Early Foods: Boosting Sales and Trust

ML Driven Recommendations

EarlyFoods, an e-commerce platform offering millet-based products for new and expecting mothers, found customers missing relevant items—limiting cart value. To solve this, we recommended them an AI-driven recommendation engine. They added it on product pages using a hybrid of collaborative and content-based filtering to deliver personalized suggestions. This has boosted average order value, enhanced product discovery, and strengthened customer trust.

EarlyFoods AI recommendation system interface showing product suggestions

CHALLENGES

Boosting Product Discovery for Busy Mothers

Limited Browsing Time

Expectant and new mothers had little time to explore, often skipping detailed product info and missing key nutritional items.

Lower Cart Value

Fewer product views led to fewer items added per session, reducing average order value and overall customer satisfaction.

Need for Smart Suggestions

Research shows helpful recommendations increase cart additions by 45%, highlighting the importance of guided discovery.

Risk to Conversions and AOV

Without personalized suggestions, Early Foods risked lower conversion rates and reduced revenue, as seen across the retail industry.

Solutions

We added an AI-powered “You may also like” section that shows smart product suggestions based on your browsing and preferences.

Collaborative Filtering

We built a user–product interaction matrix from past purchases and browsing data. Using cosine similarity, the model finds users with similar tastes to recommend items—capturing the "wisdom of the crowd."

Content-Based Filtering

Each product's attributes (e.g., ingredients, nutrition facts, goals) are vectorized to build a feature profile. The system then recommends items with similar ingredients or health benefits.

Nutritional Relevance

To suit Early Foods' audience, the engine filters by category (e.g., pregnancy-safe) and ensures diversity, avoiding near-duplicates to keep recommendations valuable.

Real-Time Integration

Built in Python with Pandas and Scikit-learn, the pipeline runs on each page view, instantly showing AI-driven suggestions to help users discover products without leaving the page.