5 mistakes companies make when analyzing customer data – and how to avoid them

Whether in life or in business, sometimes we still get it all wrong even when trying our hardest. Recently I ran into a pioneering tech company that was getting AI wrong in a way that was both comical and a little alarming. To demonstrate their grasp of AI technology, they had a robot working the entrance to an event. The robot’s job was to make sure guests registered before entering. What did the robot do if you hadn’t registered? It blocked your path like an NBA power forward trying to keep LeBron James from driving to the hoop. It was irritating, a bit aggressive, and made me—a speaker who did not have to register—late. It’s hard to appreciate a next-gen AI wonder when it’s keeping you from doing your job.  

That robot fail was of course much smaller than some of the giant tone-deaf business gaffes we saw this year, from Pepsi’s poorly thought out protest ad to United Airlines’ early response to the viral video of a customer being dragged off one of its planes. After my robot bouncer encounter, I kept thinking, “Did they forget about their audience?” With the volume of customer and prospect data we are constantly collecting you would think it’s nearly impossible to get customer engagement wrong. Businesses should know everything there is to know about their cola drinkers, airline passengers and trade show attendees, shouldn’t they?

Despite all the data, businesses get responses, engagement efforts and products wrong often enough. Based on my experience, gathering the data is not the challenge. It’s how businesses analyze and leverage the data that is causing difficulties … and that is something that can be fixed.

Here are five common customer data analysis mistakes I am seeing today along with advice on how businesses can either avoid or remedy them.

Mistake #1: forgetting about the exceptions

When analyzing data, businesses often focus on the trends and then shape solutions, products and user experience based on trending customer preferences and desires. The problem is that a customer trend or a majority percentage doesn’t represent everyone. There are always exceptions and those “exceptional insights” can often show businesses new ways of doing things or open a door to a surprising opportunity.

Take for example groundbreakers in today’s changing retail marketplace who recognized exceptional behavior in their customers in recent years. They saw customers who liked to look and learn in stores but complete their purchases online and sometimes vice versa: Enter the “omnichannel” customer.

While trending data might have said X% of customers buy online and X% buy in stores, innovative retailers watched and listened to the exceptions and discovered the ominchannel customers whose varied shopping habits are a mix of online, in-store and mobile shopping. Rather than forcing the customers to follow one model, some retailers adapted by creating pressure-free showroom environments (think Amazon’s retail stores and the Apple store) where customers can come to experience the brand and products without any pressure to buy. They made moving from mobile shopping to the physical world and back seamless. Now as omnichannel shoppers become the rule rather than the exception, retailers need to once again check their data to see if they are listening to all sides. That’s the key in gathering and analyzing customer data—if you focus only on one trend or one side, you only get part of the story and that means you may be missing a big chunk of opportunity.

Mistake #2: missing customer blind spots

Customers have blind spots too. I was recently with a friend who was showing me all the features on his smart watch, which allowed him to unlock his house, turn on lights, set music and change the temperature. He was enchanted as he talked until I asked what would happen if he lost that watch. It hit him like a ton of bricks that he  was now vulnerable to security risks that he had never considered.

Customers will leverage products and solutions in many creative and innovative ways but not in perfect ways. Good data analysis can help businesses track and predict customer behaviors and even identify challenges before they happen. The best customer data analytics today are predictive and preventative, looking at the potential good of  products, solutions and markets as well as the their risks .

Mistake #3: looking back, not forward

For a long time now customer data has been able to tell us what has happened and even what is happening now. Today, however, when continuous disruption is the new norm, businesses need use customer data to look ahead. In 2006, Microsoft introduced the Microsoft Zune to compete with the iPod. Today, there are no Zunes, but iPods are still in demand.  Why did the Zune fail? Because Microsoft was recreating what already was (the iPod) rather than looking ahead to see what else customers may want.  

Focusing on “what was great back then” or “all the rage right now” is an almost certain way to fall behind with consumers who continuously ask “what’s next?” And who knows what’s ahead? The businesses clever enough to leverage predictive analytic tools and models to envision the next great thing.

Mistake #4: not letting the customer lead

The digital age had bread digitally native consumers who shop and engage on their own terms. Recently, I was at an event in Australia during which an IT leader asked a panel of CIOs about how companies can keep customers engaging inside their business ecosystem. (Think Amazon’s Alexa Echo now offering customers new ways to get to and access the services and products they want) One CIO’s responded that the customer takes the lead and it is the businesses’ job to follow where the customer wants to go. If a customer prefers voice recognition to make a bank transfer rather than go through the bank’s web site and engagement process, we don’t shut down that option. Instead, our job is to facilitate the customer’s desired path.

Mistake #5: forgetting to explain the why

One final mistake that many businesses make is forgetting is to provide meaningful context as to why products and services are updated and changed. While the data may point to a change that’s coming or needs to happen, customers might not be ready to hear it. Apple did not do well in this area when it removed the headphone jack from the iPhone 7. While Apple had customer-driven reasons to remove the headphone jack (create a waterproof iPhone, win critical space for new components, eliminate the use of snag-prone headphone cables, etc.), the storyline consumers latched onto was that it was a way for Apple to make more money. It became a story about Apple’s greed rather than its vision.

When data reveals and/or drives changes that will significantly affect how customers live and work, be sure to explain why. Customers want to know why change is coming or occurred. If you don’t take the time to explain, you risk disgruntled customers as well as an unflattering story in the marketplace that hurts both your brand and customer loyalty.

With today’s powerful data collection and analysis capabilities, businesses have a remarkable view into the wants, needs, challenges and environments of their customers. The challenge is to use it wisely and to always keep the customers’ perspectives and needs front and center.  I always remind my customers that at the end of the day, no matter what the data tells us, the customer still comes first. The data offers insight but it is the customers who invest in your brand and success.