Using Data to Ditch the Blame Game

A lot of companies are embracing big data, but calculations mean nothing if you can’t implement them.

That’s the meeting where everyone blames everyone else for what’s gone wrong. No one has any suggestions other than “change the way YOU do things and then I’LL be successful.” Let’s take a quarterly sales meeting as an example. Sales are down and new reps aren’t meeting their quotas.

  • The VP of Sales blames HR for poor sourcing. The HR rep says it’s poor hiring decisions and poor rep management that’s to blame.
  • The head of Presales blames the Marketing director for sending them unqualified leads.
  • The Product Development lead blames it on poor rep training, which the director of Learning counters by saying it’s because of poor attendance – managers aren’t allowing reps to go to class so how could they possibly learn?

Sound familiar? We’ve probably all attended meetings like these, with smart people throwing around attitude and ego instead of good ideas. Is this how we really want to run our businesses?

There’s another way. Data analysis has the potential to stop the arguments and defensiveness and get everyone on the same page. The right data can reveal objective solutions for business problems that companies are trying to solve.

Take, for example, a classic issue for any Chief Learning Officer: learner complaints. I’ve had team members come to me claiming that “everybody’s complaining” about a particular training course. Who’s everybody? How many people actually complained? Is it one person complaining multiple times? More importantly, do the complaints have a real bearing on issues impacting our bottom line?

Data to the rescue. Only by analyzing facts can I determine which complaints I need to address in order to improve performance.

For example, every month we onboard about 50 sales reps with a two-day training. For scheduling reasons, we alternate months training in a cold, windowless, bunker-like basement and an 11th-story glass-enclosed conference room with stunning views of Palo Alto.

The energy on the 11th floor inspires. Feedback consistently rates the training as one of the best the reps have ever had. The basement reviews, meanwhile, come back as drab as the space. Instructors started demanding the upper floor every other month – a change that would create a major backlog. Was it worth creating a scheduling headache?

By tracking the performance of 200 sales reps, representing four cohorts of each classroom, we learned that the classroom environment didn’t matter. By a variety of performance measures, the sales reps performed similarly no matter which training they took.

I’m not in the business of making people miserable but at the end of the day I want them to learn. If they can do that, I don’t really care to change a system to create problems – such as a training backlog. I had the numbers to back up this decision.

How might you begin to embrace data analytics and end the blame game?

A lot of companies are embracing big data, but calculations mean nothing if you can’t implement them. A recent HR study by KPMG indicates that there is a learning curve, noting three key steps toward effectively using data:

  1. Become comfortable with data and analysis – know all the data sources you need to access and bring data experts on board to extract both information and conclusions from it.
  2. Develop industry and organizational knowledge – this will help guide efficient analytics efforts by starting with the right hypotheses.
  3. Design an analytics operating model – to maximize collaboration across groups and set standards for information flows, visualization, decision-making and more.

The first two points are a lot of the same advice I have been giving about moving ahead with data analytics – in blogs, at learning and HR conferences and in meetings with SAP customers. The third point is brilliant. Want to drive true change? Put structure around it.

Let’s go back to that quarterly sales meeting scenario. Fast-forward to a year later, after implementing an analytics initiative. The company has been able to pinpoint, for example, the behaviors of the most successful (and least successful) reps and identify exactly what actions drive sales KPIs. They’ve also analyzed the top factors that influence whether or not a prospect will buy. Thanks to data analytics, the team is now clear on how to turn the situation around.

  • The VP of Sales and HR are now on the same page about sourcing and hiring decisions because they know what traits to look for.
  • The Presales head and Marketing director has come up with a new plan to promote and qualify leads that fit the hot prospect profile.
  • Product Development actively contributes training content and Learning no longer has to struggle to get reps to go to class -it’s now mandatory.

This is just one illustration of how data can drive collaboration and transform relationships. I challenge you to think back to the last “blame game” you witnessed (or played!) and consider how you can leverage data to remove the guesswork from driving success for your organization.