Posted December 1, 2016
An ever growing love for smartphones, fitness trackers and other connected gadgets that gather data about us has helped deepen our relationship with the companies that serve us.
Indeed, customers have proven willing to actively divulge personal information — as long as it means receiving a little extra attention from businesses. A 2015 report by Columbia Business School’s Center on Global Brand Leadership found that 70 percent of respondents would consider sharing their name, contact info, birth date and other personal data, even if not required.
Through an abundance of digital data, and thanks to recent advances in computer learning and data science, companies are increasingly able to leverage our personal digital information caches to recognize and address specific customer needs.
As any experienced customer service rep will tell you, providing a great experience to a customer is seldom the result of a one-size-fits-all approach. Harnessing data in a few key areas can be an effective, even intuitive way for companies to provide exceptional service, sometimes anticipating customer needs before they even ask for help.
There are three main types of data analytics offering companies a “by-the-numbers” perspective. Descriptive analytics use tailored analytical models to assess companies’ past performance and make sense of its trends and bigger picture. Prescriptive analytics use the descriptive outputs to make suggestions for improvements going forward. And finally, predictive analytics allows companies to anticipate customers’ needs based on past behaviors and events, including ones outside of a company’s control.
Predicting demand helps companies avoid costly errors in under- or over-stocking products customers want, while predictive maintenance practices — performing regular upkeep before potential problems arise — can keep things running smoothly, cutting down on expensive customer service responses.
Gary Stephen, Vice-President of claims and risk management at PURE Insurance, says that predictive analytics have been a major factor in the company’s preemptive customer service strategy, as well as in its ongoing success.
A coming cold snap, for example, can prompt the company to alert customers to the possibility of frozen or burst pipes, in turn giving them the knowledge to prevent that from happening. “Or, by tracking home burglary reports and patterns, we can offer members in an area experiencing a spike in burglaries practical solutions to substantially reduce their risks,” Stephen says.
Using a notification system to help limit avoidable problems gives customers peace of mind, while also reducing claims — something that helps keep insurance premiums down and customers happy.
Speech analytics and social media monitoring offer companies several other ways of “listening” to customer complaints to take quicker action on their behalf. For instance, a recent rash of complaints about slow package delivery for a TELUS International retail client was found to be the result of a technical glitch on the part of the delivery company. Rather than coaching agents to respond differently, the client simply contacted the delivery company to sort out the problem.
Check your data’s best-before date
Of course, to get good data-driven insights, you first need good data. In the case of predicting consumer trends, knowing what customers will buy in the future depends largely on what they’re buying right now.
To make accurate predictions, however, companies need to ensure customer data and the analytical tools used to interpret such information are kept up to date. “If your model was created several years ago, it may no longer accurately predict current behavior, [and the] greater the elapsed time, the more likely customer behavior has changed,” states Thomas H. Davenport in a 2014 Harvard Business Review article. For example, Netflix’s earliest predictive models had to be sent out to pasture when the online streaming company realized that earlier and later Internet users were vastly different.
To keep information and assumptions about customers fresh, companies should make a habit of proactively gathering customer data both in and outside of normal touch points. For instance, taking advantage of voice or SMS based surveys can help identify potential issues before customers complain.
Additionally, certain kinds of form-submitted feedback can automatically trigger a response from an experienced customer service agent, allowing them to address the problem before that customer’s patience — and their patronage — runs out.
How data can lead to cost and time savings
For companies to retain their best customers, it’s important to make them feel valued; that means not wasting their time. In the call center, a predictive system that enables skills-based routing can mean the end of incorrect (and costly) transfers. In true omnichannel fashion, customer calls are immediately and individually routed by careful algorithms that account for a customer’s location, available demographic information and contact history. As a result, callers reach the right agents with the right skills, right away.
From multichannel to omnichannel customer experience
A checklist for assessing readiness to make the jump - with Everest GroupDownload PDF
In the long run, that kind of predictive service — paired with quick and easy access to a live human being for further resolution — could help contact centers achieve a higher level of customer loyalty, and perhaps even new sales.
However, the up-front expense of introducing such as a cloud-based system, often clashes with the need to cut costs. “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,” wrote Jeff Foley, product marketing director at customer engagement software maker Pegasystems, earlier this year in CMS Wire. “Its benefits, however, may even outweigh the cost savings of other initiatives.”
If your company isn’t already taking advantage of preemptive customer service methods, chances are your competitors are. Analytics, together with a strong digital strategy and a commitment to supporting preemptive customer service, can transform the way companies and customers communicate.