Customer data comes from various sources. However, most customer issues reach the company through contact centers. They act as a nerve center of customer interactions for organizations. So, it is safe to say that the effectiveness and efficiency of a contact center largely defines the customer experience for the organization.
As they act as ground-zero, or the first approach point for customer issues, companies need to be able to analyze the customer calls and integrate them with product and customer demographic data to yield crucial insights. Without the analytics of contact center customer data, businesses would not be able to earn customer loyalty, cross sell, upsell or take the necessary actions to stop a customer from moving to another brand. By analyzing contact center customer data, companies can:
- Predict better and understand the parameters which affect customer loyalty
- Identify products which have higher profitability
- Design accurate campaign strategies for specific customer targeting
- Better leverage the different messaging and communication platforms
Thus, mining contact center data can improve the overall customer experience as well as customer loyalty.
A 2014 Market Survey of Teradata found that, out of the 1,506 respondents, 92 percent agree that integrating data across teams can improve customer service. Forty-three percent of marketers say they now control their company’s customer data (up from 34 percent in 2013)—and a vast majority (83 percent) say they take an omni-channel approach to reaching customers.
Difficulties in Integrating Customer Data
The second important aspect in analytics and predictive churn management is integration of data from contacts across channels like CRM, call centers, customer survey, social media, etc. It is very important for companies to gain a unified, 360-degree view of their customers. However, this is easier said than done. Some factors that make it difficult to build a 360-degree customer view are:
- Too many customer interaction points, and information from the different interactions are not usually stored together. The data could be anywhere in the organization—digital channels, CRM systems, customer support systems, call-centers, and so on.
- Diverse ways of customer identification. Each department creates their own customer ID formats, leading to confusion.
- The problem of Big Data. Companies have huge amounts of data, amounting to almost 100 million usage events in a single day! Storing this data, assigning individual subscriptions, indexing or reporting of this data is a huge problem.
Strategy for Integrating Customer Data
Integrating customer data from various sources would bring together the different pieces of the jigsaw puzzle and help in getting a clearer picture of the customer. Thus, through data integration, we get a holistic view of the customer to base the business strategy on. This in turn enables the organization to enhance sales by focusing on the most profitable customers and designing products, services and solutions which are in touch with the customer needs and wants, thus leading to increased customer loyalty and better marketing strategies.
While on the drive for customer data integration, there are a few key best practices to be followed.
- The first step is to understand the data, clean and format it. The data set commonalities need to be identified, followed with analysis unit identification (location, household, product groups individuals, customer groups, etc.). The analysis unit is dependent on the business objective.
- While integrating data, gaps or holes are found in certain data elements. The level of accuracy needs to be identified. This is followed by data mining, stating the hypothesis and analyzing the data.
The focus should be on unlocking customer-centric information. The analysis should be around the needs of the customer, what is important for the customer and why, the customer decision making map, basis for customer interaction and so on. Customer intelligence managers should analyze varied data sources to answer such questions.
Clearly, to complete the picture and unlock the full potential of analytics, the multi-channel customer information has to be intelligently integrated with the text analytics. The customer service agents need to be empowered with all the information on their screens working to provide inputs, suggestions and insight into what the customer is likely to purchase. This would help companies in effectively dealing with customer issues and planning more customer-centric strategies.