4 Metrics to Improve Customer Experience Analytics

We often hear from IT leaders who know that they need to make better use of their companies’ data but don’t know where to start. 

Typically, these IT professionals are under pressure from a C-level executive, perhaps recently returned from an industry conference, who is insisting that the company needs to implement artificial intelligence (AI) across the organization. Usually, however, businesses see the greatest success from their analytics efforts when they start focusing on a more narrow goal. 

Here’s an example: We worked with one company that wanted us to help it use data to optimize shipping. It was a small engagement, scheduled to last only a few weeks. But while we were onsite, the senior vice president of global sales confided that his forecast accuracy was “abysmal.” What started as a tiny project turned into a multiyear relationship, and we helped that executive improve his forecast accuracy from under 75 percent to almost 90 percent. What’s more, each percentage point gained meant nearly $900,000 in revenue for the business. The results were huge, and they were the consequence of an organization whose leaders knew exactly what they needed to measure and improve. 

These four metrics might not always yield multimillion-dollar wins, but they’re great starting points for companies seeking to improve their analytics.

There are essentially two ways for retailers to earn more revenue: either attract new customers or increase the value of their existing customers. By focusing on market efficacy, they can do the latter. For instance, one of our retail customers found that in-store shoppers spent a certain amount, and online shoppers spent another amount. But the customers who spent the most were those who used channels that combined both buying channels, such as buy online, pick up in store (BOPIS).

People can’t buy your products if they’re not on the shelves. By analyzing logistics and inventory data, retailers can ensure that stores have the products that people need, when they need them. Some of the most impressive logistics analytics programs we’ve seen have centered on disaster scenarios. In the wake of hurricanes, for example, some big-box retailers have been able to stage inventory just outside the projected impact zone, using granular data extending even to which flavor of Pop-Tart sells best in a crisis.

Artificial intelligence tools have come a long way in the past several years, to the point that they can now scan social media for comments from a brand’s customers and analyze them for sentiment — even detecting nuances like sarcasm. Think back to some of the biggest product blunders in business history. For instance, there have been a few instances where a food or beverage company has tinkered with its formula, only to have its new offering met with disastrous reviews from the public. How many of these debacles might have been prevented if the companies had access to customer sentiment analysis?

Much like market efficacy data, information about how customers navigate omnichannel sales environments can help retailers to better understand what motivates people to buy — and how companies might be able to capture sales that might otherwise be lost. Shoppers today interact with brands in a plethora of ways, ranging from mobile apps to emailed offers, online purchases and physical stores. By better understanding which channels best convert sales, retailers can determine where to make their next investments.

It’s important to keep in mind that when we talk about data analytics, we need to pay at least as much attention to the data as we do to the analytics. Too often, analytics programs are hampered by data that is redundant, unclear or just plain wrong. But organizations that take the time to get their metrics right — and then leverage those metrics to solve specific problems — can create tremendous value for their businesses.

Story by:

Drew McMahon

Joel Tew