At thrv, we transform how our portfolio companies build, market, and sell products to create superior equity returns for our investors. We use our proprietary and patented Jobs to be Done (JTBD) method to replace vague assumptions with measurable understanding of what customers need to accomplish—regardless of the product.
Today, companies face tight timelines and rapidly shifting customer expectations. CEOs must deliver better results with clear evidence that roadmap decisions directly contribute to accelerated growth and equity value creation.
Basic JTBD principles alone aren't enough. That's why we've developed a proprietary method that consistently creates superior equity returns.
Our platform enables us to:
This approach is how we create equity value faster with less risk. Our portfolio companies achieve growth through strategies that target the most underserved segments willing to pay to get their jobs done better.
When Target's Registry team used our JTBD method, they reversed declining revenue trends and achieved over 25% top-line growth annually within 12-18 months. They also saw a 20% increase in Net Promoter Score and transformed from a market follower to a leader, with Amazon beginning to replicate features that Target had introduced.
Job identification is the foundation of our JTBD method—but traditional customer interviews are slow, expensive, and difficult to scale.
We implement hybrid research models that combine qualitative insight with behavioral data to uncover customer jobs more efficiently and objectively.
Instead of relying only on direct interviews, we place researchers into customer workflows—via product analytics, support channels, or even co-browsing sessions. This real-world approach reveals unspoken needs: recurring frustrations, workarounds, and steps customers skip or repeat.
Our JTBD research includes analyzing search intent and product usage logs to identify customer jobs. By examining patterns in high-volume search queries, we determine the goals behind the keywords. When combined with product analytics, these signals become powerful predictors of jobs worth addressing. We map search terms directly to job steps, creating a comprehensive view of which job steps are most critical to job beneficiaries at different stages of their journey.
We use AI to group thousands of support tickets or user reviews into job-based themes. Our tools integrate with support systems to transform this data directly into structured job statements, creating a living map of customer pain and intent.
This approach scales JTBD research exponentially. Rather than conducting dozens of interviews, product teams can analyze thousands of customer interactions, turning what was once anecdotal into statistically significant patterns.
Quantification is where JTBD becomes operational. Without measurable job steps, product teams revert to intuition, internal opinions, and reactive roadmaps.
We use quantitative job scoring to prioritize features based on customer value—before a single line of code is written.
A good job statement follows our direct action/variable structure:
The specificity of these statements is crucial. Rather than vague goals, precise job statements create a measurable target. They eliminate subjective interpretations and establish a shared understanding across product, marketing, and sales teams.
Using our survey-based JTBD methods, we measure Customer Effort Scores (CES) for each job step. CES represents the percentage of customers who report that it is difficult to satisfy a given step in their Job to be Done, based on effort required, speed of execution, and accuracy of execution.
High Customer Effort Scores indicate significant unmet needs and valuable targets for growth. This creates a quantitative foundation that allows our portfolio companies to move beyond the "loudest voice in the room" problem. Instead of prioritizing features based on who argues most persuasively, teams can point to data showing which job steps create the most customer struggle.
Quantifying job steps at a high level is useful—but segmentation unlocks even more precision. We create job maps by role, by workflow, or by context. This reveals underserved needs specific to each segment, enabling personalized product strategies.
This entire process is built into our platform—making it easy to quantify, prioritize, and visualize customer jobs for stakeholders across product, marketing, and sales.
We use AI to reshape JTBD workflows by accelerating identification, improving pattern recognition, and automating analysis at scale.
Here are the most impactful ways we're using AI with our portfolio companies:
Our AI platform analyzes interview transcripts, reviews, support tickets, and product usage data to identify unmet needs in three key ways:
The real power comes from processing volume. Where traditional methods might analyze 10-20 interviews, our AI-assisted approaches extract job steps from hundreds of sources, ensuring a more complete view of customer needs.
Our AI technology doesn't replace JTBD—it supercharges it. Portfolio companies that adopt our AI-powered research not only move faster but uncover patterns humans often miss.
Many teams struggle to translate JTBD insights into everyday execution. They treat job maps as static documents—when in fact, they should be the foundation of agile planning.
Here's how we bridge the strategy-execution gap:
Every backlog item addressed in an agile sprint should tie directly to a job step. For example:
As a user trying to submit expense reports
I want to auto-categorize receipts
So I can complete monthly reporting faster
This ensures every sprint output aligns with measurable customer value—not internal feature ideas.
