Product roadmaps are high-stakes documents, especially within the fast-paced environment of private equity-backed technology companies. The pressure to generate growth and create equity value is immense, yet a staggering 64% of software projects fail due to poor requirements management, according to Aqua Cloud. Traditional prioritization methods often fall short, relying on subjective inputs, internal politics, or the "loudest voice" in the room. This leads to misallocated resources and features that fail to move the needle on revenue or customer adoption.

At thrv, we use our proprietary and patented Jobs to be Done (JTBD) method to create a more precise, data-driven path forward. By focusing on a single, powerful metric—customer effort—and using Artificial Intelligence to measure it at scale, we help our portfolio companies build roadmaps that create predictable growth. This guide explains how we use an AI-driven approach to feature prioritization to identify the most significant growth opportunities and build durable equity value in our portfolio companies.

Table of Contents

• The Flaws in Traditional Prioritization Frameworks
• Why Customer Effort is the Key to Creating Value
• How Our AI Identifies and Measures Customer Effort
• Analyzing Qualitative Data with Natural Language Processing
• Understanding User Behavior with Predictive Analytics
• The Power of Our Unified Data Model
• Our AI-Powered Value vs. Development Effort Matrix
• Our 5-Step Framework for AI-Driven Prioritization
• Case Study: How We Reduced Churn with AI-Driven Prioritization
• The Future of Product Management is AI-Powered
• Frequently Asked Questions (FAQ)

The Flaws in Traditional Prioritization Frameworks

For years, product leaders have relied on frameworks like RICE (Reach, Impact, Confidence, Effort) and MoSCoW (Must-have, Should-have, Could-have, Won't-have) to plan their roadmaps. While useful for organizing ideas, these methods share fundamental weaknesses:

• Subjectivity: "Impact" and "Confidence" scores are often educated guesses, easily influenced by internal bias
• Lack of Real-Time Data: These frameworks are static and struggle to incorporate the continuous stream of data from customer feedback and user behavior
• Inability to Scale: Manually applying these frameworks to hundreds of potential features is inefficient and prone to error
  
  

The most significant flaw is that they do not systematically connect feature development to the primary driver of customer value: making progress in their lives. They fail to answer the most important question: What part of getting a job done is a customer struggling with so much that they are willing to pay for a better solution?

Why Customer Effort is the Key to Creating Value

Our JTBD product management approach is built on a more reliable foundation: the Customer Effort Score (CES). The CES is the percentage of customers who report that a specific step in their Job-to-be-Done is difficult. Difficulty is measured objectively by the speed and accuracy required to complete the action.

The connection to equity value is direct and powerful:

• High Customer Effort indicates significant struggle
• Struggle creates a strong willingness to pay for a new solution
• A new solution that reduces effort drives product adoption, market share, and pricing power
• Adoption, share, and pricing power are the fundamental drivers of revenue growth and higher valuation multiples
  
  

Think of customer effort as friction in a system. By pinpointing and removing the points of greatest friction, we accelerate growth for our portfolio companies and build products that customers adopt quickly and are willing to pay more for.

How Our AI Identifies and Measures Customer Effort

Manually calculating a Customer Effort Score across an entire customer base and every step of their job is a monumental task. This is where our AI-powered platform becomes a critical accelerator for our portfolio companies. It systematically processes vast amounts of customer data to generate objective CES data.

Analyzing Qualitative Data with Natural Language Processing

Customers constantly provide feedback through support tickets, online reviews, and survey responses. Our platform's NLP engines analyze this unstructured text to identify specific unmet customer needs. Our AI is trained to recognize the core action and variable in customer statements, such as the need to "determine shipping status" or "calculate invoice amount." It then connects these needs to steps in the customer's job and measures the associated effort expressed in the feedback.

Understanding User Behavior with Predictive Analytics

Actions speak louder than words. Our platform's AI models analyze product usage data to spot behavioral patterns that indicate struggle. These can include:

• Excessive time spent on a single task
• Repeated or erratic clicks ("rage clicks")
• High rates of abandoning a process before completion
  
  

These behaviors are mapped to specific job steps, providing a quantitative measure of effort without ever having to ask the customer a question.

The Power of Our Unified Data Model

The true power of our approach comes from integrating these different data streams. Our platform combines the qualitative insights from NLP with the quantitative findings from behavioral analytics. This creates a unified data model that generates a single, highly reliable Customer Effort Score for every step in the customer's job. This gives our portfolio company CEOs a clear, ranked list of their customers' unmet needs.

Our AI-Powered Value vs. Development Effort Matrix

With a ranked list of unmet needs, we help product teams make data-backed investment decisions. We use an AI-powered matrix that plots Customer Effort Score against Development Effort.

