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In today's highly competitive and data-rich go-to-market (GTM) landscape, it’s extremely important to recognize that not all opportunities hold equal value. Propensity modeling offers a powerful, statistically driven methodology that fundamentally transforms how organizations identify, prioritize, and engage with their most promising potential customers and prospects.

Across leading Private Equity funds, this approach is a core component of value creation, often referred to by proprietary terms such as “CapDB” (Vista Equity Partners) or “MoneyMap” (Bain Capital). Regardless of the nomenclature, the foundational principle remains consistent: leveraging advanced data-driven insights to precisely identify and prioritize accounts exhibiting the highest likelihood of conversion or expansion.

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Propensity Modeling’s Evolution

Historically, propensity modeling has been powered by the advances in machine learning. Machine Learning has made the outputs better, but it has still required a data scientist and typically a six-figure price tag.

AI (Artificial Intelligence) opens up an entirely new realm of possibilities for predicting behaviors. AI offer predictive capabilities that can get progressively “smarter,” and agentic offers the possibility to actually take action within the CRM based upon a propensity score.

The general output is the same, but it will be better, faster and cheaper.  Those outputs are:

• Optimized Lead Qualification: Accurately identifying accounts with the highest propensity to purchase.
• Predictive Expansion Potential: Forecasting opportunities for growth within existing client accounts.
• Territory Optimization: Strategically aligning sales resources with the most fertile geographic or account segments.
• Focused GTM Efficiency: Directing marketing and sales efforts toward the most promising opportunities, thereby maximizing ROI.

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Foundational Elements to Propensity Modeling

Data Integrity

Successful propensity modeling - as well as revenue operations as a whole - is predicated on the availability and validity of robust, integrated data. The foundation begins with historical Opportunity and Account data residing within an organization’s CRM. There is software and apps that make the data cleanup and deduplication process much less painful.  However, the key is really around process:

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• Helping teams - including finance - get the data out of finance, ERP and CRM systems and into an easy format to manipulate
• Providing a written framework for the teams going forward to ensure that their customers, invoice history and activity are easily understood and analyzed.

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Enriched Data Sets

This internal data is then enriched with external intelligence, including firmographic and technographic insights, extending beyond conventional data points. Firmographic insights refer to any information around the company, like revenue, employees, funding rounds, or number of customers.  Technographic insights refer to the technologies used at the company. For instance, a highly correlative variable for a top-tier "A" account might be the relative percentage of developers within that company as compared to industry peers.

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Enriched data is extremely important, but it is not data that should necessarily be in the CRM or under consideration by employees.  That could be, at best, confusing and, at worst, paralyzing for employees trying to understand accounts.  This data is ideal for analysis within projects like propensity modeling.

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‍AI and Machine Learning

Modern propensity models employ sophisticated machine learning algorithms capable of identifying and evaluating hundreds of potentially significant variables. These variables are assessed both individually and in tandem to uncover multi-variable correlations.

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Advancements within AI have significantly enhanced our capacity to discern the impact of correlated factors that even a regression analysis might overlook given hundreds of enriched data points.  For instance, there may be a correlation between customers who had a recent funding round, but only among the customer who meet other criteria such as a recent c-suite job change. Or alternatively, we can now involve macro-economic trends. We can quantify how specific policy shifts, such as tariff policies, generate economic headwinds or tailwinds for particular industries and geographic locations.

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Strategic Recommendations for Testing

• Initiate with a Pilot: Begin with a focused pilot project within a specific market segment or product line to validate the model's effectiveness and gain internal buy-in.
• Prioritize Data Investment: Ensure a robust foundation of data collection, cleansing, and integration. Data quality is paramount to model accuracy.
• Select Appropriate Tools: Choose analytics platforms and modeling tools that offer strong predictive capabilities and integration with your existing tech stack.
• Foster Data Literacy: Invest in training across your sales and marketing teams to ensure they can effectively interpret and leverage the insights provided by the propensity model.

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By embracing propensity modeling, organizations can move beyond intuition to a data-driven approach, fundamentally transforming their go-to-market efficiency and accelerating predictable revenue growth.

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