Data analytics, borne out of the need to make sense of big data that contact centers collect during their everyday operations, is completely changing the way brands deliver customer service.
Brands are always on the hunt for new ways to add value to the services they bring to customers. As the consumers’ standards of good customer service continue to rise, brands must always be, or at least try to be, one step ahead of upcoming trends. Keeping up with the demands of the market means innovating constantly and creating unique strategies to stand out.
Thanks to the emergence of data analytics, brands now have a way to personalize the customer experience like never before.
How Exactly Does it Work?
Analytics is a subset of data science. Simply put, it involves examining raw data in order to organize and interpret them, and draw relevant conclusions. The main purpose, therefore, is to allow organizations to make wise business decisions backed by insights generated from their customers, employees, and other constituents.
This process is, for the most part, aided by technology. Tools and computer software automate a large chunk of the analysis to do away with human errors. This also makes the entire process easier for data workers, especially as most organizations (90% to be exact) now invest heavily on big data analysis.
When applied to the call center setting, data analytics helps managers deliver personalized services that target the preferences of their customers. It takes up many forms, including the following.
The focus of speech analytics is on recorded calls. It entails analyzing conversations in order to gather information about customers and the issues being discussed. As these tools are often equipped with speech or voice recognition features, they can also identify spoken words and phrases, analyze the tone and voice of the customer, and therefore recognize emotions.
Speech data provides customer insights that are simply not available from other sources. It helps in the identification of the causes of customer dissatisfaction and opportunities to improve compliance, operational effectiveness, and agent performance.
Text analytics zones in on the written language—documents, emails, web chats, and even social media comments. During analysis, the tool assigns numerical values to words and phrases. Then, data mining functions are carried out to identify patterns and relationships among the data sets.
Brands can enhance customer satisfaction via product impressions, find product problems, conduct market research, and monitor brand reputation, among other things, by recognizing trends and patterns with text analytics.
Unlike the first two forms of data analytics discussed, which are both customer-oriented, desktop analytics is agent-focused. Using this technique, organizations can monitor, capture, and analyze desktop-based activities as well as workflows. The tool detects keystrokes to understand data entry and keep track of the applications being accessed.
This can help managers can measure agents’ compliance to protocols in a contact center, training needs, performance issues, and bottlenecks in processes.
Cross-channel analytics, the process in which data sets from multiple channels are consolidated, is necessary to make omnichannel customer service work. Most customer relationship management tools make use of this technology usually by combining speech, text, and desktop analytics features. It’s used to identify and evaluate various customer support platforms and how customers use them to interact with brands.
Cross-channel analytics provides insights into why customers convert and allows you to convert more of them.
To provide customers the option to solve issues on their own, brands are starting to create self-service portals—online spaces that users can access to find the answers they’re looking for. Self-service analytics tools analyze and evaluate the customer experience in this channel to determine glitches, pain points, and other problems.
This is perhaps the most advanced form of data analytics in this list. Predictive analytics tools leverage several techniques including data mining, machine learning, and artificial intelligence to predict future events, such as customers’ purchasing behavior. Customer support providers can also use such tools to identify the most effective channels or approaches for interacting with customers.
One of the most obvious reasons to use predictive analytics is its ability to help you see into the future and plan accordingly across a wide range of data like stock, staffing, and customer behavior.
This can tell you what is likely to happen and prepare you in advance and adjust how you allocate your resources.
Data science, a continuously growing field, has given birth to several forms of analytics processes with myriad applications for brands, including customer service, sales, and marketing. Familiarizing yourself with the many forms of analytics can help you make business decisions that would improve the customer experience.
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