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.
This project offers an plug-and-play solution allowing retail analytics teams to build a recommendation system to push the right product to the right customers. More details on the specifics and requirements of the solution can be found in the knowledge base. This solution is available on installed instances and Dataiku Online.
Companies that successfully implement personalization drive 40% more revenue than the average company. And this should come as no surprise: indeed, 71% of consumers expect companies to deliver personalization and are more likely to shop with brands that they recognize, that understand them, and provide relevant offers and recommendations that grow lasting relationships. Recommending the right product to customers is now a must-do in order to secure market share and build loyalty. This can notably be done by implementing a recommendation engine based on a collaborative filtering approach which aims at answering a simple question: what items will appeal to customers who share similar preferences?
By answering this important question, brands can in turn recommend products that have not yet been purchased by a customer. The resulting outcome: product discovery; increased customer engagement; and improved revenue. With this solution, companies open an opportunity to optimize their customer engagement activities, starting with online experiences: offer a website landing page specifically tailored to logged-in users; a digital app connecting customers to personalized offers; promotional emails personalized based on purchase history; and much more…
A ready-made project with clear flow zones turns transactional data into actionable insights to best match the right customers with the right products.
Use the Dataiku App to configure the solution. It includes connection settings, allows you to detail the data you are connecting to the project, allows you to frame main parameters associated with the data batch and choose how you want to optimize the Machine Learning model - Random Forest.
Audit and validate the behavior of your Product Recommendations model. Select "users" from your operational system and put their historical purchases in perspective with the recommendations coming from the machine learning model.
View your item distribution and corresponding customer interactions, along with the value (total revenue) generated.
Get a quick and shareable view of how often your existing customers purchase your items (distinct by user), along with the total and average number of interactions that occurred at a given time period.