Propensity Modelling

Improve decision making

Statistical technique that predicts customer behaviour so you can make informed decisions through outcome mapping

Modelling for marketing and sales

Our customer propensity modelling services use past behaviours to predict what customers are most likely to do in the future. You can use this intelligence to make your marketing and sales efforts more targeted, timely and impactful. Identify the customers who are most likely to meet your desired outcome. We tailor your propensity models to your business objectives, so whether you’re looking to improve customer responses, repeat purchases, or renewals – we can help you with the data insight to match. It is all built around you.

Understanding your needs

We begin by understanding your business and campaign objectives. This tells us which customer groups are of the most interest to you, and the desired outcome of your marketing or sales campaigns.

Our team of statistical analysts build bespoke propensity models based on your goals. This is achieved through logistic regression, which estimates the probability of certain customer behaviours based on real data.

Proven marketing data

We match your customers to our Prospect database, the most comprehensive, clean and accurate consumer database in the UK. Using statistical analysis of over 900 consumer behaviour variables, we generate a view of which customers are the most likely to meet your desired outcome.

Our propensity models are used to predict how and when customers are likely to engage with your brand in the future.

Putting you one step ahead

Propensity models allow you to identify your best customers and provide them with personalised communications. Tailor your messaging with the right offer, to the right customer, through the right channel. The power of predictive data modelling is unmatched – increase response rates, lifetime value, repeat purchases, and more. With predictive customer data, you can ensure your marketing is targeted, timely and impactful - that can be applied to: