Data Mining 101 by Debbie Major for CMA

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Data Mining 101

The more we know about our customers/prospects, the better we can tailor our products/service to meet their needs. It’s this basic premise that makes data mining and data analytics fundamental to the success of any organization.

Data mining is the process of analyzing data from varying perspectives, and leveraging that information to cut organizational costs, increase revenue or both. Your company collects vast amounts of data in different formats and different databases. The downfall of many organizations comes when the data is not being mined in a useful or meaningful matter that translates back to revenues or greater efficiencies for your company.

I recently participated in a webinar by analytics service provider Boire Filler Group – they suggest data can be broken out into four silos: a) interaction data – how are your customers reacting to you, b) attitudinal data – from surveys you may have done, c) descriptive data – such as census data and how long a customer has been with you, and d) behavioural data.

A good way for organizations to get started with data mining is by first completing a data discovery or a data matrix – this can help you identify the gaps and opportunities that are missing in your data. For example, you may discover that ‘gender’ is not something you’re currently collecting data on, which could help you improve your product marketing. Your gap study may also help identify areas where data infrastructure upgrades need to take place first in order to collect the information most effectively.

Once you have all the available data, there’s value in creating a roadmap to harness this information in a way that helps you make key business decisions. According to Boire Filler Group, here are the steps to take leading to the roadmap:

  1. Prepare: Understand your data, understand your challenges, define your objectives and finalize your data discovery requirements.
  2. Data Audit: Review your existing data, access the accuracy of your data, identify gaps in your data, and look at initial frequency reports and any variable creation possibilities that exist. The challenge will be to consolidate all your data into one view and you may need to seek an expert in this field. You would look at a sample diagnostics of your database and a frequency distribution report of your database.
  3. Preliminary Analysis: Once you have produced reports on key indicators of your data, you can start to make some measurement analysis based on the information – the basic profiling and basic segmentation of your data. You would also identify areas for further deeper analysis as well as any gaps or opportunities for maximizing your business and assisting in making better decisions.
  4. Roadmap: Now you can create a roadmap or a basic understanding of your data. With this in hand, you will be positioned to execute marketing campaigns that can be tracked, measured and modified accordingly.

Debbie Major


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