People expect more from customer service these days. 

And while customer service organizations are finally shifting away from reactionary to more proactive service, in highly competitive markets disrupted by customer-centric business models, proactive service is already just table stakes. 

It’s the companies that can predict customer needs that will take the lead in customer service.

Forward-thinking organizations predict how, when and where their customers want to be reached. With the right technology and data, service representatives can get ahead of potential customer issues and provide a solution before the customer becomes aware of the problem. 

So, how can companies take advantage of vast amounts of customer data to create experiences that hit the mark?

Customer Data: More Than Account Info

Companies already collect a lot of customer data — data that goes beyond standard account information like name, hometown and date of birth. Every customer interaction — every purchase, phone call, in-person visit or bill payment — contains new insights into habits, proclivities and long-standing issues.  

Companies can incorporate other public information into their customer profiles too. With social media conversations on platforms like Twitter, forums, community posts and third-party review sites, each social interaction is an opportunity to better understand the context behind a customer service request.

Most companies haven’t even begun to tap into this rich data to improve their customer service, let alone tried to use it to anticipate customer needs. These companies will look out of touch as the Internet of Things makes even more customer data available to consume or ignore. If a customer calls with a problem on her newly purchased device, knowing her location, the device’s status or which part is malfunctioning can turn an average service experience into a memorable one.

Paradoxically, as businesses collect more and more customer data, they find it harder and harder to access and make real use of that data. 

Much of the data is locked away in separate departmental silos. Without it, employees repeat tired scripts and enforce redundant policies which ignore what they should already know. Analytics technologies can comb through that data, pulling out insights and guiding decision-making, so employees deliver timely, relevant customer service.

Learning Opportunities

Turning Data into Insight

You shouldn’t have to look for insights — the insights should find you. As a consumer, you want companies to use the data they’ve already collected to anticipate your needs and resolve your issues as efficiently as possible. 

As a customer service organization, you want that insight into how to keep customers happy and loyal, increasing their lifetime value and encouraging referrals. 

If you use customer data to gain insights and preempt customer concerns, you’re more likely to:

  • Improve likelihood of best outcomes: Predictive intelligence increases the chance of success — whether it’s higher conversion rates, accepted settlements for collections or retaining an at-risk customer. Modeling past customer behavior leads to insights on whether to recommend an offer, a workaround or a satisfying alternative
  • Make better and balanced decisions faster: Many companies leave decisions about authorizing credits, settling debts or making offers completely up to the experience and intuition of its employees. Giving guidelines to those employees, based not only on customer data but business goals, can take the guesswork out of those service decisions and eliminate long delays waiting for manager approvals
  • Test, learn and act in real-time: With adaptive analytics, past interactions can optimize future decisions so employees learn over time rather than repeat each other’s mistakes. For example, “Which response is most likely to resolve the issue?” Or, “How do churn rates of high-value, disgruntled customers improve when we authorize a credit?”
  • Imagine the future with visualization and simulation: With customer data, a business can play out the effects of a price adjustment or policy change to forecast results and optimize the response to an issue. Then a business can gain insights like, “What if you gave refunds to every platinum customer who complained?” Or, “How many customers would be eligible for this new offer? How many are likely to accept it?”

The Best Prediction: The Bottom Line

So many contact centers are focused on basic cost saving techniques — such as reducing handle times, accelerating employee training and automating manual procedures — that they de-prioritize predictive customer service as a “phase two” project. Its benefits, however, may even outweigh the cost savings of other initiatives.

Here are three examples:

  • Avoiding misroutes: Predicting the reason for a call and directing it to the right department or skilled agent makes for less frustrated customers — and improves containment rates while decreasing costly misroutes and transfers
  • Deflecting interaction to self-service: By acknowledging a service disruption and redirecting to a personalized page for a status update, companies can avoid costly phone calls when notifications and self-service are all the customer needs
  • Reduce propensity to call: Sure, preemptively addressing customer concerns makes for a more satisfying experience. But it also reduces inbound call volume, and can direct customers to more efficient channels for resolving an issue

If your service organization is not moving toward a preemptive customer service model, then you may already be feeling pressure from other companies who rely on insights from their customer data. 

Look for quick wins where you can apply customer data to solve trouble spots in your toughest customer journeys today. Then identify when and how you can improve your existing systems and processes to tap into customer data. Finally, make sure that preemptive service using customer data is a centerpiece of your company’s digital transformation plans over the next three to five years.

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