In today's dynamic business landscape, identifying genuine high-growth opportunities requires more than traditional market segmentation. Static demographics and broad behavioral clusters often obscure the nuanced needs of potential customers. We recognize that true market potential is found by understanding the specific Jobs to be Done (JTBD) by customers and the effort they expend. This article explores how AI-enhanced market segmentation, powered by JTBD insights, is our roadmap to finding and capitalizing on these high-growth avenues for our portfolio companies.
Table of Contents
The Evolution: From Traditional to AI-Powered Segmentation
Traditional market segmentation methods, while foundational, often struggle with the scale, speed, and complexity of modern data. They can produce segments that are too broad or based on lagging indicators, making it difficult to proactively identify emerging opportunities.
AI transforms this landscape. It allows for:
- Scalability: Processing vast datasets far beyond human capacity
- Dynamic Analysis: Adapting segments in near real-time as customer behaviors and market conditions shift
- Predictive Power: Forecasting future trends and segment growth with greater accuracy
The global artificial intelligence in marketing market, valued at USD 20.44 billion in 2024, is projected to reach USD 82.23 billion by 2030, a CAGR of 25.0% (Grand View Research). This underscores the significant shift towards AI-driven strategies. Our AI-powered platform is central to how we enable product teams in our portfolio companies to move faster and with less risk.
How AI Elevates Core Segmentation Techniques
AI doesn't just replace traditional techniques; it amplifies their effectiveness, providing deeper, more actionable insights.
AI & Behavioral Segmentation: Decoding Customer Intent
Behavioral segmentation aims to group customers based on their actions, usage patterns, and interactions. Its goal includes understanding how to address particular needs and tailor products. AI takes this further:
- Deep Pattern Recognition: Our AI algorithms analyze complex behavioral patterns, moving beyond simple metrics like purchase history to understand the why behind actions. For instance, AI can help determine the sequence of actions leading to a high-value conversion.
- Predicting Future Needs: By analyzing past behaviors, AI can help forecast future customer actions and needs, allowing our portfolio companies to anticipate demand. Amazon's recommendation engine, responsible for a significant portion of its sales, illustrates the power of predictive behavioral analysis.
- Personalizing Experiences: AI enables the creation of highly personalized experiences that resonate with specific behavioral segments, improving engagement and reducing customer effort.
AI & Volume Segmentation: Finding Your Most Valuable Prospects
Volume segmentation categorizes customers by their transaction frequency or value. AI enhances this by:
- Understanding Purchase Drivers: Our systems go beyond simple counts to help identify the core drivers behind high-volume purchases or engagement
- Predicting Lifetime Value (LTV): AI models can more accurately calculate potential LTV, enabling our portfolio companies to focus resources on segments with the highest long-term growth potential
The Critical Role of Jobs-To-Be-Done (JTBD) in AI Segmentation
We use our proprietary and patented Jobs to be Done (JTBD) method as the foundation of our approach. We understand that customers "hire" products or services to get a specific "job" done. Understanding this job is key to unlocking value.
What is JTBD in Market Segmentation?
JTBD provides a lens to segment markets based on the underlying functional and emotional goals customers are trying to achieve, rather than just who they are or what they've bought. When combined with AI, JTBD allows for a highly precise and needs-based segmentation. The focus shifts from demographic profiles to shared, unmet needs in getting a job done.
Using AI to Find Unarticulated Customer Needs
Many critical customer needs are unarticulated. AI excels at uncovering these:
- Analyzing Unstructured Data: Our AI platform uses Natural Language Processing (NLP) to analyze customer feedback from reviews, support tickets, and social media. This helps identify recurring pain points or desired outcomes related to specific job steps. For example, AI can help determine customer sentiment towards current solutions for a job.
- Pattern Recognition in Usage Data: AI can identify patterns in product usage data that indicate where customers struggle or are using workarounds—hallmarks of unmet needs and high customer effort. For example, if many users repeatedly attempt to locate specific information within an application and fail, this signals a high-effort job step.
By focusing on JTBD, our AI-driven segmentation helps portfolio companies identify underserved customer segments whose primary job is not being adequately addressed. These segments often represent significant growth opportunities.
AI in Action: Technologies for Identifying High-Growth Segments
Our AI-powered platform employs several key technologies to identify and define high-growth market segments for our portfolio companies.
Key AI Technologies We Use
- Machine Learning Clustering: Algorithms automatically group customers based on similarities in their job execution, needs, and behaviors, often revealing non-obvious segments or "white space" in the market. This helps identify distinct customer groups with shared unmet needs.
- Predictive Analytics: We use predictive models to forecast the growth trajectory of identified segments and their potential value, allowing for data-driven prioritization.
- Natural Language Processing (NLP): Essential for extracting JTBD insights from text data, NLP helps us determine the precise language customers use to describe their needs and struggles. This is crucial for defining customer need statements such as "calculate the total cost" or "ensure data accuracy."
- Generative AI: We use Generative AI to help formulate initial hypotheses about segment needs and to draft tailored value propositions quickly, accelerating the innovation cycle.
