Fancy Mills is the group training and content director for the International Customer Management Institute (ICMI). She also runs a small contact center for their subsidiary, HDI, an events and services organization for the technical support and service management industry. In January of 2019, her company launched an ad campaign to let people know there was an option to chat when they needed product support. After the campaign came out, data showed that in just one month, the number of chats had soared to reach 50 percent of the volume for the entire previous year. In other words: When they alerted customers to the chat option, customers used it...a lot.
“We had to look at ‘What are people chatting with us about? Do we need to put solutions on the website to make the information more clear… make it an easier customer experience, easier to find information on the website? Do we need to build out our FAQ?’” Mills said.
Like most customer support leaders, Mills is not a data analyst. But she is learning to use the growing amount of customer data to make decisions—a skill that is becoming a crucial part of her job. And a skill that ICMI is trying to ensure other customer contact center leaders acquire.
“ICMI is launching a customer experience bootcamp course to teach contact leaders to better understand the data,” Mills said. “The analytics around the industry have changed so dramatically in the past two-to-five years. We’re talking about all social media, CX chatbots, automation….”
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Most customer support leaders reach their position by rising through the ranks of customer support organizations. But today, everything about the customer journey, from how the customer uses the product and service, to how they contact the company, to agent performance and engagement metrics has to be covered—and measured.
The vast quantities of data coming in
It’s a lot to balance, as Cory Peace, head of operations at Simplr, a customer service outsourcing company for high growth companies, noted.
Support leaders have to balance the data around support center efficiency with the data around customer experience. They have to balance the data that comes in quickly—first response times, first contact resolution rates, surveys—against the lagging data, such as cost per resolution, utilization rates of the team, Net Promoter Scores, and customer loyalty.
But today, everything about the customer journey, from how the customer uses the product and service, to how they contact the company, to agent performance and engagement metrics has to be covered - and measured.
“These quant data sets will also need to be supplemented by qualitative (sample set) reviews of customer interactions to check for tone, voice, and brand adherence,” Peace said. But, he noted, advancements in AI make it so that some of that manual review work can be reduced. Customer interactions can be flagged for human review through good intent recognition. “If your operations, training, and knowledge base is set up well to drive great interaction, speedy responses and one-touch, efficient resolutions, your costs will go down.”
“Having the right data is half the battle,” he continued. “Being able to set up the right operating model for customer service that balances quality with efficiency is the other half.”
Are we there yet?
A 2018 study by Harvard Business Review on using real-time analytics to drive customer experience showed that 83 percent of respondents believed it was important to translate data into actionable insights at the optimal time—but only 22 percent said they currently had the capability. And nearly 60 percent said their companies have seen a significant increase in customer retention and loyalty as a result of using customer analytics.
Today with sophisticated CRM systems and data visualization, companies that invest in modern analytics technology have the ability to create data sets that leaders can use to make real-time decisions. Among these tools, as Peace pointed out, are Tableau, Looker, MixPanel, and Heap Analytics, along with tools like Zendesk Explore. But only about 30 percent of the companies in the Harvard study said they’re really investing in these systems, citing problems like funding restrictions, data silos, organizational silos, and legacy systems.
There are some data sets most leaders track, Peace said, such as cost per resolution and utilization rates of their team. But often, Mills said, leaders have to ask for the data they’re looking for—and the more limited the data sets, the more limited the insights.
Today with sophisticated CRM systems and data visualization, companies that invest in modern analytics technology have the ability to create data sets that leaders can use to make real-time decisions.
Typically, managers ask for data and department leaders bring it to them. Sometimes the data team analyzes it, sometimes they don’t. Only really large enterprises tend to have their own data science team.
“I would love to have a team of people to do that,” Mills said, “but I don’t.”
The more common substitute for an in-house team of data scientists is robust CRM and data visualization technology that creates dashboards that can inform leaders. The Harvard study listed CRM, predictive analytics, social media monitoring, content management systems, and marketing operations management as the most important technologies and capabilities for real-time customer efforts today—and see IoT, intelligent assistants, text/speech/voice analytics, mixed reality, content management, and cloud computing becoming more important in the future.
Sixty percent are in the process of creating a rules or decision engine to support instant, relevant, automated real-time decision-making. The rest are considering it.
When they get the data, leaders need to be able to start with a high-level observation, like that inquiries on returns have increased, and drill down to see the underlying factors that caused the trend. Toyota’s Five Whys is a good tool for this. For example:
Ask “why” inquiries on returns have increased. Then, when you learn it’s because many people returned a specific product, ask why they returned the product. Say you discover that the product or packaging was flawed. Keep asking why until you get to the root of the problem. It’s a deeper integration of the contact center with other aspects of the business. But it begins with data.
Mills, who heads the content area for ICMI, says the transition for support leaders becoming experts in data-driven decision-making is in its early stages. But there are increasing numbers of courses available for business leaders in data analytics, including those from Coursera, Kellogg, and Wharton Executive Education. The time is coming, Mills said, when being able to make decisions based on data analytics will be an essential skill for customer support leaders.