I recently read a very interesting post, How Machine Learning can Improve Customer Interaction, that does a great job of listing different ways you can leverage machine learning to communicate better with your customers. The ideas include:

Machine learning

  • A personalized approach when you visit a website. When you are on an e-commerce site or using a search engine, the host collects rich information on your behavior. Machine learning analyzes the data and transforms the website into something geared to the individual customer. Machine learning then will control what you see, what appears in a search bar, how the site communicates with you, to best meet your individual needs.
  • Making recommendations. Making recommendations relevant for the user was one of the first major consumer applications of machine learning. Virtually everyone has experienced Amazon’s recommendations, when you make a purchase it recommends products likely to resonate (and almost everyone has taken advantage of these recommendations). Automated personalization with machine learning takes information about the shopper, refines those recommendations and tailors them specifically to the individual shopper. As the article points out, “it is like having a salesperson with the customer the whole time, pointing out what products he or she thinks are right up the customer’s alley.”

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A key to predicting and effectively using customer lifetime value (LTV) is to take a long-term view of your data and not just rely on the first month or even first few days. Many marketers will draw conclusions about a new product launch, a new feature or a unique customer cohort based on the initial data they generate. While you cannot wait months or years to make crucial business decisions, understand that these predictions are less reliable and thus making decisions based on this data is problematic.

The challenges

While intuitively more data is always better, there are challenges involved in looking back over a long period. First among these challenges is customer attribution. If you are determining the value of a specific growth channel, do you credit the lifetime spend of a user to the channel you used to acquire them initially or do you attribute the revenue to a channel (Facebook feed, email, A2U notification, etc.,) that brought the user back after a long period of inactivity.

The second issue is the sheer quantity of data. If you have millions of customers or players and years of data, it becomes quite a challenge to process all of that data. You may have multiple interactions with that user every day, literally for years. Think of how you interact with Amazon and consider they track all the products you look at, how often you visit, what you purchase, what you purchase instead, etc. You need the software, data warehousing and systems so that you can actually analyze this data quickly. Continue Reading…