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.
So it’s essential for companies to analyze the customer interactions feedback, and integrate them with traditional data sources such as transactional data, demographics, etc. to yield crucial insights. By analyzing the contact center customer data, companies can:
- Predict better and understand the parameters which affect customer loyalty
- Prevent customers from moving to the competition
- Enhance customer service through better agent training
- Address root causes of customer churn and effectively retain existing customers
- Improve overall customer experience and satisfaction levels
Difficulties in Integrating Customer Data
It’s essential for companies to gain a unified, 360-degree view of their customers. For that, successful integration of data from contacts across channels like CRM, call centers, customer surveys, etc. is extremely important. This is easier said than done. Businesses often face a myriad of data integration difficulties. Here are some factors that make it difficult to build a 360-degree customer view of your customers:
- 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.
In terms of openness, here is a key finding that surfaced: Data integration remains a great challenge. Only 36% of executives say they have attained real-time, highly integrated capabilities across all the customer channels within their enterprises.
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.
As today’s businesses have understood the importance of unlocking the customer-centric information in this customer-led market, the analysis should be around the needs of the customer, why it is important for the customer, the customer decision making map, bases for customer interaction, and so on. Customer intelligence managers should analyze varied data sources to answer such questions, or else have a technology-driven customer intelligence solution that can help businesses convert customer data from any data source into actionable business insights.
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.