Introduction to Data Mining for Associations
The TV show, Parks and Recreation, recently had an episode where a huge tech company used data mining to the extreme-- knowing what would be the perfect present for every person in the town. In the episode it caused a huge stir due to privacy concerns. And I am sure most of you remember the incident where Target predicted a girl’s pregnancy before her family even knew. Although data mining is getting a bad rap lately, when done in a way that respects the privacy of your customers, it can provide valuable information for your association.
What is it?
Data Mining is just using computational power to comb through large amounts of data in order to find patterns and uncover meaningful information.
What does it involve?
- Classification - This takes your data and determines what bucket it goes to. Suppose you ran a model on your data to determine who is likely to renew or who is likely to drop their membership. Classification would then take this model and break out your members and put them in buckets depending on how likely they are to renew.
- Factor Analysis - A useful tool for investigating relationships within complex areas such as socioeconomic status, dietary patterns, or psychological scales. It allows researchers to investigate concepts that are not easily measured directly.
- Regression analysis – is used to understand which among the independent variables (e.g. job sector, meetings attended, etc.) are related to the dependent variable (e.g. member retention), and to explore the forms of these relationships. Regression analysis is also widely used for predicting and forecasting.
- Segmentation – Most of us do segmentation all the time. You can segment your organizations by number of employees or individuals who have similar job titles.
- Association – If you have been to Amazon you have seen how association works. Let’s say I buy a NutriBullet and a FitBit, an algorithm runs to see other people who have bought those items and what else they buy. Then Amazon will recommend those products to me.
- Sequence Analysis - This type of analysis looks for patterns of values that happen in sequence. For example, if you were looking how your customers interact with your website, you can see if accessing web pages in a certain sequence means they are more likely to purchase products.
How do I do it?
Now that you understand all the different types of analysis that can be performed while data mining, the next step is to understand how you can get started.
- Define the problem you want to solve- As smart as most computers are, they won’t be able to answer a question when none is asked. First determine what question you want to ask, for example, "Who is most at risk for not renewing membership?" or "What is the profile of our optimal customer?"
- Determine what analysis is appropriate for your question – Now that you have defined what you need to ask, you need to determine which of the 6 analysis types above will best help you answer your question.
- Prepare your data – Now you will need to find and clean your data. You can only analyze data you have access to and the answers you will get from analysis will only be good if your data is clean.
- Training – Just like a person, your model will need training as well. Think of this like giving a new employee a tour when they first start. They meet all the staff and get to find out how your association works and what everyone does.
- Testing - Once you run an analysis on your data and a model is created, you then make sure that the model is accurate. The best way to do this is by using historical data and run it through the model to see if it accurately predicts what has already happened.
Data mining is an advanced part of Business Intelligence and should be an end goal for any association analytics initiative. It allows you to take your most valuable asset, data, and use it to help you not only in the present but also help you plan for your association’s future.