Tips For Making Customer Interaction Data Actionable
Companies large and small collect an ever-growing amount of data. According to a TechJury report, in 2020, every person on average generated 1.7 megabytes of data per second. All of that isn’t available to all companies, but depending on the company and the type of data, a good portion of it is.
“All of the world today is data-driven in nature, and the COVID environment has only accelerated this,” says Jeff Nicholson, global leader of CRM systems at Pegasystems. “Customers are aware that they are leaving these digital footprints and are expecting companies to use this information.”
While companies such as Amazon are excellent at collecting customer data and turning it into action, such as recommending related purchases, other companies are still struggling with siloed data that prevents them from fully leveraging customer information.
“Customer engagement happens across lines of business, from marketing to sales to service and support and beyond,” says Jonathan Moran, global product marketing manager at SAS. “The problem is that many of these interactions happen in silos, within departments, and never make it out to the broader organization. The result is an organization that doesn’t iterate, learn, and improve based on those interactions. Often the customer experience is what pays the price.”
Several others cited the issue of data silos as one of the primary hurdles that prevent companies from turning customer interaction data into corporate action and knowledge.
Data silos have been a problem for decades, despite investments in systems that were expected to provide a unified customer view. Some of these systems didn’t integrate with others as expected, while other companies have yet to make the investments in centralized data and analytical systems, according to Jeff Mosler, CEO of Nexa. Many companies still have separate CRM, contact management, and other systems with limited integration between them.
“Many companies still have antiquated technology, antiquated operations, old CRM, or operations that are not set up to benefit from real-time diagnostics, assessment, and behavioral change,” Mosler says. “It’s hard to implement a real-time learning technology. It’s hard to train up a workforce to leverage from real-time diagnostics. It’s also hard to train models and statistical models, machine learning models that offer the right results and guidance on behavioral change. So while it’s easy to market these types of solutions, it’s difficult to implement these types of solutions.”
There are three basic building blocks for turning customer data into corporate action and knowledge, according to Moran:
1. Data
Companies need their data storage systems and processes to overcome the issue of silos and create a “single version of the truth,” Moran says. “It shouldn’t be extremely difficult to uncover customer interaction data. Sales should be able to see the service department interaction history with Customer X. Marketing should know that Customer Y just asked for technical support on a product and now would not be a good time to send a cross-sell campaign.”
It’s not easy to create a single profile for each customer, Moran cautions. This includes the hard work of setting up first-party data quality and data management processes and embedding them into systems like customer data platforms. Just because someone has four different email addresses, he shouldn’t have four different profiles.
“Collecting data off of digital touchpoints is something that every organization should be doing fully, but they don’t always do that,” Moran says. “And they don’t always use that information, right, so they don’t turn that data into action.”
2. Analytics
Companies should use analytics to score and then improve their business processes, which leads to growth, Moran advises. “Companies don’t grow without happy customers.”
So businesses should gain insight from customer metrics such as Net Promoter Score and customer lifetime value, using text and voice analytic technologies, Moran suggests.
Lumen Technologies, a telecom company, tracks not just the rational interactions customers have to achieve a goal (buy a service, get support, pay a bill) but also how they think and feel across each of those interactions, said Ryan Willis, director of experience management, in a recent blog post. “Our philosophy is that customers are the primary focus. Without them, we cannot be successful, so we must approach everything from their perspective. Doing that, we can drive a better experience by keeping our promises, communicating, and being responsive.”
Lumen is also successful at turning customer interactions into company action because the entire company, from executive management down, is committed to excellent customer experiences, according to John Hernandez, executive vice president and general manager for Genesys Multicloud Solutions. “It’s been wrapped into their rebranding efforts. They have a transformation office that reports directly to the CEO of the company. The change really comes when people across the entire company look at [interactions] from the lens of the customer. This is a prime example of not only taking that data in, but training employees as to why it’s important and to have that frame of reference.”
3. Engagement Feedback
The Lumen Technologies example shows the need for collecting customer feedback, Moran’s third building block for turning customer data into corporate action and knowledge.
“Often, customers who want to take the time provide genuine and valuable feedback, unless they are mad,” Moran says. But to obtain much of this feedback, companies must offer something of value in return, like incentives such as prizes or discounts.
Though implementing comprehensive solutions to collect data, analyze it, and trigger company actions might be difficult, some companies are doing this successfully.
Among them, the most successful companies are going beyond reacting to customer data to using it for predictive analysis, attempting to respond proactively to customer needs and expectations, according to Nicholson.
The traditional legacy call center is moving from a cost center that just takes calls and inquiries to an aggregator of data, enabling companies to leverage frontline employees and build better customer relationships, says Hernandez. “When we see those things happening, [the call center] is moving away from just interactions and transactions to fluid conversations.”
