Binary-code-04012013Lately you may have seen buzz words like “data science”, “big data” and “algorithms” making headlines on sites like TechCrunch, PandoDaily and others, where they are being actively debated and questioned. While a surprise to some, the concept of data science is nothing new and has been a backbone of personalization and other machine learning technologies for years. But for retail companies, the concept of “big data” and more importantly what to do with it, is a puzzling business problem that almost all C-level execs are looking to solve.

Successful personalization relies on two important things: big data and the algorithms which allow you monetize the data. Big data for personalization comes from a variety of sources from implicit behaviors like clicks, search terms and purchases and explicit data provided by users such as zip code or email address.

As companies realize the value of big data and personalization, they may start building their own data science teams to exploit and monetize their data. These teams tend to focus on the areas with the highest density and clarity of data, which tend to be explicit data provided by users. Baynote algorithms focus on the implicit data generated by users, so we see a tremendous opportunity to complement a variety of personalization approaches with our technology. Here is a breakdown:

  1. No data science team, no problem. Baynote has 6 Ph.D’s on staff with a powerful and proven modeling and real-time deployment environment. We can help by modeling based on implicit data and using our models to drive value for your sites and apps. This kind of turnkey approach is what we have been doing for years.
  2. Building or existing data science team. Use Baynote as a complement. We speak your language and we have implicit data and models that can help augment what your team is doing. We can also use our patented intent-based approach to personalize for anonymous and even known users while your teams may want to focus on loyalty program members or frequent buyers.

Whatever your approach, we are excited to work alongside your merchandising and data science teams to deliver results.