Saturday, January 19, 2013

CUSTOMER ANALYTICS: Understanding Customers



                                                  
CUSTOMER ANALYTICS: Understanding Customers


What is 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. Customer analytics help to turn data into something which could be really used. It helps in finding out the hidden value in an organizations data. It also gives the organization the customer insight necessary to deliver offers that are anticipated, relevant and timely.

Why customer analytics is necessary?

In today’s era customers are more empowered and connected than ever. Customers have access to information anywhere, anytime. They can easily get information on different things like- what to buy, what offers are available, etc. So, if an organization has a deeper knowledge about customer’s behavior, his buying habits and preferences, it can more accurately predict what he will be buying in near future. This helps in providing relevant offers which attracts customers.
 


So, the top three reasons for why customer analytics is necessary are:
·      
 1.  Need to increase customer loyalty/retention.
·       2. Need to increase profitability.
·       3. Need to predict what customer’s desire for new product.

Data Mining

There are two types of data mining used in customer analytics:
·       
          Predictive models: These models use previous customer interactions to predict the future events. By taking everything into account like, credit card purchases, magazines subscriptions, web events a customer’s profile can be created which can be used to plan marketing.
·        Segmentation technique: It places the customers with similar behaviors and attributes into distinct groups. According to an article by Jill Griffin for Cisco Systems, traditional segmentation focuses on identifying customer groups based on demographics and attributes such as attitude and psychological profiles. Value-based segmentation, on the other hand, looks at groups of customers in terms of the revenue they generate and the costs of establishing and maintaining relationships with them. Segmentation helps a company to identify a particular group of customers easily.

Difficulties in managing Customer Analytics

In the "State of Customer Analytics 2012" report, Forrester customer intelligence analyst Srividya Sridharan concludes that lack of data management, integration, and quality are the biggest inhibitors to making better use of customer analytics. A full 54 percent of companies surveyed have difficulty managing and integrating data from the many varied sources, while 50 percent are concerned about consistent data quality.
Companies also grapple with assembling the right type of analytics professionals, communicating the results of the analysis to relevant colleagues, performing real-time analytics and making insights available during customer interactions, protecting data and addressing privacy concerns, and keeping pace with the velocity of data generation.

Present and Future of Customer Analytics:

According to the report, predictive analytics as a growing trend, with 40 percent of firms claiming to use it. Conversely, 70 percent have been using descriptive analytics and business intelligence reporting for more than 10 years. By continuing to improve customer prediction techniques it will become a necessity rather than a convenient commodity for businesses to use customer analytics.[5]
At present customer analytics is vastly used by sectors like retail, finance, health-care etc. May be in future we can see its application in political races, jury selection, and developing clinical trial communities.