Customer analytics
Customer analytics is a process by which data from
Uses
- Retail
- Although until recently over 90% of retailers had limited visibility on their customers,[2] 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.[citation needed] 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.[citation needed] Additional reasons set forth by Bain & Co. include: prioritizing product development efforts, designing distribution strategies and determining product pricing.[3] Demographic, lifestyle, preference, loyalty data, behavior, shopper value and predictive behavior data points are key to the success of customer analytics.[citation needed]
- Retail management
- Companies can use data about customers to restructure retail management. This restructuring using data often occurs in dynamic scheduling and worker evaluations. Through dynamic scheduling, companies optimize staffing through predictive scheduling software based on predictive customer traffic. Worker schedules can be adjusted in response to updated forecasts at short notice. Customer analytics allows retail companies to evaluate workers by comparing daily sales to daily traffic in a store. The use of customer analytics data affects the management of retail workers in a phenomenon known as refractive surveillance. The model of refractive surveillance describes how the collection of information on one group can affect and allow for the control of an entirely different group.
- Criticisms of use
- As retail technologies become more data driven, use of customer analytics use has raised criticisms specifically in how they affect the retail worker. Data driven staffing algorithms can lead to irregular working schedules because they can change on short notice to adapt to predicted traffic. Data driven assessment of sales can also be misleading as daily traffic counters do not accurately distinguish between customers and staff and cannot accurately account for workers’ breaks.[4]
- 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 psychographicvariables, 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
Data mining
There are two types of categories of
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. [5]
See also
- Buyer decision processes
- Business analytics
- Customer intelligence
- Data warehouse
- Psychographics
- Market research
- Mattersight Corporation
- Customer privacy
- Customer data management
References
- ^ Kioumarsi et al., 2009
- ^ "The futre of retail supply chains". www.mckinsey.com. Retrieved 22 November 2018.
- ^ Bain & Co.[clarification needed]
- ^ Levy, Barocas, Karen, Solon (2018). "Refractive Surveillance: Monitoring Customers to Manage Workers". International Journal of Communication. 12: 2–10.
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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.