Customer analytics is a process by which

data Data ( , ) are a collection of discrete or continuous values that convey information, describing the quantity, quality, fact, statistics, other basic units of meaning, or simply sequences of symbols that may be further interpreted for ...

from

customer behavior Consumer behaviour is the study of individuals, groups, or organisations and all activities associated with the Purchasing, purchase, Utility, use and disposal of goods and services. It encompasses how the consumer's emotions, Attitude (psy ...

is used to help make key business decisions via

market segment In marketing, market segmentation or customer segmentation is the process of dividing a consumer or business market into meaningful sub-groups of current or potential customers (or consumers) known as ''segments''. Its purpose is to identify pr ...

ation and

predictive analytics Predictive analytics encompasses a variety of Statistics, statistical techniques from data mining, Predictive modelling, predictive modeling, and machine learning that analyze current and historical facts to make predictions about future or other ...

. This information is used by businesses for

direct marketing Direct marketing is a form of communicating an offer, where organizations communicate directly to a Target market, pre-selected customer and supply a method for a direct response. Among practitioners, it is also known as ''direct response ...

site selection Site selection indicates the practice of new facility location, both for business and government. Site selection involves measuring the needs of a new project against the merits of potential locations. The practice came of age during the 20th centur ...

, and

customer relationship management Customer relationship management (CRM) is a strategic process that organizations use to manage, analyze, and improve their interactions with customers. By leveraging data-driven insights, CRM helps businesses optimize communication, enhance cus ...

. 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

;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. 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. Demographic, lifestyle, preference, loyalty data, behavior, shopper value and predictive behavior data points are key to the success of customer analytics. ;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, meaning that 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. ;Finance:Banks, insurance companies and pension funds make use of customer analytics in understanding

customer lifetime value In marketing, customer lifetime value (CLV or often CLTV), lifetime customer value (LCV), or life-time value (LTV) is a prognostication of the net profit contributed to the whole future relationship with a customer. The prediction model can have ...

, identifying below-zero customers (that is a segment of the customer base that costs more than they are worth) which are estimated to be around 30% of customer base, increasing cross-sales, managing

customer attrition Customer attrition, also known as customer churn, customer turnover, or customer defection, is the loss of clients or customers. Companies often use customer attrition analysis and customer attrition rates as one of their key business metrics (alo ...

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 Psychographics is defined as "market research or statistics classifying population groups according to psychological variables" The term psychographics is derived from the words "psychological" and "demographics" Two common approaches to psychogr ...

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

(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 subscription The subscription business model is a business model in which a customer must pay a recurring price at regular intervals for access to a Product (business), product or Service (business), service. The model Publication by subscription, was pioneer ...

loyalty card A loyalty program or rewards program is a marketing strategy designed to encourage customers to continue to shop at or use the services of one or more businesses associated with the program. Single-company vs. coalition programs Loyalty prog ...

membership, surveys, and

voter registration In electoral systems, voter registration (or enrollment) is the requirement that a person otherwise Suffrage, eligible to Voting, vote must register (or enroll) on an electoral roll, which is usually a prerequisite for being entitled or permitted ...

. 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 Drive time is the daypart in which radio broadcasters can reach the most people who listen to car radios while driving, usually to and from work, or on public transportation. Drive-time periods are when the number of radio listeners in this c ...

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 Data mining is the process of extracting and finding patterns in massive data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and ...

. 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.

References

External links

Wharton Customer Analytics Initiative
Marketing analytics

Analytics Analytics is the systematic computational analysis of data or statistics. It is used for the discovery, interpretation, and communication of meaningful patterns in data, which also falls under and directly relates to the umbrella term, data sc ...

Business intelligence terms Applied data mining Market research