The type of question we hear most is: How can I get the big data train moving? After all, there’s no shortage of rational reasons to get big data programs rolling: companies that use customer analytics extensively are more than twice as likely to generate above-average profits as those that don’t; an integrated analytic approach can free up to 20 percent of marketing spending; and injecting big data and analytics into operations can help companies outperform their peers by 5 percent in terms of productivity and 6 percent in profitability. So what’s the problem?
The challenge is that many of the obstacles derailing big data efforts aren’t rational. They’re emotional. For all the technical and procedural complexity around big data, the biggest hurdle is often human behavior. Recommendations based on advanced analytics can make a huge difference—if sales reps and customer service agents use them. But many simply don’t want to. Leaders charged with making big data programs work need to understand and acknowledge this reality and develop specific approaches to build trust that overcomes the emotional resistance. That means more than just training employees to use technology to better engage with customers. The best leaders develop examples of what most effectively addresses specific concerns, creating a clear path of action and adopting new approaches to reward new behavior. Here are three of the most common behavioral obstacles and some thoughts about minimizing their impact.
Obstacle 1: “It’s too hard and not worth the effort.”
Many sales reps believe these “new fangled” analytics are too complicated and won’t provide enough benefit for the effort required to understand how to use them. And they have good reason to be skeptical: many have hit “tool fatigue,” having seen one allegedly revolutionary approach after another come and go. That means even with a tool with excellent usability, leadership needs to work hard to convince reps that the analytics aren’t complicated and that it’s worth adopting.
This issue needs to be addressed in three ways. First, note that studies show that the additional time associated with working with recommendations from analytics is insignificant or nonexistent. Second, in many cases these systems can in fact save agents’ time by providing accurate recommendations for specific cases that, in the past, agents themselves had to do with outmoded software and little or no analytics support. One of the best ways to convince your reps is to get them to commit to investing a small amount of time (less than 30 minutes) to test run a recommendation or run a simple query. Finally, frontline agents need to know you value their input and are listening to them. The keys to a program’s success are having robust user acceptance and operational-performance testing to ensure analytic recommendations are being delivered in a timely and accurate manner in support of employees.
Obstacle 2: “I know better.”
Many sales reps are convinced their instincts and experience can provide better answers than analytics. The reality is, a well-implemented analytics solution can provide better, more relevant answers than all but the very best reps. Convincing reps of this fact requires showing how analytics can help them do their job better and, critically, make them more money. So, show them the earnings difference between a team that uses analytic recommendations and one that doesn’t (if the example happens to show a marked improvement in the performance of previously ho-hum sales reps, all the better). Of course, this isn’t to say that judgment isn’t necessary. In fact, good judgment and experience remain critical to making data-generated recommendations more useful and effective. That’s why good analytics programs ensure that all analytic recommendations are provided with supporting context and rationale so reps understand the “thinking” behind the recommendation.
Obstacle 3: “I don’t trust you.”
This one is probably the toughest issue to overcome: the psychological concern that machines are replacing humans. And while automation has eliminated some low-value and repetitive tasks, and technology may be more efficient at making recommendations directly to consumers (especially in digital channels), the overwhelming reality is that people still want to talk to people. Sales reps and customer-service agents are more valuable than ever for understanding customer needs and more complex purchases, such as bundled product sales.
It’s not easy to build trust and overcome inherent resistance. One thing we recommend is turning top performers into allies and, more importantly, advocates. Top sales performers often have major influence within organizations. Getting them to work with the “meaty middle”—the 80 percent of reps who are neither at the top nor the bottom of the pack—is critical because changing the behavior of this large group will have the biggest impact. It can’t be a mandate from headquarters. If the meaty middle sees top performers adopting new ways of working that are both maintaining and extending their success, it will soon follow suit. At the same time, companies must tweak their incentive structures to get top performers on board with new approaches.
When it comes to ensuring your organization gets the most out of advanced analytics, don’t expect the data to do all the work for you. Getting the big data train rolling hinges on how well you can read and react to emotions.
