How do you decide which products to recommend for your customers? Chances are, you’re using one of two approaches.
Approach One: Product and category owners decide which products they think are good matches. They manually enter this into a recommendation system.
Approach Two: You have a product recommendation solution that is basically a black box. It has a few nobs and checkboxes, but how it decides to recommend items is a mystery. Your A/B tests showed a slight bump when you purchased the system, and the vendor says it’s the best solution around!
In case you missed it, last week we hosted a webinar on our product recommendation solution, which retailers use for a data-driven, customer-centric approach to product recommendations. One large retailer told us they expected a Year One revenue boost of several million dollars after integrating this approach to recommendations into their email marketing campaigns.
The solution lets you combine both in-store and online purchase behavior for a more robust view of your customers. You can leverage tried-and-true analytic techniques such as collaborative filtering and FPgrowth, cutting edge techniques such as pSALSA, which is used by social networking giants such as Twitter, or combinations thereof. Our data scientists are happy to provide guidance on which techniques best apply to your specific use cases.
With Teradata Aster AppCenter, you can generate the most up-to-date recommendations with the click of a button. And we can integrate with the delivery channel of your choice, whether that’s an email marketing solution, website, point-of-sale system or some other solution.
If you haven’t already, I encourage you to watch the on-demand replay of the webinar. If you don’t have half an hour, this two-page overview should sum everything up for you. And if you have any questions, feel free to email me at email@example.com.