Data-driven contact centers for proactive, predictive and preventive support

Almost half (48%) of people would rather go to the dentist than call customer service. Yeah. But should that really be so surprising? Here are data-driven contact centers for proactive, predictive and preventive assistance in your customer service.

It is not uncommon to wait days or even weeks for a response to an email, if at all. Or wait for hours to speak to an agent on the phone. Callback options don't always work either: 62% have been ghosted by companies multiple times. And perhaps worst of all, even when customers interact with an agent, 65% have to follow up multiple times to resolve a single issue. Against this background, the dentist does not look so bad.

These adverse experiences cause customers to lose patience and increasingly turn on customer service agents. 1 in 3 admit to having yelled at or insulted a customer service agent. Meanwhile, agents, under more pressure than ever and overwhelmed by the increase in tickets, are getting more upset and sometimes acting rude.

Is your customer service center providing service or is it letting your staff down?

Customer service lets everyone down. The standard way of doing things, which relied heavily on customers participating in the tedious task of contacting a business, is costing businesses billions of dollars. Yet inefficiencies also lead to customer churn.

Self-service in the form of knowledge bases and virtual agents automatically closing tickets has had a noticeable impact on the overall support experience. However, this self-service needs to go even further and see brands become customer champions, anticipating and preventing problems from ever happening.

Customer champions are created with data

Organizations have so much data at their disposal, but so often that data sits in silos, never talking to each other. As a result, organizations are not effectively using more than 80% of data.

To become customer champions, brands need to better leverage their cross-functional data. Before AI, it was too expensive to scale.

Now AI can be trained to be those master orchestrators, understanding similar attributes of customers who contact them and when, and finding correlations between lifecycle and customer journeys and contacts with a company. AI can now combine all of this with product and contextual intelligence from real-time signals.

All of this data can give businesses the superpowers to really anticipate what customers might need in the future.

Critical data to propel this new era of support includes:

Type and frequency of contact: Are there specific customers who contact us frequently, even with minor or basic questions? (i.e. common technical questions). Can we anticipate their next question or questions they may have with new products or services?

Contacts related to specific products or services: what are the queries and at what stage of the journey (pre-purchase, purchase, six months after purchase, etc.) customers who contact you for a particular product or service? For example, after a customer has owned a new robot vacuum for three months, are there often questions regarding maintenance or filter replacement from customers who fit a specific profile? Is there an opportunity to anticipate these touchpoints and communicate information before a customer has to?

Context-Drivers for Contacts: Do you have information about the day, time, location, weather or other external factors that influence the likelihood that a customer has a problem and contacts a company? ? For example, if a person is in a place where the temperature is very high, does the performance of different products change? Are there any tips that can be provided to mitigate poor performance before it is ever experienced? "Wow, it's hot out there. Keep your e-bikes charged by not riding in temperatures over 113 degrees!"

Back-end system insights: AI must be able to act on changes within business systems, such as order and inventory management, customer relationship management, loyalty and operations.

When data speaks to itself and reveals patterns from historical context, it can truly fuel a proactive and preventative support experience. However, it is essential to be targeted in raising awareness...