Guest Blog: Customer Service Becomes Predictive

This week on our Friends on Friday guest blog post my colleague, Jeff Foley, writes about using customer data to create memorable customer experiences and to be proactive in knowing what the customer wants. – Shep Hyken

What do customers want?  If companies could predict that, they could demonstrate true customer service leadership.

Forward-thinking organizations are shifting from a reactionary stance to a proactive one. They are preemptively servicing their customers and predicting 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 even becomes aware of the problem.

So, how can companies take advantage of vast amounts of customer data to create memorable experiences that positively impact the company’s bottom line? First, there has to be an understanding as to why this approach to customer service is important.

Customer Data: Beyond Name, Rank and Serial Number

Companies already collect quite a bit of information on customers beyond the standard account information like name, hometown and date of birth. Every customer interaction contains new insights into habits, preferences and issues.  Companies can integrate other public information into these customer profiles to form a deeper picture. With social media, each digital interaction provides a fresh chance to better understand the context behind each customer service request.

Despite the riches of information available, most companies don’t know how to apply all this data to improve customer service, let alone use it to truly anticipate customer needs. As the Internet of Things generates even more data from devices, companies risk appearing out of touch with customers. For example, if a customer is calling with an issue about his newly purchased laptop, knowing that customer’s location, the device’s status or which part is malfunctioning can all turn an average service experience into an exceptional one.

Paradoxically, as businesses learn more about customers, they tend to do less to demonstrate what they’ve learned.  Despite tapping into increasing streams of customer data, businesses find it difficult to easily access and put to real use. Without that data – much of which is locked away in separate departmental silos – employees repeat warmed-over scripts and enforce redundant policies that ignore what they should already know. Analytics technologies have the ability to sift through that data, pull out insights and guide decision-making in real time, allowing employees to deliver on-the-spot, relevant customer service.

Turning Information into Insight

In this modern world, no one should have to look for insights – the insights should find everyone. Consumers want companies to use the data already stored to anticipate needs and resolve issues as quickly as possible. If an organization uses customer data to gain insights and preempt customer concerns, the following is likely to happen:

• Optimize outcomes: Simply put, predictive intelligence increases the chance of success. Modeling old customer behavior leads to new insights on whether to recommend a particular offer, find a workaround or create a satisfying alternative.

• Make quick and balanced decisions: Many companies leave key decisions about authorizing credits, settling debts or making offers completely up to the whims of its employees, providing few centralized policies. Organizations should guide employees based on customer data and business goals. This will allow them to eliminate the guesswork out of those service decisions and avoid long delays waiting for manager approvals.

• Learn and adapt from past missteps in real time: With adaptive analytics, past interactions can inform future decisions so employees learn over time rather than repeat each other’s mistakes.

• Reimagine the future with visualization and simulation: With customer data, a business can simulate the effects of a proposed price adjustment or policy change to forecast results and optimize the response to an issue. This allows businesses to identify the probable outcomes of key scenarios such as, “What if you gave refunds to every platinum customer who complained?”

The Best Metric of Success: The Bottom Line

While most contact centers focus on baseline cost-saving techniques, like reducing handle times and accelerating employee training, they often underestimate the revenue-generating benefits of predictive customer service and classify it as a “phase two” project.  However, the benefits may outweigh the cost savings of other initiatives. For example:

• Eliminating misroutes: What’s one of the best ways to make a customer feel less frustrated? Predict the reason for a call and direct it to the right department or skilled agent, decreasing costly misroutes and transfers.

• Redirecting inquires to self-service: By acknowledging a service disruption and redirecting inquires to a personalized web page for a status update, companies can avoid costly phone calls when notifications and self-service are all the customer needs.

• Reducing call volume: Addressing customer concerns before they happen always makes for a more satisfying experience. But more than that, it also reduces inbound call volume if companies can direct customers to more efficient channels for resolving an issue.

If an organization is not moving toward a preemptive customer service model, it may soon feel the heat from customers getting better service from more forward-thinking companies.

Changing to this model begins with a few immediate steps.  First, identify the thorniest customer journey trouble-spots to determine where to find quick wins.  Determine a metric that reflects success for that journey – for instance, one telecommunications company chose to focus on reducing the total number of customer touches during a change of residence.  Start by using real people to track those journeys until an understanding is gained around which legacy systems and processes to improve or replace.  From there, begin to apply customer data to make smarter decisions and anticipate customer needs.

Finally, make sure that preemptive service using customer data sits as the centerpiece of the company’s digital transformation plans over the next three to five years.

Jeff Foley is a Product Marketing Director at Pegasystems. As an MIT engineer-turned-marketer, Jeff aligns sales, marketing, and product organizations to deliver software customers love. (Twitter: @jjfoley)

A version of this post appeared on CMS Wire.

For more articles from Shep Hyken and his guest contributors go to customerserviceblog.com.

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