While standing at the banks of the fastest-flowing river in Europe, I thought of an alarming statistic. Currently, there is four times more “digital dust,” or data, in the world than grains of sand on the beach. According to Gartner more than 75% is left untouched and is not used for making business decisions. Much like you need the proper kayak and equipment to safely ride Class 5 rapids of this river, you can’t harness the benefits of big data without the right technologies.

Artificial intelligence (AI) and machine learning have become paramount in realizing the potential of big data. For example, one of our customers is in a competitive and saturated market, where everyone is fighting for their share. Customers were leaving at an alarming rate, while the price of retaining them continued to increase.

Traditional methods used to keep customers, such as bundles, upgrades and discounted subscriptions, only eroded profits. “Save desks” were established to intercept customers before they departed. But, even with these systems in place, the company continued to lose customers at an unsustainable rate.

The management team looked for numerous potential causes and were surprised at what they found. Some agents performed well while handling frustrated customers, others performed poorly. The agents that were struggling on average were performing well on particular interactions, but not on all interactions. Executives wondered why this happened—even after they had focused on employee training and engagement for years. They believed their save agents were the best in the business.

By using AI and machine learning for customer experience, the executives discovered patterns in agent and customer activities and were able to turn much of that latent data into actionable insights. The first step was to identify the agents who were best at saving certain customer profiles. After the AI found this correlation, the team applied AI augmented routing to match numerous aspects of an agent profile to a specific customer who contacts the company. Providing the finest grain match of the customer for their specific problem, on a specific channel for a specific problem. Essentially, the team used patterns in the data to match the customer with the optimal agent

Doing so enables AI augmented routing to adjust as customer-to-agent behaviors change. And identifying correlations (not causations) in the data helps you continually meet business outcomes—even in the most volatile of environments.

Utilizing latent data through AI is the best way to optimize each interaction, reduce costs, improve efficiency and decrease churn. It also helps to improve the customer experience, customer satisfaction scores and first contact resolution. Routing that is based on a granular factors turns latent data into actionable and valuable insights. And it helped our customer safely navigate the rapids of big data—with more than a 3% reduction in churn.

To find out how to improve your routing through AI, I invite you to come and learn how behavior and technology are rapidly transforming how we deliver CX and what it means for your business at CX 18 in Nashville.

Graeme Provan

Graeme Provan

Graeme Provan is the Global Solution Director for Analytics and Predictive Engagement at Genesys. With more than 15 years of experience, Graeme belongs to an innovation team at Genesys, is certified in the COPC CX Standard, and has performed a...