eventual churn.
The key insight from our work with portfolio companies: CES scores become actionable when combined with job step importance and impact on overall job success. Our AI-driven method eliminates guesswork and aligns every initiative with measurable growth objectives by systematically identifying which high-CES job steps offer the greatest value creation potential.
Accelerating Impact: Measuring Implementation Speed
Understanding Development Velocity for Customer Value
Traditional product metrics focus on what you build or how customers respond after release. At thrv, we also measure how quickly our portfolio companies can identify, validate, and address the most significant customer struggles. This speed creates competitive advantage.
Measuring implementation speed requires tracking time from struggle identification to measurable improvement. Here's how leading product teams structure this measurement:
Phase 1: Detection (Target: 30 days)
- Time from customer struggle emergence to CES measurement showing problematic difficulty levels
- Includes customer research, survey deployment, and data analysis
Phase 2: Validation (Target: 45 days)
- Time from struggle identification to confirmed improvement hypothesis
- Includes solution ideation, prototype testing, and business case development
Phase 3: Resolution (Target: 90 days)
- Time from validated solution to measurable CES improvement in production
- Includes development, deployment, and follow-up measurement
A high-performing cycle shows consistent 165-day timeframes from struggle detection to validated improvement. Organizations taking 300+ days per cycle typically lose customers to competitors who solve problems faster.
Our AI-powered analysis helps accelerate the detection phase dramatically. What might take weeks of manual analysis happens in hours, giving our portfolio companies critical speed advantages in addressing customer struggles before competitors recognize the opportunities.
The Integrated Prioritization Framework: JTBD-CES Matrix
Building Your Strategic Decision Engine
Most prioritization frameworks evaluate features in isolation. At thrv, our integrated JTBD-CES approach evaluates opportunities within the context of complete customer workflows and organizational capabilities.
The framework combines five key variables:
Job Step Importance: How critical this step is to overall job success (1-10 scale) Current CES: Measured difficulty percentage for this step (0-100%) Effort Reduction Potential: Expected CES improvement from intervention (percentage points) Implementation Speed: Time from solution implementation to measured improvement (days) Development Effort: Resources required for implementation (story points, weeks, or investment level)
The Prioritization Matrix in Action
Consider a hypothetical project management platform evaluating features for the customer job "coordinate team project delivery." Here's how the matrix reveals priorities:
The matrix immediately highlights that real-time status dashboards address the highest customer difficulty with strong reduction potential and reasonable implementation speed. Advanced reporting, despite addressing significant difficulty, drops in priority due to longer implementation time and higher development cost.
Decision-Making Criteria for Maximum Impact
The integrated framework supports three critical prioritization decisions:
Feature Backlog Triage: Rank development opportunities by their combined impact on customer effort reduction, implementation speed, and development efficiency. Features scoring above 8.0 typically become immediate priorities, while scores below 6.0 require reassessment or delayed implementation.
Progress Sizing: Calculate potential impact by multiplying effort reduction percentage by affected customer volume and average customer value. This reveals which improvements could drive the most significant business results.
Risk-Adjusted Investment: Weight priority scores by implementation uncertainty and competitive threat level. High-certainty improvements addressing competitive vulnerabilities receive additional priority weighting.
The framework particularly excels at identifying "quick win" opportunities that traditional methods miss – moderate effort reductions with very fast implementation that compound into significant competitive advantages.
Advanced Implementation: Overcoming Challenges & Maximizing Impact
Gaining Executive Buy-in: Transforming Product Conversations
Executive teams understand business metrics better than product metrics. Our JTBD-CES framework translates product decisions into language executives recognize: reduced churn risk, faster customer onboarding, and measurable efficiency improvements.
We present prioritization decisions using business impact metrics:
Customer Retention Impact: "Reducing CES from 67% to 28% for data export workflows decreased churn probability by 23% in our portfolio company implementations"
Revenue Acceleration: "Features addressing high-difficulty job steps generated 30% more new bookings when customers could complete critical workflows with significantly less friction"
Market Differentiation: "Our 165-day implementation cycle vs. industry average of 280 days means we solve customer problems twice as fast as competitors"
This approach shifts conversations from feature debates to investment decisions based on measurable customer progress.
Fostering Cross-Functional Alignment: Creating Shared Language
Different teams often prioritize different aspects of product development. Sales focuses on competitive differentiation, marketing emphasizes customer acquisition, support wants fewer tickets, and engineering prefers technical elegance.