One financial services firm, for example, is collecting critical customer data at the first point of contact so that it doesn’t need to be repeated if the customer goes from digital assistance to a live agent or from one agent to the next, according to Hernandez.
Though “screen pop” technology has existed for decades, the reality is that far too many companies still require customers to repeat their names and other identifying information.
Having immediate insight into synchronous and asynchronous data allows companies to provide superior customer experiences. “What we’re also starting to see is customer service or inside sales really starting to incorporate marketing data,” Hernandez adds. Historically, marketing messages sent to the contact center didn’t connect with the contact center’s interactions with customers. But now those are being better integrated.
“Now with the contact center data merged with marketing data, we can get that data to employees through engagement, so they have a well-rounded view of what’s going on with the customer. Every company is a single brand; they shouldn’t be seen as multiple silos of a brand. They should be seen as one brand.”
Companies that are best at turning customer data into action have moved from being reactive to data long after it is collected to using real-time or near-real-time data collection and analysis to drive real-time customer behavior, Mosler says. “That could be things like real-time changes to pricing; real-time changes to offers for customers; and real-time changes to agent behavior in interacting with the customer in real time, rather than post-interaction agent learning and training. Feedback loops have dramatically increased in the past few years.”
The feedback and synergies among systems will continue to improve to help companies enhance their ability to turn customer data into action and company knowledge, Mosler predicts. “There has been rapid innovation in machine learning, [artificial intelligence], and chatbots. These are just barely scratching the surface in terms of implementation and usefulness. We’ll also see the replacement of some of these old systems frameworks that will help improve the deployment of these technologies.”
Hernandez expects to see the incorporation of even more data into companies’ data repositories, enabling them to react more quickly and precisely to customer needs and even using predictive analysis to anticipate customer needs.
“I have 46 different devices plugged into my access point right now,” Hernandez says. “Those things all have data that companies are interested in.”
INCORPORATING THE INTERNET OF THINGS
One Genesys customer, Whirlpool, collects a wide variety of data from customers using its internet-connected appliances, ranging from ovens to washers and dryers.
The smart washer and dryer, for example, can track the number of wash cycles and prompt users via Whirlpool’s Smart Kitchen Suite app when laundry supplies are running low. Integration with Amazon’s Dash Replacement Service enables users to place orders easily.
Users can also remotely activate washer and dryer modes from their personal devices. The devices can automatically notify Whirlpool about servicing requirements.
In general, Internet of Things (IoT) sensors are becoming more commonplace on the factory floor, helping companies predict when a particular piece of industrial equipment will fail so it can be taken offline for repairs during slower times rather than breaking down during a rush.
Such proactive outreach will only continue to grow as IoT devices become more prominent, Hernandez predicts.
IoT Analytics reported that IoT spending for enterprises was expected to grow 24 percent this year and 26.7 percent annually from 2022 through 2025.
Beyond IoT devices, Hernandez expects to see increasing use of video to collect data and to respond to customers, particularly in the tech sector. A customer could, for example, take a picture or record a short video showing how a piece of technology isn’t working correctly and send it to a technician, who can then respond with a video showing a fix. The last part of this is already being done, with several do-it-yourself videos on YouTube. But such videos are one-to-many rather than company-to-customer interactions.
HIGHER EDUCATION TAKES THE PLUNGE
Beyond private businesses, colleges and universities are aggressively pursuing a strategy that turns customer data into action, according to Ardis Kadiu, founder and CEO of Element451, which provides a CRM platform focused on higher education.
“The way that colleges and universities work is very similar to any other organization,” Kadiu says. “The enrollment and admissions department is your sales component. They are doing the prospecting, the marketing. The life cycle of the student starts as a prospect entering into the funnel and continues through the application and qualification process.”
Schools collect data throughout this customer journey to determine how to move students along the funnel. This is essential for their budgets, which are largely based on enrollment figures, Kadiu says. “If they miss their numbers, even by a few students, then they need to cut budget or staff, so this becomes a key driver for them. Using data in real time to change strategies is very important to them.”
Once students are actually enrolled, colleges and universities continue to collect grades and other data to determine student success and tailor curricula and course offerings to them, getting to understand which tactics and strategies are working as they go, Kadiu says.
Looking ahead, colleges and universities will use this data more for automated, predictive analytics, Kadiu says. “Rather than a person deciding to give [a prospect or enrolled student] a call or sending an email, the system would do it automatically.”
Such outreach would be based on predictive modeling, but would also include elements of personalization, Kadiu adds.
And that, in the end, is really what every company should strive to do in every customer interaction.
Phillip Britt is a freelance writer based in the Chicago area. He can be reached at spenterprises1@comcast.net.