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

Almost half (48%) people would rather go to the dentist than call customer service. Sorry. But, is this really surprising? Here are data-driven contact centers to support proactive, predictive, and preventive in your customer service.

It’s not uncommon to have to wait days – if not weeks – to get a response to an email, if it arrives. Or wait hours to talk to a support agent over the phone. Callback options don’t always work either: 62% have been ghosted by companies multiple times. And perhaps worst of all, even when a customer interacts with an agent, 65% have to follow up multiple times to resolve a single issue. In this context, the dentist doesn’t sound bad.

These adverse experiences are causing customers to lose their patience, who are increasingly attacking customer service agents. 1 in 3 people admit to shouting or swearing at customer service staff. In the meantime, agents are under more pressure than ever and overwhelmed as ticket volumes grow, becoming increasingly annoyed and sometimes acting rude.

Is your call center providing service – or not to your people?

Customer service is failing everyone. The standard practice, which relies heavily on customers engaging in time-consuming business contacting a company, is costing companies billions of dollars. However, inefficiencies are also causing a stir among customers.

Self-service in the form of knowledge bases and virtual agents that automatically close tickets have made a noticeable impact on the overall support experience. However, this self-service needs to go a step further and see brands become customer champions, anticipating and preventing problems that used to happen in the first place.

Champion customers are created from data

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

To be customer champions, brands must make better use of data between parts. Before AI, this was too expensive to scale.

AI can now be trained to be these master dispatchers, understanding the similar attributes customers are reaching for and when, and finding correlations between lifecycle and customer journeys. customers and relationships with a company. Now, AI can also combine all this with product and contextual intelligence from real-time signals.

All this data can give companies superpowers to really predict what customers might need in the future.

Key data to power this new age of support include:

  • Contact type and frequency: Are there specific customers you reach out to on a regular basis, even with small or basic questions? (ie, the usual technical questions). Can we anticipate their next questions 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 of the journey (pre-purchase, purchase, six months post-purchase, etc.) is the customer reaching for a particular product or service? For example, after a customer owns a new robotic vacuum cleaner for three months, there are often questions surrounding filter maintenance or replacement from customers who fit a particular profile? Is there an opportunity to anticipate these touchpoints and get access to information before the customer?

  • Context Driver for Contacts: Do you have details on the date, time, location, weather, or other external factors that affect your customer’s ability to crash and contact the company? Suppose, if one is in a place with very high temperature, will the performance of different products change? Are there any tips that can be provided to mitigate poor performance before experimenting? “Wow, it’s hot out there. Preserve your e-bike charge by not riding in temperatures above 113 degrees! “

  • Detailed information about the back-end system: AI needs the ability to act on changes in business systems such as order and inventory management, customer relationship management, loyalty, and operations.

When data communicates with each other and uncovers patterns from historical context, it can really provide a supportive and proactive experience. However, it is essential to be targeted within reach. We live in a messy and noisy world, and no one wants to be bombarded with unnecessary messages.

Only when a brand anticipates a problem for a particular person, in a very specific case, does this outreach happen.

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

For decades, a call center has been a collection of agents focused on solving problems and answering questions, attracting more costs and less impact on a company’s overall health. . Those times are gone. As customer experience became the key factor, the customer support function turned into a function that directly impacted revenue.

People make purchasing decisions based on customer experience, and every interaction a person has with a brand can be a catalyst to build trust or completely destroy it.

By leveraging data and moving to more predictive, proactive and preventive care, support can turn into a real advocacy center build the deepest relationships the brand has ever had with its customers. Relationships are built on trust and the notion that brands are always looking out for their customers and focusing on their best interests. Let’s see some examples of what is possible.

  • I arrived late to the airport, stuck in traffic as I desperately tried to fly home. It won’t happen. When I pulled out my phone to call the airline, I saw a message: Emily, we realize you’re still not at the airport and you may miss your flight home to Denver. There is another flight departing at 6:32 pm. Would you like us to pick a spot up there for you? Why yes, you absolutely can.

  • Or, let’s say I’m expecting a dress to be delivered to a wedding this weekend. As the delivery date approached, I opened my email: I know you are expecting a delivery today. We are very sory; There was a weather event that caused the delay. Instead of arriving tomorrow, your order will be delivered on Wednesday by 5pm. Again, we are very sorry for this inconvenience. At least I know it still arrives on time.

  • What if I’m waiting for my train in a crowded corner of the city when it starts to rain? Want to reduce waiting time by 5 minutes? Walk to the corner of Park and 35, and your driver can pick you up faster. Going there.

AI powers the future of proactive customer service

Relying solely on people to provide support has made proactive and predictive care impossible to scale. Without AI, it would be too costly to attempt to implement this type of care on a broad basis – for all clients, not just a select few.

AI can be trained to effectively predict – based on a multitude of changes and combinations of data – when an individual is likely to have a problem and take the appropriate steps to A) prevent it from ever happening out or B) at the very least, notify customers of a failure or change in plans before they have to take the time to contact a company.

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

Puneet Mehta

Puneet Mehta

Puneet Mehta is the Founder/CEO of Netomi, a YC-backed customer experience AI platform that automatically solves customer service issues at the highest speed in the industry. He has spent most of his career as a tech entrepreneur as well as building Wall Street trading AI. He’s been recognized as a member of Business Insider’s list of 50 Innovators of the Advertising Age, and Business Insider’s 100 and 35 Upcoming Entrepreneurs You Need to Meet.

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