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Your company is deeply committed to better serving its customers. Because of this, you and your team built out a survey. This lets you quickly get customer feedback and respond accordingly. While building out the survey, you decided that one of the best ways to get customer feedback would be to use an open-ended question. This all sounds great. The problem? Open-ended questions have actually become annoying for you and your company to use. You certainly don’t feel like you’re getting the full benefit from them. Text analytics can help solve this problem.

Over the past two weeks we discussed two techniques that help to find patterns within data – clustering and association rules analysis. Clustering helps to split data into groups that are similar to each other. Association rules help to find items that are commonly grouped together. On their own, these techniques are powerful and could help any business to make better strategic decisions. While these techniques help you to mine your data – to understand the patterns within it – they fail to make any predictions about what will happen in the future. That’s where the last set of techniques come into play, aptly named predictive analysis.

Predictive analytics and data mining can be used for many different purposes. Last week we discussed cluster analyses, a group of techniques that can help businesses to better identify their customer segments (among many other things). This week we move on to a group of techniques that would be of interest for companies that sell many products, especially those in a retail environment: association rules.

Last week we discussed that there are three broad buckets of predictive analytics: clustering, prediction, and association. Using the techniques in each of these buckets allows for organizations to gain deep insights into the work that they do. Each bucket is a piece of the puzzle in building a model for a company.  This week, we’ll discuss the first of those buckets: clustering.