Marketers realize that every customer and their journey is unique and personal. A touchpoint that is ideal for one customer may be ignored by another. The challenge for the marketer is to find the right next best action ultimately resulting in the customer taking the desired action (e.g. purchase).

Machine learning can be used to identify patterns in customer data based on browsing behavior, prior purchases, demographics, household data and more to determine customer interests and intent. One such pattern is the next best action or touchpoint that is likely to produce the desired outcome based on successful outcomes from interactions with other customers combined with the individual’s data. Using this system marketing campaigns can be hyper-personalized to result in the highest return of investment.

Easy to use automated machine learning allows marketers to perform modeling of next best action models without the need for a data science degree. By automating all aspects of machine learning model training the marketer can trust the model to be unbiased and high performing. Further, the marketer gets insights into what drives customer actions by investigating built-in graphics displaying what actions are important for the given model.