How a Food Delivery firm Used No-Code ML to Reshape Its Marketing Strategy

Customer Segmentation at Scale — Powered by AutoMind

The Context

A leading food delivery platform  wanted to improve its next campaign performance. Over the years, it had launched five large-scale campaigns, but this time, the marketing team wanted sharper insights:

• Who are our most valuable customers?

• How do purchasing behaviours vary by channel?

• Which segments respond best to promotions?

With a wide-ranging product catalogue, from wines to meats and even gold items were sold across stores, websites, and catalogues, they needed a smarter, faster way to segment their 2,240 pilot customers.

The Challenge

Manual segmentation approaches weren’t scalable or explainable. The team needed:

• A clear view of customer clusters based on behaviour and demographics

• A no-code interface that marketing analysts could use directly

• Transparent models that regulatory teams could trust

Enter AutoMind. Our no-code ML platform enabled their marketing team to build, test, and deploy a customer segmentation model in days—not months. Here's how:

Step 1: Data Exploration AutoMind’s built-in visual tools made it easy to identify key variables. Median income hovered around 50k, with most customers aged 25 or above. Highly correlated features were filtered, and income was prioritized as a key predictor.

Step 2: Clustering The team used AutoMind’s clustering module to segment customers. While the Elbow Method suggested five clusters, they opted for four for clarity. These were mapped as:

• Lower Income

• Middle Income

• Upper Middle Income

• Upper Income

Each segment reflected a distinct purchasing pattern, with income playing a central role.

Step 3: Insights That Matter

• Higher-income customers consistently bought more, especially food products.

• Lower-income customers were responsive to promotions and deals.

• Gold products had limited traction, across all income levels

The Outcome

• The client now tailors offers and creatives based on income tiers and channel preferences.

• Campaign planning has become data-driven, not intuition-led.

• ML experimentation is owned by the business, not restricted to the data team.

What Made It Work?

No-code environment: Marketing could build and refine models directly Speed: Full pipeline from raw data to insight in under 1 week Explainability: SHAP-based insights gave business leaders confidence

Your Turn

If your marketing or product team is struggling to translate customer data into real action—it’s time to meet AutoMind.

Connect with us to book a demo or build your first ML model—without writing a single line of code. Let’s help you segment smarter, experiment faster, and sell more.