Customer Analytics
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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 an important role in the prediction of customer behavior.[1]


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.[] The most obvious use of customer analytics in retail today is the development of personalized communications and offers and/or different marketing programs by segment.[] Additional reasons set forth by Bain & Co. include: prioritizing product development efforts, designing distribution strategies and determining product pricing.[2] Demographic, lifestyle, preference, loyalty data, behavior, shopper value and predictive behavior data points are key to the success of customer analytics.[]
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.
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 behaviour

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.

See also


  1. ^ Kioumarsi et al., 2009
  2. ^ Bain & Co.[clarification needed]

Further reading

  • Kioumarsi, H., Khorshidi, K.J., Yahaya, Z.S., Van Cutsem, I., Zarafat, M., Rahman, W.A. (2009). Customer Satisfaction: The Case of Fresh Meat Eating Quality Preferences and the USDA Yield Grade Standard. Int'l Journal of Arts & Sciences (IJAS) Conference.

External links

  This article uses material from the Wikipedia page available here. It is released under the Creative Commons Attribution-Share-Alike License 3.0.

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