Contact Center Solutions Featured Article

Analytics Help Companies Predict Customer Behavior

May 22, 2017

Our ability to predict customer behavior has never been greater. Most companies today have unfathomable amounts of data about customers, and when it’s put to use properly, it can help companies better understand what customers are likely to do next, and what the most beneficial next steps would be for the company. Our ability to do this has been expanded by twenty-first century business technologies, according to Aspect’s Manish Bajaj, country manager for India and the Middle East, in a recent blog post.

“Over the last decade enterprises have seen an exponential influx of structured and unstructured customer data, giving insights through analytics, which can help companies create a kind of customer service experience that attracts new customers, drives customer retention and advocacy, thereby creating a competitive edge,” he wrote. “In the last decade, we have also witnessed an immense rise in intelligent AI [artificial intelligence] and BI [business intelligence] tools.”

The problem is identifying where this valuable data resides. Does the sales department have it? Is it lurking on a database in marketing? Is it sitting unused in customer emails or social media posts? While it may be in all these places, according to Bajaj, its most likely location is the contact center.

“Just think about customer data from the day the relationship with a brand started: historical conversations, customer’s appreciations and complaints on social media, history of voice and chat conversations with agents and so much more, residing in one central place,” he wrote. “And add to that the history the connections built over other touchpoints like kiosks, websites, portal logins, payments made, issues faced, etc. Contact center is a goldmine of customer information.”

With the goldmine identified, it’s important to ensure the right technologies are in place to mine it. Many organizations record only a portion of calls or other contacts for training purposes, but the potential value of data analysis today means that it’s vital to record 100 percent of calls with a solution the information can be easily extracted from (by voice or text analysis).

“On the customer’s side, it can gauge customer emotion and satisfaction by analyzing their voice, tone, silence patterns, etc. It can be used effectively to identify the success of a campaign as well,” wrote Bajaj.

Self-service analytics, another important tool, can examine how and when customers are using your organization’s self-service channels such as IVR or chatbot. Text analytics can pull in information from written sources of customer communications, such as email, text messages, chat and social media. Finally, predictive analytics can be used to pull all the information into a “big picture” that will help companies understand the optimal times to reach out to customers proactively to improve the relationship by taking advantage of opportunities, or preventing problems before they occur.

The benefits described here, of course, are all on the customer side. A good analytics program can also improve agent behavior and work habits and help managers identify where they should be spending their training and coaching resources.