Anticipating Severe Customer Attrition: Predicting Without Characteristics
In the fast-paced world of business, identifying customer churn is crucial for maintaining a healthy revenue stream. Two simple and effective approaches, Sigma Modeling and Cumulative Distribution Function (CDF) Modeling, have emerged as powerful tools for predicting churn under data scarcity.
Sigma Modeling, a statistical technique, focuses on analyzing how key customer metrics deviate from the average behaviour. By examining metrics such as engagement frequency and transaction values in terms of standard deviations (sigmas), it identifies customers whose behaviours significantly deviate from the norm. These customers, whose metrics fall beyond certain sigma thresholds, can be flagged as potential churn risks.
On the other hand, CDF Modeling uses the cumulative distribution function to statistically characterize the likelihood of customer behaviour metrics falling below (or above) certain values. By comparing a particular customer's metric with the CDF, analysts can quantify the probability of churn-related behaviours occurring. For instance, a customer whose recent engagement score falls in the lower tail of the distribution (e.g., below the 10th percentile), may have a higher churn probability.
These models allow companies to detect early signs of churn from limited behavioural, transaction, or engagement data. They also support targeted intervention strategies, like retention campaigns or special offers, for customers exhibiting statistically unusual patterns signaling risk. Furthermore, they provide an interpretable, explainable framework that can guide decision-making even when advanced machine learning tools are unavailable or unreliable due to limited data.
Notably, customers are evaluated only after a fixed amount of purchases (6 orders in this case) to build reliable order frequency distributions. At inference time with the CDF approach, it's possible to identify the probability of any difference between today's and the last order date being greater than a predefined level of confidence. Both approaches can be tuned and adjusted by tweaking the sigma level or the CDF confidence.
In a comparison, the Sigma Modeling shows the highest precision, while the CDF Modeling has a higher recall rate. This means that Sigma Modeling is better at identifying true positives (customers who actually churn) without false positives (customers who don't churn), while CDF Modeling is better at identifying all potential churners, although it may also include some false positives.
In conclusion, by focusing on statistical deviations (Sigma Modeling) and the probabilistic placement of customer behaviour within a distribution (CDF Modeling), businesses can achieve practical and reliable churn identification under constraints of limited or incomplete data. These models serve as a starting point before more complex predictive models are deployed with richer datasets.
In today's data-centric world, businesses collect and use individual data to produce valuable insights. These simple, yet effective models, can be used as a benchmark when there are possibilities to adopt more complex solutions. They can help businesses work with few observations, low-quality data, or without meaningful predictors, making them invaluable tools in the quest for customer retention.
Advanced technology like data-and-cloud-computing and artificial-intelligence can be integrated into the churn prediction models mentioned, improving their accuracy and efficiency. For instance, AI can help automate the process of flagging customers based on sigma thresholds in Sigma Modeling or by determining the probability of churn in CDF Modeling, thus reducing manual intervention and increasing processing speed.
By employing technology solutions such as AI and data-and-cloud-computing, businesses can efficiently analyze diverse data sources, leverage advanced statistical techniques, and deliver more accurate churn predictions, thereby enhancing their customer retention strategies.
