Propensity Marketing: Stop Targeting Everyone, Start Targeting Who’s Ready
Propensity marketing is the practice of using behavioural, demographic, and transactional data to identify which customers or prospects are most likely to take a specific action, and then concentrating your marketing effort on those people. Instead of broadcasting to a broad audience and hoping the right people respond, you build a model that scores individuals by their likelihood to buy, churn, upgrade, or convert, and you prioritise accordingly.
Done well, it shifts marketing from a volume game to a precision one. Done poorly, it becomes an expensive way to optimise for the people who were going to act anyway.
Key Takeaways
- Propensity models score individuals by likelihood to act, allowing teams to concentrate budget where it is most likely to generate return.
- The biggest risk in propensity marketing is over-indexing on people already close to converting, which inflates efficiency metrics without driving real growth.
- Model quality depends entirely on data quality. A propensity score built on thin or biased historical data will systematically point you in the wrong direction.
- Propensity marketing works best as a prioritisation tool, not a replacement for audience strategy. It tells you who to focus on first, not who to ignore permanently.
- The most commercially valuable application is combining purchase propensity with lifetime value signals, so you are not just finding likely buyers but likely profitable ones.
In This Article
- What Does a Propensity Model Actually Do?
- Why Propensity Marketing Looks Better Than It Is
- Where Propensity Marketing Creates Real Commercial Value
- How to Build a Propensity Model That Is Actually Useful
- The Data Requirements Most Teams Underestimate
- Propensity Marketing in a B2B Context
- The Measurement Problem You Need to Solve
I have spent a good portion of my career working with clients who had access to more data than they knew what to do with. Retailers, financial services firms, subscription businesses. The data was there. The models were sometimes there. What was consistently missing was a clear commercial question to answer. Propensity marketing only earns its keep when it is built around a specific decision: who do we call first, who do we email this week, who do we spend acquisition budget on. Without that, it becomes a modelling exercise that never quite connects to revenue.
What Does a Propensity Model Actually Do?
A propensity model takes a set of input variables, things like purchase history, browsing behaviour, demographic profile, recency of engagement, product category affinity, and uses them to produce a score. That score represents the probability that a given individual will take the action you are trying to predict.
The most common applications are:
- Purchase propensity: likelihood to buy within a defined window
- Churn propensity: likelihood to cancel or lapse
- Upgrade propensity: likelihood to move to a higher-value product or tier
- Reactivation propensity: likelihood that a lapsed customer will respond to re-engagement
- Category propensity: likelihood to buy from a specific product category next
The mechanics vary. Logistic regression is still widely used because it is interpretable. Gradient boosting and random forest models are more common now in teams with data science capability. Some CRM platforms have propensity scoring baked in, which is useful but should be treated with appropriate scepticism, because a generic model trained on industry-wide data will never be as accurate as one trained on your own customers.
What matters commercially is not the algorithm. It is the quality of the input data, the relevance of the target variable, and whether the outputs are actually used to make decisions. I have seen beautifully constructed propensity models sit in a data warehouse for six months because no one agreed on what to do with the scores.
If you are thinking about this in the context of go-to-market planning more broadly, the Go-To-Market and Growth Strategy hub covers the wider commercial framework that propensity marketing sits inside. Propensity is a prioritisation mechanism. It does not replace the need for a coherent growth strategy.
Why Propensity Marketing Looks Better Than It Is
This is where I want to be direct, because the industry has a tendency to celebrate propensity marketing in ways that obscure a genuine problem.
When you concentrate your marketing on people who are already highly likely to convert, your conversion rates go up. Your cost per acquisition comes down. Your campaign metrics look excellent. The problem is that a meaningful portion of those people were going to buy regardless of whether you contacted them. You have not created demand. You have intercepted it, and in many cases, you have spent money to intercept demand that would have arrived on its own.
Earlier in my career I was firmly in the lower-funnel performance camp. We got very good at finding high-intent signals and converting them efficiently. The numbers were strong. But when I started asking harder questions, specifically, how much of this would have happened without us, the answers were uncomfortable. The channel was taking credit for conversions it had not caused. Propensity marketing, used naively, has the same structural problem.
