It’s no secret that consumers are demanding more personalized experiences from businesses than ever before. This seems to be a reasonable expectation, given the massive amount of data that service organizations collect on their customers – not only through surveys and forms, but also through interactions over websites, mobile apps, and live conversations. But with all this data at their disposal, most businesses continue to miss golden opportunities to deepen brand loyalty by alienating customers with generic and impersonal engagements.
A recent survey revealed that service-centric businesses have not progressed beyond the fundamentals of customer service at a time when consumer expectations are reaching new heights. The report found that 74 percent of business decision makers admit their customer service initiatives are primarily focused on “getting the basics right” – things like responding quickly, remembering customer preferences, and providing easy access to customer information for employees.
For companies that treat customer service primarily as a cost of doing business, executives focus on operational data to maximize efficiency and reduce expenses. They collect quantitative metrics like Average Handle Time, Average Speed of Answer, and Agent Utilization to optimize how quickly they process customer inquiries. Budgetary decisions aren’t made with the customer in mind but by evaluating the potential return of agent minutes saved before committing to any investment. If these companies measure customer satisfaction at all, they remedy any issues by making an efficiency trade-off: achieve higher satisfaction by spending extra time with customers, or even worse, sacrifice customer happiness by coaching employees to simply go faster.
These companies are focused inwardly, not on understanding their customers. In today’s world, these companies are using the wrong data to guide their decisions.
Omnichannel and the Customer Journey
Modern consumers expect businesses to be available any place, any time, and in their channel of choice. Yet customers also are somewhat resigned to the fact that many businesses often don’t pay attention to these expectations. According to the Economist Intelligence Unit survey, customers say that one of their top three customer-service complaints is that companies fail to listen to their needs. And yet, retail banking organizations, for example, optimistically self-rank this capability as their top customer-service attribute, indicating a major disconnect.
This is especially true when it comes to supporting the entire customer journey. Customers seeking support often engage with several different employees – in person, on the phone, or over digital channels – in pursuit of an answer to their inquiry. But according to the report, only 20 percent of businesses are working on developing omnichannel integration capabilities. It’s another example of how most organizations take a stop-gap approach to customer service instead of seeing the bigger picture of how they can better engage with customers across all channels.
Omnichannel also causes the most handwringing when it comes to budget decisions. It’s less obvious how to measure the ROI of omnichannel capabilities like live chat, co-browsing, or social media engagement in terms of “agent minutes saved,” which makes omnichannel programs a prime candidate to be cut. Adding staff and technology to cover these extra channels requires more investment, not less. If the company segregates data from these channels in separate silos, it runs the risk of delivering a disjointed, unsatisfying customer experience.
Yet the rewards are compelling. In the end, the return on supporting the entire customer journey comes not from cost cutting, but from the higher revenue driven by more loyal customers willing to be an advocate. Quantitatively, the increase in revenue shows up in metrics like reduced churn rate, lower costs to acquire new customers, and higher lifetime customer value. The data from engaging in these extra channels can lead to further personalization and a better understanding of customers – but only if the data are used properly.
Anticipating Customer Needs
After all, it’s not enough to have visibility to all this customer data. In some service organizations, customer service representatives are inundated with information. The problem is in putting that data to good use.
Once a customer identifies himself or herself by logging in, a company should shift to meet that customer’s needs. Interfaces should reflect their personal preferences. Run-rate forms should be automatically filled in with known data about the customer. Every employee should have visibility into the customer’s open issues. And the customer’s past behavior and current context should inform what happens next. What knowledge articles or web pages were viewed before calling? What emails have they sent in the last 72 hours? Was there a recent purchase or account change? All of these are good reasons to change a greeting from, “How can I help you?” to “Hello Mr. Brown, I understand you signed up for our service yesterday but haven’t activated it yet. Can I help you get started?”
This is where predictive analytics and adaptive models come into play. What if a company could more easily identify high-value customers by understanding which had the most potential to buy more? By taking into account a customer’s personal data, recent transaction history, and other variables that correlate to the segment, companies can build up intelligence over time so they can increase conversion rates. If service organizations responsible for retention knew which customers were more likely to churn, they could put together more aggressive offers designed to retain those higher-value customers and be less accommodating to high-cost troublemakers. A more programmatic approach to decision making can remove the guesswork and inconsistencies from these conversations.
More Data Means More Expectations
As consumers, we generate a lot of data for the companies with which we do business: personal data, transactional data, and usage data. As we use or shop for a service, we contribute information about our preferences, habits, interests, and satisfaction level. The least we can expect is that companies learning all this information about us will take it into account in our conversations. Instead, we are thrown into “market segments” and hit with offers and pitches that just aren’t relevant to us.
The sheer amount of customer data available is going to increase dramatically. With the advent of the Internet of Things, we’ll have data from wearable devices, appliances, and sensors pummeling companies with even more personal information. That leaves fewer excuses for not knowing a customer’s identity, not incorporating the context behind an inquiry, or not anticipating a problem when a device fails.
Customer service plays such a critical role in business success, but most companies are barely using the data they have to personalize the customer experience and encourage longer-term relationships. By applying analytics to customer data to drive personalization, businesses give themselves a fighting chance to meet the growing expectations of modern consumers.
Jeff Foley is the Director of Product Marketing at Pegasystems. Jeff aligns sales, marketing, and product organizations around new technologies to deliver software his customers love to use. He is passionate about Customer Relationship Management (CRM) software and the customer experience. Jeff started his career as an engineer before moving over to “the Dark Side” of marketing. He has two decades of product management/marketing experience at enterprise and consumer software companies, including Dragon Systems, edocs, Atari, Nuance, Bullhorn, and now Pega.
Jeff holds BS and MEng degrees in Electrical Engineering and Computer Science from MIT. Follow him on Twitter @jjfoley.
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