Data-Driven Product Recommendations With FPGrowth

Teradata Guided Analytics
Enthusiast

What analytic techniques are you using to make data-driven product recommendations this holiday season? I just published this post on FPGrowth, or frequent pattern growth, on the Learn Aster blog. This is a technique retailers use to identify multi-item associations. Think, if I purchase Item A, then I may be most likely to purchase Item C. But if I purchase Items A and B, then I may have a higher likelihood of purchasing Item D. FPGrowth allows you to look at different basket sizes to identify these patterns.

We are increasingly seeing retailers use FPGrowth in Aster-based next-best-offers with great success, so we've incorporated this technique into our Product Recommender solution. I wanted to make sure I shared the post here. Feel free to email me at ryan.garrett@teradata.com if you'd like to learn more.

Additional resources:

  • On-demand webinar and demo: Increase Conversions - And Revenue - With Data-Driven Product Recommendations
  • Datasheet: Market Basket & Product Recommendations - Make Data-Driven, Customer-Centric Recommendations
  • Blog Q&A: Teradata Aster's Product Recommender Solution - What Is It, And Why Does It Matter?