From Customer Data to Churn Predictions in Seconds
Summary
We recently wrote about predicting at the speed of an idea. This time, we wanted to put that idea into practice with a use case we work on constantly with our customers and prospects: customer churn.
Every company has customer data somewhere. It might live in a CRM, a data warehouse, a handful of spreadsheets or all of the above at once. Buried in there are years of interactions, purchases, satisfaction scores, usage patterns, and support history: the full story of your relationship with each customer.
Every marketing team already knows that keeping an existing customer is usually worth more than winning a new one. The hard part is knowing which customers are actually at risk before they walk away.
Traditionally, building a model to answer that question is a long project. Data scientists collect data from business teams, clean and transform it, engineer features, choose an algorithm, train a model, tune it, evaluate it and repeat the cycle until something reliable comes out the other end. By the time a business question turns into a usable prediction, weeks have often gone by.
With Seldon, our tabular foundation model, that process gets a lot simpler and faster.
Here's what that looks like in practice.
From customer data to churn predictions in seconds
The example starts with something almost every business already has: historical customer records where the outcome is known. We know each customer's characteristics, their behavior and whether they eventually churned.
That history is exactly the context Seldon needs to understand the patterns behind churn. We then hand it a second set of customers, the same kind of data but without a known outcome and ask a simple question: which of these customers are most likely to leave?
Using Neuralk's Excel integration, the whole thing happens inside a spreadsheet. We select the historical customer data, select the churn outcome we want to predict, provide the list of new customers and get predictions back along with churn probabilities.
No model training pipeline. No complex setup. No weeks of preparation. Customer data goes in and an actionable prediction comes out.
Predictive AI as easy as an API call
We used Excel here because it's a simple way to see the workflow, but the same thing works wherever your data already lives. If your customer information sits in Snowflake, Databricks, a CRM or any other internal system, there's no need to move it around or stand up a dedicated machine learning pipeline. You can reach Seldon through a single API call or also deploy the model directly inside your preferred cloud instance.
That changes what teams spend their time on. Instead of building and maintaining models, the focus shifts to using predictions to answer the questions that actually matter: which customers should we contact, who should get a retention offer, which accounts need extra attention from the customer success team. The faster those questions get answered, the faster a team can act on them.
Beyond churn
Customer churn is only one example of what our tabular foundation model can help with. Instead of starting from zero on every new task, Seldon carries prior knowledge from learning patterns across a huge range of tabular data, which lets it get from raw data to a usable prediction far faster than training a model from scratch would.
The same approach applies to plenty of other use cases businesses run into, across just about every industry.
You already have the data, and you are sitting on valuable signals. The challenge is turning those signals into decisions quickly enough.
Feel free to contact us to explore how you can start predicting on your own data and accelerate your use case roadmap with predictive AI.