This is not an argument against propensity modelling. It is an argument for being honest about what it does. It is a tool for prioritising who to talk to first among an audience you have already defined. It is not a substitute for reaching new audiences or building demand in markets where your brand is not yet known. Market penetration requires reaching people who have not yet decided to buy from you, and propensity models are structurally blind to those people because they have no historical signal to score.
The best analogy I have used with clients is this: a propensity model is like a map of who is already standing near the checkout. It is genuinely useful. But if your growth problem is that not enough people are entering the shop, the map does not help you.
Where Propensity Marketing Creates Real Commercial Value
With that caveat on the table, there are contexts where propensity marketing creates clear, defensible commercial value.
Sales team prioritisation. When I was working with a B2B client that had a sales team of forty people and a CRM with tens of thousands of contacts, the question was simple: who do we call this week. A propensity model that scored contacts by likelihood to engage within a 30-day window was worth more than any amount of campaign optimisation. It changed how the team spent their time, and time is the actual constraint in most sales operations. Research from Vidyard on pipeline development points to exactly this kind of prioritisation problem as a source of untapped revenue potential in go-to-market teams.
Churn prevention. This is probably the highest-value application in subscription and retention-heavy businesses. If you can identify customers showing early signals of disengagement, you can intervene before the decision to leave is made. The window matters enormously here. A churn model that flags customers after they have already mentally checked out is much less useful than one that catches the early behavioural signals, reduced login frequency, declining usage, support ticket patterns.
Budget allocation across a large customer base. If you are managing a CRM with hundreds of thousands of customers and a finite marketing budget, propensity scoring gives you a principled way to decide who gets investment. Not everyone in your base has the same expected return. Treating them identically is not fairness. It is waste.
Combining propensity with lifetime value. This is where the approach gets genuinely powerful. A customer with high purchase propensity but low predicted lifetime value is not necessarily worth the same investment as a customer with moderate purchase propensity and high predicted lifetime value. When you combine the two signals, you stop optimising for conversion volume and start optimising for commercial return. That is a fundamentally different and better question.
Personalisation at scale. In content, email, and product recommendation contexts, propensity signals can drive relevance without requiring manual segmentation. If the model knows that a particular customer has a high affinity for a product category, you can surface that category without a human having to make that call for each individual.
How to Build a Propensity Model That Is Actually Useful
I am not going to walk through the statistical mechanics here. There are data scientists better placed to do that. What I can offer is the commercial and strategic framing that separates models that drive decisions from models that produce interesting slides.
Start with the decision, not the data. Before you touch a dataset, define the specific action you are trying to predict, the time window in which it should occur, and the business decision that will change based on the score. If you cannot answer those three questions, you are not ready to build a model. You are ready to have a strategy conversation.
Audit your historical data before you trust it. Propensity models learn from the past. If your past behaviour data is incomplete, biased, or reflects a period that is not representative of current conditions, your model will encode those problems. I have seen models trained on pre-pandemic behaviour patterns that were worse than useless when deployed in 2021. The data looked fine. The context had changed entirely.
Validate against a holdout group. The only honest test of a propensity model is whether the people it scores as high-propensity actually convert at a higher rate than a control group, in a real deployment, not just in a backtested training set. Backtesting is necessary but not sufficient. Models that look strong in backtesting and fall apart in deployment are more common than the industry admits.
Build in a feedback loop. A propensity model that is not updated with new outcome data will decay. Customer behaviour changes. Market conditions shift. The signals that predicted purchase intent twelve months ago may not be the same signals that predict it today. This is not a one-time build. It is an ongoing process. Growth loops in product and marketing thinking share this same principle: the mechanism only stays useful if it feeds back on itself.
Be transparent about model confidence. A score of 0.73 is not the same as certainty. Propensity scores are probabilistic. Treating them as deterministic leads to over-confidence in targeting decisions and under-investment in the audiences the model is less certain about. Some of the best commercial opportunities sit in the medium-propensity band, people who are genuinely persuadable but whom a naive model would deprioritise in favour of the near-certain converters.
