What is Propensity to Buy Scoring?
Propensity to Buy (P2B) scoring is a predictive analytics method that assigns a numerical score to each prospect or account indicating how likely they are to purchase your product or service within a given timeframe.
Understanding Propensity to Buy Scoring
Propensity to Buy scoring goes beyond traditional lead scoring by incorporating behavioral, firmographic, technographic, and intent signals into a unified model that predicts purchase likelihood. While traditional lead scoring typically relies on static attributes like job title or company size, P2B scoring dynamically weights dozens or even hundreds of signals — including recent funding events, technology adoption patterns, hiring velocity, website engagement, and competitive displacement indicators — to produce a score that reflects real-time buying readiness.
The foundation of effective P2B scoring lies in historical pattern recognition. By analyzing closed-won deals and identifying which signals were present before conversion, the model learns to recognize similar patterns in new prospects. This feedback loop means the scoring model improves over time as it processes more outcomes. The most sophisticated P2B systems also account for timing — understanding that a signal like a new VP of Sales hire is most predictive within the first 90 days, after which its weight decays.
P2B scoring transforms sales prioritization from gut feel into data-driven decision-making. Instead of reps working through lists alphabetically or based on company name recognition, teams can focus their limited time on the accounts statistically most likely to convert. This leads to higher win rates, shorter sales cycles, and more efficient use of sales capacity across the organization.
How Prospectory Uses Propensity to Buy Scoring
Prospectory's P2B scoring engine is a core differentiator of the platform. It analyzes 128+ data points per prospect — spanning firmographic attributes, technographic signals, hiring patterns, funding events, leadership changes, web engagement behavior, and third-party intent data from 50+ sources — to produce a continuously updated propensity score for every account in your pipeline.
What makes Prospectory's approach unique is the closed-loop feedback system. The P2B model trains on your specific closed-won and closed-lost deals, so the scoring is calibrated to your ICP and sales motion rather than relying on generic benchmarks. Scores are refreshed in real-time as new signals emerge, and the platform surfaces the specific signals driving each score so reps understand why an account is ranked highly. Customers using P2B scoring in Prospectory report 3x higher win rates compared to unscored outreach.
Frequently Asked Questions
How is Propensity to Buy scoring different from traditional lead scoring?
Traditional lead scoring typically uses static, manually assigned point values for attributes like job title, company size, or email opens. P2B scoring uses machine learning to dynamically weight hundreds of behavioral and contextual signals based on their actual correlation with closed deals. This makes P2B scoring predictive rather than merely descriptive, and it adapts automatically as buying patterns shift.
What data inputs are needed to build a P2B scoring model?
At minimum, you need historical deal outcomes (closed-won and closed-lost) and the firmographic and behavioral attributes associated with those deals. More advanced models incorporate technographic data, intent signals, website engagement, hiring activity, funding events, and competitive intelligence. The richer the data, the more accurate the scoring.
How often should P2B scores be updated?
Best-in-class P2B systems update scores in near real-time as new signals are detected. Buying signals like a funding round or executive hire are time-sensitive — a score that is refreshed weekly might miss the optimal outreach window. Continuous scoring ensures your team is always working the most current prioritization.
Can P2B scoring work for companies without a lot of historical deal data?
Yes, though accuracy improves with more data. Early-stage companies can start with industry benchmarks and general signal weighting, then refine the model as they accumulate their own deal outcomes. Even a baseline P2B model built on 30-50 closed deals can meaningfully outperform random or alphabetical prioritization.
What is a good P2B score threshold for outreach?
There is no universal threshold — it depends on your sales capacity and conversion goals. Most teams define tiers: high-P2B accounts get immediate, personalized multi-channel outreach; mid-P2B accounts enter automated nurture sequences; and low-P2B accounts are monitored for signal changes. The key is matching your available sales capacity to the volume of high-scoring accounts.
Related Terms
Buying Signals
Buying signals are observable actions, events, or behavioral patterns exhibited by a prospect or account that indicate an increased likelihood of making a purchase decision in the near future.
Signal Intelligence
Signal intelligence is the practice of systematically collecting, analyzing, and operationalizing real-time market signals — such as funding events, hiring trends, technology changes, and intent data — to drive more targeted and timely sales engagement.
Propensity to Convert
Propensity to Convert is a predictive metric that estimates the likelihood of a prospect completing a specific desired action — such as booking a meeting, responding to outreach, or moving from one sales stage to the next — based on behavioral, firmographic, and engagement data.
Account Intelligence
Account intelligence is the comprehensive, continuously updated body of knowledge about a target company — including firmographic data, organizational structure, technology environment, financial health, strategic initiatives, and real-time signals — that enables sales teams to engage accounts with deep context and precision.
See Propensity to Buy Scoring in Action
Experience how Prospectory applies propensity to buy scoring to help sales teams close more deals.
Book a Demo