Quickly turn historical transaction data into actionable insights in a ready-made project platform with clear flow zones. This product recommendation project builds suggestions based on existing purchase data.
Adjust connection settings in the Dataiku App and optimize the machine learning (ML) model. Set main parameters connected to the data, train a random forest model, and use our recommendations plugin to calculate user-item affinity scores.
Add perspective to the recommendations and perform audits by combining the ML model with actual historical purchase data.
Evaluate the potential value of items by checking item distribution and related customer interactions and total revenue generated. Get clarity on which specific products should be pushed to which consumers, as well as similar products to offer.
Generate and share data showing how often your existing customers buy items and the total and average number of interactions that occurred in a given time period. Get a clearer understanding of the relationship between customers and items purchased, giving new insights to marketing and sales teams.
The Dataiku Solution for Product Recommendation helps answer a broad range of questions like:
Put AI and ML to work in analyzing customer purchase data to start building a recommendation system using collaborative filtering. Start strengthening the relationship with customers by pushing the right product to the right customer through the right channel, and reinforce your marketing efficiency.
A composite organization in the commissioned study conducted by Forrester Consulting on behalf of Dataiku saw the following benefits:
reduction in time spent on data analysis, extraction, and preparation.
reduction in time spent on model lifecycle activities (training, deployment, and monitoring).
return on investment
net present value over three years.