Our integrated framework creates alignment by focusing everyone on the same objective: reducing customer difficulty in completing valuable jobs. When sales requests a competitive feature, evaluate it within the customer's job context. When support identifies a problem area, measure it using CES to determine true priority relative to other friction points.
Teams aligned around customer job success make more consistent decisions and waste less time on internal priority conflicts. When we implement our JTBD method with portfolio companies, we establish this shared language across all functions to accelerate execution and create equity value through systematic improvement.
Avoiding Common Implementation Pitfalls
Pitfall 1: Measuring CES too broadly Avoid generic "How easy was it to use our product?" surveys. Measure difficulty at specific job steps to generate actionable insights about where customers struggle to make progress.
Pitfall 2: Ignoring job sequence dependencies Some high-difficulty steps must be completed before low-difficulty steps become possible. Map dependencies to avoid optimizing steps that customers can't reach efficiently.
Pitfall 3: Confusing customer satisfaction with effort Customers might feel satisfied despite high difficulty if they achieve important progress. Focus CES measurement on difficulty levels based on effort, speed, and accuracy rather than satisfaction levels.
Pitfall 4: Underestimating measurement complexity CES measurement requires systematic tracking across multiple customer touchpoints. Implement tracking systems before beginning CES-based prioritization. Our AI-powered platform handles this complexity by automatically analyzing customer interactions to identify struggle patterns.
Real-World Impact: Implementation Examples
Example 1: B2B SaaS Platform Reduces Churn Through Job Step Optimization
Consider a hypothetical customer relationship management platform serving mid-market sales teams that noticed increasing churn despite strong feature adoption rates. Traditional product metrics suggested customers were successfully using the platform, but retention continued declining.
The team implemented JTBD-CES measurement for the core customer job: "manage sales pipeline." CES measurement across eight job steps revealed that "update deal status information" showed 58% of customers reporting difficulty—indicating very high effort for a frequently completed task.
Further research showed that updating deal status required navigating between multiple screens, manually entering repetitive information, and checking data accuracy across different system sections. The high difficulty created a negative feedback loop where sales reps avoided updates until absolutely necessary, reducing data quality and pipeline visibility.
The product team prioritized a streamlined status update workflow with contextual data validation and bulk editing capabilities. Implementation took 12 weeks and reduced CES for this job step from 58% to 22%.
Hypothetical results after six months:
- 15% reduction in customer churn
- 34% increase in daily active usage for pipeline features
- 28% improvement in data quality scores
- Sales rep satisfaction increased from 6.2 to 8.1 out of 10
The key insight: traditional product metrics missed the difficulty-driven dissatisfaction that eventually drove churn decisions.
Example 2: E-commerce Platform Improves Win Rates Through Pre-Purchase Job Optimization
Consider a hypothetical enterprise e-commerce platform competing against established players by promising easier implementation and ongoing management. Despite this positioning, new customer acquisition remained challenging, with long sales cycles and frequent prospect concerns about switching costs.
The team mapped the customer job "evaluate and implement new e-commerce platform" and measured CES at each evaluation step. The highest difficulty scores appeared in "assess integration requirements" (CES: 64%) and "estimate implementation timeline" (CES: 61%).
Traditional competitive analysis focused on feature comparisons, but JTBD-CES data revealed that prospects struggled most with understanding how the platform would work within their existing technology environment. This struggle occurred before detailed feature evaluation even began.
The product team developed an automated integration assessment tool that analyzed prospects' current technology stack and generated customized implementation plans with timeline estimates. The tool reduced CES for both problematic job steps to under 25%.
Hypothetical results after 12 months:
- 30% of new bookings directly attributed to prospects who used the assessment tool
- Average sales cycle decreased from 186 days to 134 days
- Win rate against primary competitor increased from 23% to 41%
- Customer implementation satisfaction increased from 7.1 to 8.9 out of 10
The breakthrough came from optimizing the customer's pre-purchase job rather than focusing exclusively on post-purchase product capabilities.
Building Your Implementation Roadmap
Phase 1: Foundation (Weeks 1-4)
Week 1-2: Map Your Core Customer Job Identify the primary job your best customers hire your product to complete. Break this job into 8-15 discrete steps that customers follow regardless of their specific industry or use case. Identify the job beneficiary, job executor, and purchase decision maker for each customer segment.
Week 3-4: Deploy Initial CES Measurement Create targeted CES surveys for each job step that measure the percentage of customers reporting difficulty based on effort, speed, and accuracy. Deploy these through in-app prompts, email campaigns, or customer success interactions to gather baseline difficulty measurements.
Phase 2: Data Collection (Weeks 5-8)
Week 5-6: Analyze CES Distribution Identify job steps with CES scores above 35%. These become your initial improvement candidates representing where the largest percentage of customers struggle.
