Data-Driven Contact Centers for Proactive, Predictive, and Preventive Support

Data-Driven Contact Centers for Proactive, Predictive, and Preventive Support

Data-Driven Contact Centers for Proactive, Predictive, and Preventive Support

 

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

It’s not uncommon to wait days – if not weeks – for a response to an email, if it ever comes at all. Or wait on hold for hours to speak to an agent on the phone. The call-back 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 numerous times to resolve a single issue. In this context, the dentist doesn’t sound that bad.

These unfavorable experiences are causing customers to have waning patience who increasingly lash out at customer service agents. 1 in 3 admit to having screamed or sworn at a customer service agent. Agents, meanwhile, under more pressure than ever and overwhelmed as ticket volumes increase, are growing upset and sometimes acting rudely.

Is Your Customer Service Center Providing Service — or Failing Your People?

Customer service is failing everyone. The standard way of doing things, which heavily relied on customers partaking in the time-consuming task of reaching  out to a company, is costing companies billions of dollars. Still, the inefficiencies are also causing customers to churn.

Self-service in the form of knowledge bases and virtual agents automatically closing tickets have made a noticeable impact on the overall support experience. Still, this self-service needs to go one step further and see brands become customer champions, anticipating and preventing issues from ever happening in the first place.

Customer champions are made with data

Organizations have so much data at their disposal, but so often, this data stays in siloes, never speaking to each other. As a result, organizations are not effectively using over 80% of data.

To become customer champions, brands must better leverage their cross-department data. Before AI, this was too costly to scale.

Now, AI can be trained to be these master orchestrators, understanding similar attributes of which customers are reaching out and when, and to find the correlations between lifecycle and customer journeys and contacts to a company. AI can also now marry this all with product and context-intelligence from real-time signals.

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

Critical data to power this new age of support include:

  • Contact Type and Frequency: Are there specific customers who reach out frequently, even with minor or basic queries? (i.e., common technical questions). Can we anticipate their next question or questions they are likely to have with new products or services?

  • Contacts Tied to Specific Products or Services: What are the queries, and at what part in the journey (pre-purchase, purchase, six months post-purchase, etc.) are customers reaching out about a particular product or service? For example, after a customer has owned a new robotic vacuum for three months, are there often queries surrounding maintenance or replacement filters from customers who fit a specific profile? Is there an opportunity to anticipate these touchpoints and reach out with the information before a customer has to?

  • Context-Drivers for Contacts: Do you have insights into the day, time, location, weather, or other external factors that influence a customer’s likelihood to experience an issue and contact a company? Say, if a person is in a location with very high temperatures, does the performance of different products change? Are there tips that can be provided to mitigate poor performance before it’s ever experienced? “Wow, it’s hot out there. Preserve your e-bikes’ charge by not riding in temps over 113 degrees!”

  • Back-end system Insights: AI needs the ability to act on changes within business systems like order and inventory management, customer relationship management, loyalty and operations.

When data speaks to each other and uncovers patterns from historical context, it can genuinely power a support experience that’s proactive and preventative. It’s essential, however, to be targeted in the outreach. We live in a world of clutter and noise, and no one wants to be bombarded with unnecessary messages.

Only when a brand anticipates an issue for a specific person, at a very specific instance, should this outreach occur.

Turning support from a cost and resolution center into an advocacy center

For decades, the call center has been an assembly line of agents focused on resolving issues and answering questions, sucking up a lot of costs and offering little impact on the overall health of a company. Those times are gone. As customer experience has become table stakes, the customer support function has shifted into one that directly impacts revenue.

People base their buying decisions on customer experiences, and every interaction a person has with a brand can be the catalyst to building trust or completely destroying it.

By leveraging data and shifting to more predictive, proactive, and preventative care, support can turn into a true advocacy center that builds the deepest relationships that brands have ever had with customers. Relationships built on trust and the notion that brands are looking out for customers and have their best interests at heart. Let’s look at some examples of what’s possible.

  • I’m running late to the airport, stuck in traffic as I desperately try to make my flight home. It’s not going to happen. As I pull out my phone to call the airline, I see a message: Emily, we noticed you’re not at the airport yet and you might miss your flight home to Denver. There’s another flight leaving at 6:32pm. Would you like us to grab a seat on that for you? Why yes, you absolutely can.

  • Or, say I’m expecting a dress to be delivered for a wedding this weekend. As the delivery day approaches, I open my email: I know you’re expecting a delivery today. We’re so sorry; there was a weather event that has caused a delay. Instead of arriving tomorrow, your order will be delivered on Wednesday by 5pm. Again, we’re so sorry about the inconvenience. At least I know it’s still coming on time.

  • What if I’m waiting for my ride share on a busy city corner when it starts to rain? Want to shave off 5 minutes of wait time? Walk to the corner of Park and 35th, and your driver can pick you up faster. Heading there now.

AI powers the future of proactive customer service

The reliance solely on humans to provide support has stopped proactive and predictive care from being scalable. Without AI, it’s too costly to attempt this sort of care on a widespread basis – to all customers, not only a select few.

AI can be trained to effectively anticipate – based on a myriad of data changes and combinations – when an individual person is likely to experience an issue and take the appropriate steps to either A) prevent it from ever happening or B) at the very least, communicate the setback or change in plans to customers before they have to take the time to contact a company.

This type of help will champion the future of customer relationships.

The post Data-Driven Contact Centers for Proactive, Predictive, and Preventive Support appeared first on ReadWrite.

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Puneet Mehta

Puneet Mehta

Puneet Mehta is Founder / CEO of Netomi, a YC-backed customer experience AI platform that automatically resolves customer service issues at the highest rate in the industry. He spent much of his career as a tech entrepreneur as well as on Wall Street building trading AI. He has been recognized as a member of Advertising Age’s Creativity 50 list, and Business Insider’s Silicon Alley 100 and 35 Up-And-Coming Entrepreneurs You Need To Meet.

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