Used across industries to predict customer LTV, Machine Learning (ML) models are built to increase the understanding of customer buying profiles based on various attributes and buying patterns. Their output is used as input into various business processes such as targeted pricing, customer support prioritization, and managing acquisition costs. Using similar attributes, ML models can be created to predict the churn of existing customers. This can enable companies to either invest in retention initiatives or reallocate marketing budget to more profitable activities and client segments.
In order to estimate the lifetime value of a customer, there are a few variables that need to be estimated, such as purchase frequency, customer lifetime, future growth, the monetary value of each purchase, etc. A shared working space may calculate the LTV mainly based on customer lifetime and expected growth, while an e-commerce website may need to work with more unknowns, including purchase frequency, customer lifetime, and monetary value. This is done either by a unified ML pipeline, where the final output is given by a machine learning model (e.g. XGBoost, random forest) or by creating a machine learning model for each of the different components and combining their outputs using a pre-defined formula.
As in any machine learning environment, the production data in a system estimating LTV or churn may differ from the data used for training, validating, and testing the model in the research environment. There are many different causes of this, including inconsistencies between multiple data sources, unknown changes in the live data, and inherent biases that weren’t identified during the training process. These challenges are intensified if the ML pipeline isn’t “plain vanilla”, and contains multiple components or constraints.
There are numerous tools available in software development environments to ensure the quality of different systems before going live. However, in cases like LTV or churn, there is no such thing as an expected output derived before the real-life labels come back, essentially making many of the typical software development techniques for QA useless. This leads to a situation in which companies don’t have robust methods for continuously validating their LTV/churn system. Since ML systems fail silently, this may mean that major issues may remain in the system, undetected, for months.
How Deepchecks Can Help
Deepchecks continuously validates the end-to-end integrity of your LTV or churn machine learning pipeline, throughout its various phases — identifying anomalies and detecting blind spots in real-time. Leveraging the combination of advanced algorithms and a robust engineering stack, Deepchecks warns about bad calls and minimizes risk with validation and observability for machine learning-based systems. Deepchecks’ solution helps increase your confidence for LTV and churn by:
- Connecting the ML pipeline’s different components across both training and production, including raw text, features based on raw text (if applicable), processed features (if applicable), and the model predictions.
- Learning the training data characteristics and automatically identifying concept drift, out of distribution samples, anomalies, and data integrity issues in real-time.
- Enable defining a rule-based model to distinguish whether input and output data is legitimate or not, with the system triggering alerts and displaying insights.
Irrespective of whether they’re based only on text or a combination of text and other data sources, Deepchecks is the best choice for ensuring the quality of your machine learning system. Allowing you to scale faster within your organization, increased confidence in NLP/NLU predictions enables you and your data science team to move on to other NLP or NLU tasks. And there’s so much more to do!