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» Make custom content recommendations: By creating a customer profile,
the engine can see what content your customer has already read and select
other items that will move them towards a possible purchase.

» Develop tailored offers: The engine can determine which offers have already been
selected (or heavily reviewed) and make new recommendations based on them.
» Provide discounts: Discounts are always popular with customers. When the
engine can provide them for the products they use the most, they will use
them and deepen their satisfaction with your company.
» Customize ads: Ads that reflect the customer’s preferences are not usually
considered annoying, but there needs to be a balance. The engine can
determine which ads to display.

If you’d like to look at some personalized recommendation engines, you may want
to start with a few that the 2019 Gartner report, “Magic Quadrant for Personalized
Engines,” chose as the top three leaders providing standalone personalization
engines. They are:

» Evergage (https://www.evergage.com/), shown in Figure 25-1, uses
machine learning to create customer profiles to predict outcomes. It works
well for teams and those in retail, technology, and finance.

FIGURE 25-1:
Evergage
home page.

CHAPTER 25 Changing Tactics and Metrics 369