Customer analytics

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.

Uses of Customer Analytics
Retail

Gathering customer data and implementing it into some type of application that can enhance insight and the decision making process is a common application of customer analytics used by retailers.

Community

Municipalities utilize customer analytics in an effort to lure retailers to their cities. Using psychographic variables, communities can be segmented based on attributes like personality, values, interests, and lifestyle. Using this information, communities can approach retailers that match their community’s profile.

Customer Relationship Management

Analytical Customer Relationship Management, commonly abbreviated as CRM, enables measurement of and prediction from customer data to provide a 360° view of the client.

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 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 with confidence because every decision is 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.

The Future of Customer Analytics
By continuing to improve customer prediction techniques it will become a necessity rather than a convenient commodity for businesses to use customer analytics. With this valuable information there is an opportunity to fine-tune retail operations and store manager decisions. Rapid decision making will increase in speed and effectiveness in the future as tools and information become more easily accessible. The possibilities are still emerging, but applications in political races, jury selection, and developing clinical trail communities are areas that customer analytics could be used in the future.