With our quantified jobs method, product owners can use Customer Effort Scores to prioritize sprints. A feature that addresses a high Customer Effort Score takes precedence—even if it's less glamorous or harder to build.
This approach transforms sprint planning discussions. Instead of debating which features seem most important based on internal opinions, teams can focus on objective data about customer needs.
We redefine "done" to include job validation. A feature isn't complete until it measurably improves the Customer Effort Score of the job step it was designed to impact.
Our JTBD method becomes the connection between strategy and delivery—grounding every team decision in real customer jobs.
OKRs often fail when they're disconnected from real customer needs. Our JTBD method fixes this by anchoring both objectives and key results to customer jobs.
Here's an example of JTBD-aligned OKRs:
Objective: Improve the experience of onboarding new customers
KR1: Reduce Customer Effort Score for "Understand setup requirements" from 67% to 35%
KR2: Reduce average onboarding time from 5 days to 2 days
This moves the conversation away from vanity metrics and toward evidence that you're helping customers get their job done faster and more accurately.
Our JTBD method also supports cascading OKRs:
With this structure, the entire organization is aligned around value creation—not just activity.
We've seen that early-stage customer development often fails due to vague insights and feature-led interviews.
Our JTBD method offers a structured approach that transforms go-to-market execution:
This reframing is particularly valuable for early-stage portfolio companies. Instead of prematurely defining who their customers are based on demographics, they identify which jobs they can help with—then find job beneficiaries struggling with those jobs.
Our JTBD method also defines the early product vision. Instead of building an MVP around functionality, you build around a small, high-priority subset of job steps—and expand from there.
Our JTBD method isn't limited to product teams. Here's how we're expanding its application:
Sales teams use job statements to reframe messaging: "We help procurement teams evaluate vendors faster" is far more compelling than "Our software automates forms."
This job-centered messaging resonates because it speaks directly to what customers care about—the progress they're trying to make, not the features they're trying to buy.
Content marketers align content to specific job steps, creating more relevant assets that naturally attract prospects struggling with specific aspects of a job.
Instead of pricing by feature, our portfolio companies package solutions around job segments. Rather than forcing customers to translate features into benefits, packages directly address the progress they're trying to make.
Our portfolio companies use JTBD health scores, tracking Customer Effort Scores across key job steps post-sale. This identifies risk areas early and turns onboarding into a strategic advantage.
By monitoring how well customers are able to achieve their key job steps—rather than just tracking feature adoption—success teams can identify at-risk accounts before traditional metrics show any problems.
Our JTBD method is evolving from a research methodology into a company-wide operating system for equity value creation through our JTBD OS technology platform.
The thrv platform is a modern approach to software that uses new technologies to speed up product development and uses artificial intelligence for both our portfolio companies' products—by implementing AI algorithms to help get the customer's job done faster and more accurately—and our portfolio companies' engineering processes by using AI tools to help write software faster and more accurately.
The goal of our platform is to help our portfolio companies transition from legacy, outdated software and processes to state-of-the-art, AI-assisted products and product development. The platform focuses on increasing the return on investment from engineering investments by reallocating resources from infrastructure maintenance to customer-facing capabilities that directly solve customers' Jobs to be Done.
Using AI in this manner yields dramatically faster time to market for new capabilities. Our Tiger Team uses the thrv platform to develop new product features and to focus as much of the engineering effort as possible on the most valuable part of the solution for customers: the algorithms that plan, assess, and revise the metrics in the job.
This represents a fundamental shift in how our portfolio companies identify and deliver value—implementing JTBD at scale, across functions, and at the speed of modern business to create superior equity returns.
Our Jobs to be Done method has always focused on customer value. But to create superior equity returns in today's environment, value needs to be identified faster, measured more rigorously, and delivered more consistently across the entire product lifecycle.
For Limited Partners seeking superior equity returns, our JTBD method offers a practical path to:
As markets become more competitive and customer expectations continue to rise, the companies that win will be those that most effectively identify and address the jobs their customers need done. Our proprietary JTBD method provides the structure and tools to do exactly that—turning customer insights into measurable business results.
At thrv, we don't just advise—we execute. Our proprietary JTBD method and AI-powered platform transform how our portfolio companies create equity value through product innovation. Our AI capabilities accelerate this process, helping our teams generate insights in hours rather than weeks, giving our portfolio companies a critical speed advantage when developing new products that satisfy customer needs faster and more accurately.
For Limited Partners seeking superior returns, our operating approach delivers accelerated growth, multiple expansion, and reduced investment risk through systematic application of our JTBD method across every aspect of business operations.