Propensity Scoring

What is it and why is it interesting for your business?

Propensity scoring is a concept often used in the customer or subscriber management. Propensity scoring can be used to determine the likelihood of a customer buying, up-selling or churning. While all related they can be used throughout the customer lifecycle and require different strategies for management. As customer churn has its own intricacies, we dedicated page specifically to this topic.

Propensity to renew refers to the likelihood that a customer will continue or prolong their contract with the business upon its expiration. This metric is particularly relevant for subscription-based businesses where customer retention is essential for sustained revenue growth. Factors influencing propensity to renew may include customer satisfaction, perceived value of the service, ease of use, customer support experience, and competitive offerings. Rather than defining yourself who is the audience most (un)likely to renew, AI scoring helps marketeers continuously analyses all historical data to automatically gather a relevant target audience at all times. Updating the scoring of each customer over time.

How does it work?

Every customer exhibits unique behaviors and characteristics, ranging from the specifics of their contract (duration, installments, etc.), the product or service they've engaged with, to their individual attributes (customer type, age, past renewal history, etc.). The challenge here is to sift through this diverse array of data and identify patterns that can accurately predict whether a customer is likely to renew their contract or not.

As a proof of concept, we approach this problem as a classification task. We aim to develop a machine learning model capable of discerning whether a customer will renew their contract or not. This involves analyzing the data to determine if there are strong correlations between various factors and the likelihood of renewal. The model outputs the probability of a customer renewing their contract within a given timeframe (e.g., next month, next three months). If the proof of concept generates satisfactory results, the scope can be expanded to answer questions such as when will a customer renew or will the customer renew in the same product/service to determine up-sell possibilities.

 

What do we need to pay attention to?

Utilizing time-series data necessitates a careful train-test split to prevent data leakage, ensuring that information in the training set doesn't inadvertently aid predictions in the test set. For instance, when segmenting contracts into monthly data points, it's crucial to assess if contracts overlapping between training and testing sets lead to data leakage.

Additionally, in the B2C market, strict adherence to GDPR regulations is paramount. It is essential to implement rigorous measures to safeguard customer data privacy and uphold ethical standards throughout the analytical process.