• Customer Value (Y-axis): Represented by the Customer Effort Score. A high CES means solving this problem will create significant value for the customer and, therefore, the business
• Development Effort (X-axis): An estimation of the time and resources required to build a feature that addresses the unmet need
  
  

This matrix provides a clear guide for capital allocation. Features in the top-left quadrant—addressing high customer effort with low development effort—are the quick wins that can immediately improve the customer experience and business results. Features in the top-right—high customer effort and high development effort—are strategic initiatives that can create a durable competitive advantage. This framework is a core part of our equity value creation platform.

Our 5-Step Framework for AI-Driven Prioritization

We implement a disciplined, repeatable process within our portfolio companies to align product development with the highest-impact growth opportunities.

1. Define the Customer's Job-to-be-Done: We start by working with teams to build a complete map of every step the customer takes to achieve their primary goal.
2. Consolidate Customer Data: We connect all sources of customer data—support tickets, reviews, product analytics—into our JTBD platform.
3. Generate Customer Effort Scores with AI: Our AI engine analyzes the consolidated data to assign a CES to each job step, instantly revealing where customers struggle the most.
4. Translate High-Effort Needs into Product Features: We work with product teams to design features that directly reduce the effort of the job steps with the highest scores.
5. Build a High-Growth Product Roadmap: We help teams prioritize these features on their roadmap, ensuring development resources are focused on work that will create the most equity value.

Case Study: How We Reduced Churn with AI-Driven Prioritization

When we used our JTBD method for one of our portfolio companies, a B2B SaaS firm experiencing flat growth and increasing customer churn, we discovered their product roadmap was filled with feature requests from their largest customers, but these additions were not improving the company's key metrics.

Upon implementing our platform, our AI analysis revealed a very high Customer Effort Score of 45% on a single job step: "prepare monthly performance report." Customers found the existing process slow and error-prone. Armed with this data, we worked with the product team to prioritize the development of a new automated reporting module.

The results were transformative. The CES for that job step dropped to below 5%. Within six months, the company saw a 15% reduction in customer churn and a significant increase in new customer acquisition, contributing to a higher valuation multiple at exit. This is a clear illustration of how our Jobs-to-be-Done software helps our portfolio companies accelerate growth.

The Future of Product Management is AI-Powered

The era of building products based on intuition and guesswork is over. As 83% of companies state that AI is a top business priority, the teams that succeed will be those that use it to gain a deeper, more accurate understanding of their customers' needs.

By shifting the focus from subjective opinions to objective data on customer effort, we help our portfolio companies de-risk their product investments and accelerate growth. Our AI-driven, JTBD-based approach to feature prioritization is not just a better way to build products—it is a superior method for creating equity value.

Learn more about our process for creating equity value or see how our Jobs-to-be-Done software works.

Frequently Asked Questions (FAQ)

What is AI-driven product feature prioritization?

AI-driven product feature prioritization is the method of using artificial intelligence to analyze customer data, identify unmet needs, and rank potential features based on their potential to create customer and business value. At thrv, our method focuses specifically on using AI to measure Customer Effort Scores to guide these decisions.

How can AI improve product management?

AI improves product management by replacing subjective guesswork with objective, data-driven insights. It automates the analysis of vast amounts of customer feedback and user behavior, allowing product teams to identify the most pressing customer problems with speed and accuracy. This aligns product, marketing, and sales teams around a common goal and accelerates value creation.

What types of data does AI use to prioritize features?

Our AI platform uses a combination of qualitative and quantitative data. This includes unstructured data like support tickets, sales call transcripts, and online reviews, as well as structured data like product usage analytics and user behavior patterns.

Is an AI-driven approach suitable for all product teams?

Yes. Our AI-driven approach is valuable for product teams of any size. For our portfolio companies, our platform makes this powerful analysis accessible and scalable, giving them a significant competitive advantage without requiring a dedicated internal data science team.

What are the risks of using AI for product planning?

The greatest risk is not using a data-driven approach to product planning. Relying on intuition or flawed frameworks is a primary reason that so many technology products fail to meet expectations. Our AI-driven system, guided by our sound JTBD methodology, removes bias and dramatically increases the probability of success.

How does Customer Effort Score differ from traditional metrics?

Customer Effort Score measures the difficulty customers experience when trying to complete specific steps in their job-to-be-done. Unlike traditional metrics that focus on features or subjective opinions, CES provides objective data on where customers struggle most, directly connecting to their willingness to pay for better solutions.

How quickly can AI identify high-priority features?

Our AI platform can analyze customer data and generate prioritized feature recommendations in hours rather than weeks. This gives our portfolio companies a critical speed advantage when developing new products that address customer needs faster and more accurately than competitors.

What makes thrv's AI approach different from other product management tools?

Our AI platform is built specifically around the Jobs-to-be-Done methodology, focusing on customer effort rather than feature requests or internal opinions. This creates a direct connection between product decisions and equity value creation, helping our portfolio companies build competitive advantages that drive measurable business results.