Strategic Approaches to Uncover Growth
- Finding Adjacent Market Opportunities: AI can analyze patterns to suggest new verticals or customer types who have similar Jobs to be Done that current offerings could address with minor modifications
- Uncovering Underserved Segments: Our focus is on identifying segments where current solutions result in high Customer Effort Scores for critical job steps
- Micro-Segmentation: AI enables the creation of highly specific customer segments. This allows for hyper-targeted product features and messaging designed to resolve specific unmet needs with high precision
Measuring Market Opportunities with AI and Customer Effort
Identifying a segment is only the first step. We then use AI to quantify its potential, primarily through the lens of customer effort.
Quantifying Potential with Customer Effort Scores (CES)
We use Customer Effort Scores to measure how difficult customers find it to get their job done, specifically looking at the speed and accuracy of job execution for each step.
- High CES Indicates Opportunity: A high CES for specific job steps within a segment signals significant unmet needs. These are areas where customers are likely struggling and would be willing to pay for a solution that helps them execute the job step faster or execute the job step more accurately.
- Prioritizing by Effort: Segments with high aggregate CES for critical jobs represent prime targets for innovation and growth. Our AI tools help identify these high-effort areas across the customer base.
This approach, focusing on objectively measurable effort rather than subjective measures, allows our portfolio companies to concentrate on innovations that deliver tangible improvements in speed and accuracy for the customer.
Tailoring Value Propositions with AI Insights
Once a high-potential, high-effort segment is identified, AI insights are crucial for developing value propositions that resonate deeply.
- Personalized Messaging: Understanding the specific JTBD and associated high-effort steps allows for crafting marketing messages that speak directly to the customer's struggle and the solution's benefits in reducing that effort (e.g., "Our new feature helps you determine the optimal configuration in seconds, not hours.").
- Feature Prioritization: Product development can be focused on features that directly address the highest-effort job steps, ensuring that R&D investment is targeted at what truly matters to the customer and thus drives willingness to pay.
Our AI-powered platform helps our portfolio company teams generate these insights in hours rather than weeks, providing a critical speed advantage.
Implementing AI-Enhanced Market Segmentation Effectively
Successfully implementing AI-enhanced market segmentation within our portfolio companies involves several key considerations:
- Data Quality and Accessibility: High-quality, comprehensive data is the fuel for AI. We work to ensure robust data pipelines and governance.
- The Right Tools and Technology: Our proprietary AI platform is designed specifically for JTBD-driven analysis and segmentation.
- Skills and Team Structure: We support our portfolio company teams with training and expertise to effectively use AI tools and interpret their outputs.
- Ethical Considerations: We are mindful of data privacy and consumer trust. It's noteworthy that consumer comfort with AI in marketing saw a decline from 57% in 2023 to 46% in 2024, reinforcing the need for transparent and ethical AI practices.
Our Approach: Intelligent Segmentation for Equity Value Creation
AI-enhanced market segmentation is not an academic exercise; it's a core component of how we operate and create equity value within our portfolio companies. By integrating AI with our proprietary Jobs to be Done methodology, we can:
- Identify unmet customer needs with remarkable precision
- Define high-potential market segments based on customer effort
- Accelerate product innovation that directly addresses these needs
- Drive focused marketing and sales strategies
This systematic approach allows our portfolio companies to achieve accelerated growth and increased company valuations, ultimately creating equity value through product innovation.
Ready to explore how a JTBD-driven, AI-enhanced approach can find your company's next high-growth market? Learn more about our unique methodology for value creation or our AI-powered platform.
Frequently Asked Questions (FAQs)
Q1: What is AI-enhanced market segmentation?
A1: AI-enhanced market segmentation uses artificial intelligence technologies, such as machine learning and natural language processing, to analyze vast amounts of customer data. This allows for the identification of more nuanced, dynamic, and predictive customer segments than traditional methods, often based on deep behavioral patterns, unmet needs (like those identified through Jobs to be Done), and customer effort.
Q2: How does Jobs to be Done (JTBD) improve AI market segmentation?
A2: JTBD provides a critical framework for AI market segmentation by focusing on why customers make choices—the "job" they are trying to get done. AI can then analyze data to find groups of customers with similar unmet needs or high effort in completing these jobs. This leads to segments based on solvable problems rather than just demographics, creating clearer pathways for innovation and targeted value propositions. For example, AI can help identify the steps in a job where customer effort is highest.
Q3: Why is Customer Effort Score (CES) important in AI segmentation?
A3: Customer Effort Score (CES) measures the difficulty customers experience when trying to complete specific steps of their Job to be Done, focusing on speed and accuracy. In AI segmentation, a high CES for a particular job step within a potential segment signals a significant unmet need and a strong opportunity for growth. AI helps to calculate CES at scale and identify segments where reducing effort will provide the most value, thereby increasing willingness to pay.
Q4: What are the key benefits of using AI for market segmentation?
A4: Key benefits include:
- Finding hidden growth opportunities: AI can uncover underserved or entirely new market segments
- Improved targeting precision: Developing more relevant products and marketing messages
- Enhanced customer understanding: Gaining deeper insights into customer needs, behaviors, and effort
- Increased speed and efficiency: Automating data analysis and segment identification
- Predictive capabilities: Forecasting market trends and segment viability
Q5: How does thrv use AI in market segmentation for portfolio companies?
A5: We integrate our proprietary AI platform with our Jobs to be Done methodology. This allows us to identify unmet customer needs and high-effort job steps within potential markets for our portfolio companies. Our AI tools accelerate the analysis of customer data, enabling us to pinpoint high-growth segments, guide product innovation to reduce customer effort, and tailor strategies to maximize equity value creation.