Adapt The Solution & Play With Your Own Data
The flow can be easily adapted with on-demand customization services to allow you to upload your own transactional data and rebuild the flow based on your goals.
Explore !This project offers an adapt-and-apply solution allowing retail analytics teams to build a recommendation system in order to push the right product to the right customers. The solution showcases how to use the new Recommendation System Plugin to solve a real-world use case. More details on the specifics of the solution can be found on the knowledge base.
Business Overview
95% of companies that implement personalization see 3x ROI in the year after the investment. And this is no surprise: indeed, 91% of consumers are more likely to shop with brands that recognize, remember, and provide relevant offers and recommendations. Recommending the right product to consumers has thus become a must-do to secure market shares and grow loyalty. This can notably be done by implementing a recommendation engine based on a collaborative filtering approach that aims at answering a simple question: what are the items that users with interests similar to yours like?
By doing so, brands can recommend products that have not yet been purchased by a user and that a group similar to this user has purchased. What the outcome is: encourage customers to discover new products they simply would not have actively looked for, and keep them engaged longer. Several use cases can then be run, many of them online: personalization of website homepage in a very tailored way (for logged-in users), sending of personalized promotional emails based on past transactions, … Impact of built recommendation engines will be maximized by the quality of data: the richer the history is, the more performant the recommendation engine will be.
Highlights
- Input transaction data, split your customers into established vs growth depending on the number of past purchases or interactions with the brand
- Leverage our recommendation plugin to calculate user < > item affinity
- Define the solution settings by easily changing the settings of our recommendation plugin
- Leverage Dataiku’s auto ML features to train a model that will predict whether a user will buy an item
- Consume the output and understand which products should be pushed to which consumers, and for each product what are the top ten most similar ones
- Automate the pipeline to rebuild with new/fresh data and to send the results to a CRM system
Data Driven Insights
A ready-made project with clear flow zones turns transactional data into actionable insights to best match the right customers with the right products.
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Understand the similarities across your products
Select a product and get the 10 most similar products.
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Understand your consumers' affinity with products
Get a quick and shareable view of the link between your existing consumers and your products. Assess the potential incremental value.
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Go further: automate data exports to feed your CRM system
The 'Send New Recommendations' button will kick off a Dataiku Scenario that scores new customer data and sends a dataset of recommendations via email. The Send Recommendations scenario can be configured to send the data to a CRM of choice.
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