Week 7-8: Validate High-Difficulty Steps Conduct follow-up interviews with customers who reported difficulty to understand specific frustration points and validate potential solutions.
Phase 3: Prioritization Framework (Weeks 9-12)
Week 9-10: Build Your Prioritization Matrix Create a scoring system that combines CES levels, improvement potential, development effort, and estimated implementation speed for addressing each high-difficulty job step.
Week 11-12: Generate Initial Roadmap Use the matrix to identify top 3-5 improvement opportunities and sequence them based on dependency requirements and resource availability.
Phase 4: Implementation and Measurement (Ongoing)
Begin development on highest-priority improvements while continuing CES measurement to track progress and identify new friction points as they emerge.
Successful implementation typically shows measurable CES improvements within 90 days of deploying solutions, with broader business impact metrics following within 6-12 months.
At thrv, our AI-powered platform accelerates this entire process by automatically identifying high-CES job steps from customer interaction data, generating solution hypotheses, and predicting implementation impact. This gives our portfolio companies the speed advantage needed to create equity value through systematic product innovation.
FAQ: Common Questions About JTBD Metrics
What is the difference between JTBD and traditional user personas?
Traditional personas describe who your customers are (demographics, roles, characteristics), while JTBD describes what customers are trying to accomplish (jobs, progress, success criteria). JTBD provides more actionable insights for product prioritization because it focuses on customer motivations rather than customer attributes. At thrv, we focus on the job beneficiary (who benefits from job completion) and job executor (who performs the job) rather than demographic personas.
How often should we measure CES for each job step?
Measure CES continuously for core job steps that directly impact customer success. For critical workflows, implement always-on measurement through in-app surveys or regular customer outreach. For secondary job steps, quarterly or semi-annual measurement typically provides sufficient insight for prioritization decisions.
What sample size do we need for reliable CES data?
For statistically significant CES measurements, aim for minimum 100 responses per job step. However, strong directional insights often emerge with 30-50 responses, especially when combined with qualitative feedback from customer interviews.
How do we handle customers who use our product for multiple different jobs?
Prioritize measurement around the job that drives the most customer value or represents the largest revenue opportunity. Once you've optimized the primary job workflow, expand measurement to secondary jobs. Attempting to optimize multiple jobs simultaneously often dilutes focus and slows improvement speed.
What's the relationship between CES and customer satisfaction (CSAT) scores?
CES measures difficulty while CSAT measures satisfaction. Customers might report high satisfaction despite high difficulty if they achieve important progress. Conversely, customers might report low satisfaction despite low difficulty if the progress doesn't meet expectations. CES provides better prioritization insight because reducing difficulty almost always improves customer experience.
What if CES scores don't align with customer complaints or support tickets?
CES often reveals friction points that customers haven't explicitly complained about because they've adapted to high-difficulty workflows or don't recognize alternatives exist. Use CES to proactively address issues before they become complaint-worthy problems. Our AI-powered analysis helps identify these hidden struggle points systematically.
How do we get customers to respond to CES surveys about specific job steps?
Trigger surveys immediately after customers complete relevant job steps, when the difficulty level is fresh in their memory. Keep surveys short (1-2 questions), explain how responses improve their experience, and share improvement results with participants to encourage ongoing participation.
Can smaller product teams benefit from this integrated approach?
Absolutely. Smaller teams often benefit more because they must prioritize ruthlessly due to resource constraints. The framework helps small teams focus on changes that deliver maximum customer impact with available development capacity.
What happens when different customer segments show different CES scores for the same job step?
Segment-specific CES differences often reveal important prioritization insights. If enterprise customers show higher difficulty scores than SMB customers for the same job step, this might indicate scalability problems that become more acute with usage volume. Prioritize solutions that address the most valuable customer segments first.
Our integrated JTBD-CES approach transforms product prioritization from opinion-driven debates into data-driven investment decisions. By systematically measuring where customers struggle most and optimizing your ability to address those struggles quickly, you build products that customers genuinely value while maximizing your development team's impact on business results.
At thrv, we've developed this methodology through our work with portfolio companies to create equity value through product innovation. Our proprietary JTBD method combined with AI-powered analysis provides the framework for translating customer struggles into requirements that drive measurable business results and accelerate growth.
Start with mapping one core customer job, measuring CES across its key steps, and identifying your first high-impact improvement opportunity. The framework's power compounds as you build organizational capability to detect, validate, and resolve customer struggles faster than competitors.
Posted by thrv