Data Mining Definition and Methods

Most companies find themselves collecting tons of data from their customers. However, collecting the data is only half the battle. What’s more important is what you do with it. That is where data mining comes in.

What is “Data Mining”?

In short, data mining is sifting through the data you have collected to find trends that weren’t immediately obvious before. This can reveal buying habit or preferences that may aid you in better marketing or catering to that customer.

In fact, it seems the only thing hindering complete data mining is technological restrictions. However, it isn’t necessarily how much you know, but how you use this new information.

So How Do I Mine for Data?

What types of information you glean correlates to what method was used to obtain the data in the first place. Surveys, feedback analysis, demographic information, and other methods can help extract particular types of information.

There are several different methods for mining data. These terms may sound intimidating; however, it is likely you have encountered or even used some of these methods already.

Affinity Analysis

This is one method that most retailers have used even if they aren’t aware of it. Affinity analysis seeks to examine what a customer has purchased and use that information to predict what they will purchase in the future.

Online retailers use this information to recommend products related to the items in a shopping cart, or recommending similar products that other customers have also purchased. Often, they will also recommend accessories or add-on items as well.

Offline retailers use this information to improve store layout. Purchasing data can also aid in training sales staff on common upsell and cross-selling opportunities.

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Sales Forecasting

This method focuses on predicting not what a customer has purchased, but when they are likely to purchase again. This is especially useful for retailers who sell consumable goods. This method looks at the available customers in your geographic area and the amount of competition as well.

Customer Loyalty

This is less of a method and more of a practice that leads to gathering customer information. Customer loyalty programs offer a benefit to the customer in the form of points, promotions, discounts, or other incentives to frequent your store.

At the same time, loyalty programs also collect customer contact information as well as records purchasing behavior. This information can then be used to more specifically market specific products towards these customers.

Customer Segmentation

At some point, you’ll want to break down your customers into specific segments that are more highly focused. Because loyalty programs can gather key demographic data points, they are especially useful in segmenting your customers into smaller groups.

If you collect enough information, these groups can be broken down into specific segments, such as customers who respond to promotional or discount offers, those who are most likely to shop on a weekend, or those who are more prone to a specific brand’s products.

This information can be further used to specifically target these customers when appropriate. If, for example, you know that X number of customers will respond to a specific promotional offering you can schedule and forecast the effectiveness of that promotion.

Satisfaction Data

Customers are often willing to share their thoughts and can provide valuable feedback. From analyzing this feedback, you can obtain valuable insight into your customer’s buyer journey. What went right? What went wrong? What would they like to see done differently?

Data mining is typically thought of as a broadly scientific approach to optimizing sales. Often, it is a approach from a technical mindset which produces statistical patterns without regard to the application of those patterns.

Satisfaction data in the form of customer feedback is a more direct, personal method of obtaining specific information directly informed by the customer’s experience. This information can be used to make improvements to your store layout, customer service policies, product offerings, and more.

As part of any customer relationship management plan, collecting satisfaction data in the form of customer feedback is crucial to any retailer. Not only does it allow customers to share positive information, it offers a platform for honest critique as well as suggestions for improvement.

Retailers who are thinking of investing in data mining would be wise to explore feedback management as part of their strategy. Statistical analysis may reveal patterns and aid in sales forecasting but honest feedback from real customers can reveal a lot more.