The Data Requirements Most Teams Underestimate
Propensity modelling is data-hungry. Not in the sense that you need a massive dataset to get started, but in the sense that the quality, recency, and relevance of your data will determine whether the model is commercially useful or statistically interesting but practically inert.
The minimum viable data foundation for a purchase propensity model typically includes: transaction history with timestamps, product or category-level detail, recency and frequency metrics, some form of engagement signal (email opens, site visits, app activity), and ideally some demographic or firmographic context. Without transaction history, you are guessing. Without recency signals, you cannot distinguish active customers from dormant ones.
First-party data is the foundation here, and the businesses that invested in building clean, structured first-party data assets over the past decade are now sitting on a significant competitive advantage. Those that relied on third-party data are facing a harder rebuild. Forrester’s work on intelligent growth has consistently pointed to data infrastructure as a differentiating capability, not just a technical requirement.
One thing I have noticed across multiple client engagements: teams consistently underestimate how much data cleaning and preparation work sits between raw CRM data and a usable model input. Duplicate records, inconsistent product categorisation, missing timestamps, mixed-currency transaction values. These are not exotic problems. They are present in almost every large customer database I have worked with. The modelling is often the fastest part. The data preparation is where the time actually goes.
Propensity Marketing in a B2B Context
Most of the literature on propensity marketing is written with B2C in mind, but the logic applies equally in B2B, with some important structural differences.
In B2B, the unit of analysis is usually the account, not the individual. A buying decision involves multiple stakeholders, and propensity signals need to be aggregated at the account level. Someone reading a case study is a weak signal. Three people from the same company reading case studies in the same week is a meaningful one.
Intent data providers have built a significant market around this insight. They aggregate signals from across the web, content consumption, search behaviour, review site activity, and score accounts by their apparent interest in a category or solution. Used carefully, this is a useful supplement to first-party signals. Used uncritically, it produces a lot of false positives and wastes sales team time on accounts that were researching competitors, not you.
The most effective B2B application I have seen combined first-party engagement data (website visits, content downloads, email engagement) with product usage signals (for existing customers) and firmographic fit scoring. That combination produced account scores that the sales team actually trusted, which is the real test. A model that the commercial team does not believe in will not change behaviour, regardless of its statistical validity. BCG’s analysis of go-to-market strategy in financial services makes a similar point about the importance of aligning analytical outputs with commercial team workflows.
The Measurement Problem You Need to Solve
Measuring the impact of propensity-based targeting is harder than it looks, and most teams do it wrong.
The standard approach is to compare conversion rates between high-propensity and low-propensity segments. High-propensity converts better. The model is declared a success. But this tells you almost nothing about incremental impact. Of course high-propensity customers convert more. That is what the model was designed to find. The question is whether they converted because of your marketing, or whether they were going to convert anyway.
The honest measurement approach requires a holdout test. Take a random sample of high-propensity customers and deliberately exclude them from your campaign. Compare their conversion rate against the high-propensity customers who received the campaign. The difference is your incremental lift. If there is no meaningful difference, your campaign is not adding value. It is riding existing intent.
This is an uncomfortable test to run, because it sometimes produces uncomfortable results. I have run it with clients who were confident in their propensity-driven programmes, and the incremental lift was materially lower than the headline conversion rates suggested. That is not a reason to abandon the approach. It is a reason to be honest about what you are actually measuring and to keep investing in the audience-building activity that creates future propensity rather than just harvesting current propensity.
Growth strategy is not just about converting the people who are ready. It is about expanding the pool of people who will be ready in the future. That broader framing is what the Go-To-Market and Growth Strategy hub is built around, and propensity marketing is one tool within it, not a substitute for the whole.
About the Author
Keith Lacy is a marketing strategist and former agency CEO with 20+ years of experience across agency leadership, performance marketing, and commercial strategy. He writes The Marketing Juice to cut through the noise and share what works.
