Customer analytics is a process by which data from customer behavior is used to help make key business decisions via market segmentation and predictive analytics. This information is used by businesses for direct marketing, site selection, and customer relationship management. Marketing provides services to satisfy customers. With that in mind, the productive system is considered from its beginning at the production level, to the end of the cycle at the consumer. Customer analytics plays an important role in the prediction of customer behavior.

Uses

Predicting customer behavior

Forecasting buying habits and lifestyle preferences is a process of data mining and analysis. This information consists of many aspects like credit card purchases, magazine subscriptions, loyalty card membership, surveys, and voter registration. Using these categories, consumer profiles can be created for any organization’s most profitable customers. When many of these potential customers are aggregated in a single area it indicates a fertile location for the business to situate. Using a drive time analysis, it is also possible to predict how far a given customer will drive to a particular location. Combining these sources of information, a dollar value can be placed on each household within a trade area detailing the likelihood that household will be worth to a company. Through customer analytics, companies can make decisions based on facts and objective data.

Data mining

There are two types of categories of data mining. Predictive models use previous customer interactions to predict future events while segmentation techniques are used to place customers with similar behaviors and attributes into distinct groups. This grouping can help marketers to optimize their campaign management and targeting processes.

Retail uses

In retail, companies can keep detailed records of every transaction made allowing them to better understand customer behavior in store. Data mining can be practically applied through performing basket analysis, sales forecasting, database marketing, and merchandising planning and allocation. Basket analysis can show what items are commonly bought together. Sales forecasting shows time based patterns that can predict when a customer is most likely to buy a specific kind of item. Database marketing uses customer profile for effective promotions. Merchandising planning and allocation uses data to allow retailers to examine store patterns in locations that are demographically similar to improve planning and allocation as well as create store layouts.