Propensity modelling attempts to predict the likelihood that visitors, leads and customers will perform certain actions. It’s a statistical approach that accounts for all the independent and confounding variables that affects said behaviour. The propensity score, then, is the actual probability that the visitor, lead, or customer will perform a certain action.
So, for example, propensity modelling can help a marketing team predict the likelihood that a lead will convert to a customer or that a customer will churn, or even that a recipient of digital marketing will unsubscribe. Effective use of data and modelling can therefore improve performance-levels achieved in a marketing campaign, resulting in an increased return on investment.
Predictive modelling is a very broad category and it is possible to build predictive models for a variety of behaviours.
In this first part of a two-part blog, we provide a short overview of just some of the propensity models that Principa has developed across the customer engagement lifecycle to predict behaviour and solve business problems.
Customer Engagement Lifecycle Stages: Acquisitions, Account Growth, Retentions and Reactivations
Propensity to connect and right-party-contact (RPC) model
By making use of customer data, the RPC model predicts your being able to contact the right or intended party at a customer’s account level. Looking at the RPC curve, you can easily determine its peak, plan a call and determine the likelihood of having the call answered.
The call connect model utilises your internal dialler, customer account and alternative sources of data to model the probability of achieving a right-party-contact (RPC) at both an account and contact number level.
Risk modelling is a technique used by organisations to determine the level of risk associated with extending a facility or service to a borrower. Credit risk analysis models can be based on either financial statement analyses, default probability, or machine learning.
A common method for predicting credit risk is through the credit scorecard. The scorecard is a statistically based model for attributing a number (score) to a customer (or an account) which indicates the predicted probability that the customer will exhibit a certain behaviour. In calculating the score, a range of data sources may be used, including data from an application form, from credit rating agencies, or from products the customer already holds with the organisation.
When you are running a marketing campaign it is not always possible, or even desirable, to target your entire customer base. Various analytical techniques can be used for response modelling such as logistic regression, decision trees, neural networks and random forests.
Response models are used primarily to help marketeers understand how consumers individually and collectively respond to marketing activities and how competitors interact. Once the response target has been appropriately defined, the historical data for analytical modelling needs to be gathered from previous marketing campaigns in order to properly understand the customer’s response behaviour.
Propensity to Purchase Model
A propensity to purchase model is a type of predictive model that is used to understand the likelihood that a customer will be predisposed to purchasing a product, based on a purchase they’ve already made at some point in time. Traditional propensity to purchase models score customers based on their similarity to past purchases. These models require having historical data and measuring the enterprise’s past performance regarding offerings and customer activities, for you to effectively deploy cross-selling and up-selling techniques.
Propensity to Pay / Default Model
Predicting a customer’s propensity to pay at an early point in the revenue cycle can provide organisations with opportunities to improve the customer experience, reduce hardship and reduce the risk of impaired cash flow and occurrence of bad debt.
The propensity to pay model enables you to segment and prioritise collections actions at a debtor-level in the active collections book, based on propensity to pay scores. It can then be used in conjunction with a to connect model to further improve payment and collections strategies.
Building Advanced Analytical Capabilities
In part two of this blog, we further expand on the types of propensity models that organisations typically adopt that predict the likelihood that visitors, leads and customers will perform certain actions.
Principa was established in 1999 and has over the past 22 years developed a multitude of market leading solutions for our clients in South Africa, Africa and the Middle East; across the customer engagement lifecycle – where data analytics lies at the core of the solutions we deliver to the market.
Find out how Principa can help you better manage your customers and Work Wonders. Contact us.
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