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. Marketing provides services in order 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 a very important role in the prediction of customer behavior today.[1]

Uses

Retail

Although until recently over 90% of retailers had limited visibility on their customers, with increasing investments in loyalty programs, customer tracking solutions and market research, this industry started increasing use of customer analytics in decisions ranging from product, promotion, price and distribution management.

Finance

Banks, insurance companies and pension funds make use of customer analytics in understanding customer lifetime value, identifying below-zero customers which are estimated to be around 30% of customer base, increasing cross-sales, managing customer attrition as well as migrating customers to lower cost channels in a targeted manner.

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.

See also

References

  1. ↑ Kioumarsi et al., 2009

Further readling

External links

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