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Marketing to (and with) algorithms with EDM

Ian, my fellow blogger here at edmblog.com, pointed me to this article You Must Market To Algorithms, Not Just People. The article nicely summarized the growing role of algorithms in marketing and, as predictive analytics and even rules can be considered a form of algorithm, it made me want to talk about enterprise decision management or EDM in this context. Let's start with the key concept:

As more human behaviors emit trails of digital residue, the more opportunities reside for algorithms to harness those human-induced data and become information intermediaries

Absolutely right. And these data are available in such volume that reporting on them is not going to help anyone - you have to build insight from the data so that you can use it, not just report on it with BI. This means building predictive analytic models based on the data that can be embedded into your operations - algorithms in other words. In reality you must also combine rules - about the user's preferences so as to maximize the customer's influence on decisions and about policy and regulation to ensure compliance. This is particularly true for any business subject to the Long Tail. Automating decisions in this way can let you improve the customer experience and scale 1:1, personalized communication to thousands or millions of customers.

As Max says, "all behaviors .. create halos of metadata, which algorithms process, mediate and disperse to others" and you need to account for all this information in your decisioning. Max gave some examples of algorithms and I thought it would be fun to show how EDM-like some of them were:

  • Restaurant recommendation
    Well you could use predictive algorithms to segment people by the kind of restaurants they like/use and to build recommendations by comparing to other people. The customer can set rules for price, location etc as well and the combination comes up with restaurants.
  • Real-estate
    Looks like this one was just rules - rules from the customer about properties in which they are interested
  • Travel
    More preference rules plus models predicting occupancy and handling dynamic pricing as a result
  • Music playlists
    Rules and predictions for like and dislike
  • Fraud
    Rules and analytics, a classic EDM one.

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