Recommend relevant products to customers
December 5, 2022/2 min read
Match the right customers with the right products
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.
Predict user likelihood to buy specific items
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.
Validate the recommendation model
Add perspective to the recommendations and perform audits by combining the ML model with actual historical purchase data.
Check ou the recommendations dashboard
Recommend ideal products
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.
Explore customer purchasing frequency
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.
Go to the user analysis dashboard
Answer key marketing and sales impact questions
The Dataiku Solution for Product Recommendation helps answer a broad range of questions like: How can I use past transaction data to uncover consumer preferences and purchase patterns? How can I use ML to predict whether a user will purchase a product or not? How can I improve my customer lifetime value and marketing efficiency using personalization approaches?
Read the documentation on product recommendation
Get the right product to the right consumer at the right time
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.
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