Propensity Modelling

Predict customer behaviour

Predict customer behaviour

Propensity modelling helps you understand what your customers are going to do next. Predictive in nature, it enables you to analyse prospects’ known features, past behaviours and purchasing history to determine if they will take the action you want them to, such as make a purchase or respond to a discount.

By knowing what your customers are up to, you can effectively optimise different channels in your marketing mix and increase conversions. You can understand when to launch an email sequence, if a prospect has budgetary constraints and which personalised promos will retain the most active shoppers.

How we can help you

How we can help you

Our data science team can help you consolidate your transaction history and customer activity data to create a dynamic, scalable and ROI-driven propensity model, providing you with insights that will drive more results with less spend.

Our propensity modelling service

Scope definition and analysis

In our initial meeting, we’ll go through your business objectives and suggest the best modelling options. We can develop propensity models for first-time and repeat buyers, implement predictive lead scoring or create more specific models estimating the prospects share of wallet, likelihood to convert/churn or even devise a model suggesting the best discount strategies for different customer segments.

Data collection and preparation

Our team will schedule a deep inventory of your current data sources, recommend and implement the most suitable data warehousing and consolidation solutions, and start the data cleansing process.

Roll out & monitoring

Our team will keep a close eye on the model’s early performance, and ensure that new information flows in smoothly so that you can avoid any confirmation bias in the future. At this point, you will gain access to a reliable stream of insights and can take immediate action to improve your marketing campaigns.

Frequently asked questions

Propensity modelling predicts the likelihood of users, leads and customers taking specific actions. By analysing the historical behaviour of your audiences and comparing this against the behavioural patterns of similar users, we can predict how likely individual prospects are to buy from you or respond to specific types of messaging – eg: a lead nurturing campaign, a coupon offer or even suggesting a more expensive product.

Propensity modelling is commonly used to score the quality of leads and customers as they progress along the consumer journey. So, early on in the consumer journey, propensity modelling can predict the likelihood of leads converting into paying customers. This allows you to target low-scoring leads with campaigns designed to increase incentive and convert more of them into customers.

After the initial purchase, propensity modelling can predict the likelihood of future purchases, customer lifetime value and customer churn. You can use this data to recommend products that are most likely to secure the next purchase or take action as soon as customers show signs of churning and keep them engaged with your brand.

An agency that specialises in propensity modelling has the tools and processes needed to make accurate predictions about your target audiences. Here at Vertical Leap, we’ve built a large database of user behaviour, which we can use to compare your visitors to make reliable predictions from day one.

Yes – this is the key benefit of using propensity modelling. It uses big data to inform marketing decisions so that you can respond to user needs faster and with greater accuracy. For example, when a prospect starts showing signs of purchase interest, you can automatically trigger remarketing or email campaigns to capture the sale before they buy from one of your rivals.

Likewise, you can make data-driven product recommendations to your existing customers to maximise repeat purchases and respond as soon as they show signs of churn to maximise customer retention.

Propensity modelling uses a wide range of datasets to predict user behaviours, including:

  • Demographics: Age, gender, location and any other accessible data that helps paint a picture of “who” users are.
  • Behavioural data: On-site or in-app actions, such as product pages viewed, content engaged with, conversions completed, etc.
  • Purchase history: Historical purchases and patterns that can be used to make relevant recommendations and predict future purchases.
  • Brand engagement: This can include frequency of website visitors, app usage, email engagement, purchase frequency and other signals that can be used to predict engagement and potential churn.
  • Third-party data: By comparing the data of your visitors, leads and customers with similar audiences, we can predict actions with greater accuracy to inform marketing decisions.

With a deeper understanding of what your customers are doing, you can optimise and automate campaigns to respond to their changing needs with greater speed and accuracy.