HBR.org

Help reluctant employees put analytic tools to work

Companies that succeed in getting employees to adopt new analytic tools rely on a three-faceted approach.

By Michael C. Mankins and Lori Sherer

First published on Οκτωβρίου 07, 2014
min read


This article originally appeared on HBR.org. Bringing advanced analytic tools into your organization can help you clone your best decision makers so that you get better, faster decisions in every situation that requires human judgment. But you may have to revamp your decision processes to make the tools work smoothly. And you will certainly have to confront that pesky human factor: people’s natural reluctance to adopt new ways of doing things. Companies that are most successful at getting employees to use the new tools seem to rely on a three-faceted approach: They co-create analytics-based solutions People often fear analytic tools because they don’t understand what’s inside the “black box” or why it’s important. So some companies have learned to involve employees who will be affected by the new tools in designing and refining the tools’ applications. A property-and-casualty insurance company, for instance, knew that business customers who invested in risk management were longer tenured and more profitable than others. So it naturally wanted to know which prospects in its database would be most likely to implement safety recommendations. It began creating a predictive analytic model—but what were the right variables to use? The company ran a series of workshops involving salespeople, claims adjusters, site-inspection engineers—anyone who might have a point of view on the issue. Participants came up with idea after idea that the analytics designers might never have thought of: whether the prospect had favorable employee ratings, what percentage of its management team held technical degrees, whether the company had a senior risk-management executive, whether it invested to protect its brand’s reputation, and others. The analytics folks tested many of the group’s hypotheses and eventually rank-ordered the variables; they also kept asking workshop participants whether the results and rankings made sense in light of experience. Eventually, participants became convinced that the model outputs matched their experience and intuition. Involved from the outset, they became advocates of the new tool throughout the organization. They involve marketing in the rollout. The tech-savvy experts who create and introduce analytic tools aren’t necessarily the best people to tell everyone why the tools are important. Social and communications skills, after all, don’t always go hand in hand with an advanced engineering degree. Marketers, however, are professional communicators—which is precisely why many companies rely on their marketing departments to develop a rollout plan for analytic tools. At one company, many employees were concerned about the privacy implications of tools relating to collections. Wouldn’t customers feel annoyed that the company had so much data about them and was using it to customize the collections process? In introducing the tools, marketers emphasized that the tools made life better for everybody, agents and customers alike. If high-value, high likelihood-to-pay customers got special white-glove treatment, for instance, they would be happier than if faced with a one-size-fits-all collections protocol. They make a game out of it. One company wanted to introduce a new predictive tool to help its sales representatives identify promising prospects. But it was afraid the reps would never use it. So executives identified a unit that was far behind plan, where the reps were unlikely to earn their bonus. It gave this unit the tool first, saying, in effect, “Try this. It might help.” The reps, desperate for anything that might help, eagerly tried the new tool. When they discovered its power—and saw their unit’s results improving—they began talking it up to their colleagues in other units. Before long, every sales agent in the company was clamoring for it. Every tool and every introduction is, of course, different. Early in the process, you will want to assess the specific risks you are likely to encounter. But these three approaches have helped many once-skeptical people overcome their reluctance to put today’s analytic tools to work. Michael C. Mankins is a partner at Bain & Company. He is based in San Francisco and heads Bain’s Organization Practice in the Americas. Lori Sherer is a partner at Bain & Company in San Francisco and heads the firm’s Advanced Analytics practice

This article originally appeared on HBR.org.

Bringing advanced analytic tools into your organization can help you clone your best decision makers so that you get better, faster decisions in every situation that requires human judgment. But you may have to revamp your decision processes to make the tools work smoothly. And you will certainly have to confront that pesky human factor: people’s natural reluctance to adopt new ways of doing things.

Companies that are most successful at getting employees to use the new tools seem to rely on a three-faceted approach:

They co-create analytics-based solutions

People often fear analytic tools because they don’t understand what’s inside the “black box” or why it’s important. So some companies have learned to involve employees who will be affected by the new tools in designing and refining the tools’ applications.

A property-and-casualty insurance company, for instance, knew that business customers who invested in risk management were longer tenured and more profitable than others. So it naturally wanted to know which prospects in its database would be most likely to implement safety recommendations. It began creating a predictive analytic model—but what were the right variables to use?

The company ran a series of workshops involving salespeople, claims adjusters, site-inspection engineers—anyone who might have a point of view on the issue. Participants came up with idea after idea that the analytics designers might never have thought of: whether the prospect had favorable employee ratings, what percentage of its management team held technical degrees, whether the company had a senior risk-management executive, whether it invested to protect its brand’s reputation, and others.

The analytics folks tested many of the group’s hypotheses and eventually rank-ordered the variables; they also kept asking workshop participants whether the results and rankings made sense in light of experience. Eventually, participants became convinced that the model outputs matched their experience and intuition. Involved from the outset, they became advocates of the new tool throughout the organization.

They involve marketing in the rollout.

The tech-savvy experts who create and introduce analytic tools aren’t necessarily the best people to tell everyone why the tools are important. Social and communications skills, after all, don’t always go hand in hand with an advanced engineering degree. Marketers, however, are professional communicators—which is precisely why many companies rely on their marketing departments to develop a rollout plan for analytic tools.

At one company, many employees were concerned about the privacy implications of tools relating to collections. Wouldn’t customers feel annoyed that the company had so much data about them and was using it to customize the collections process? In introducing the tools, marketers emphasized that the tools made life better for everybody, agents and customers alike. If high-value, high likelihood-to-pay customers got special white-glove treatment, for instance, they would be happier than if faced with a one-size-fits-all collections protocol.

They make a game out of it.

One company wanted to introduce a new predictive tool to help its sales representatives identify promising prospects. But it was afraid the reps would never use it. So executives identified a unit that was far behind plan, where the reps were unlikely to earn their bonus.

It gave this unit the tool first, saying, in effect, “Try this. It might help.” The reps, desperate for anything that might help, eagerly tried the new tool. When they discovered its power—and saw their unit’s results improving—they began talking it up to their colleagues in other units. Before long, every sales agent in the company was clamoring for it.

Every tool and every introduction is, of course, different. Early in the process, you will want to assess the specific risks you are likely to encounter. But these three approaches have helped many once-skeptical people overcome their reluctance to put today’s analytic tools to work.

Michael C. Mankins is a partner at Bain & Company. He is based in San Francisco and heads Bain’s Organization Practice in the Americas. Lori Sherer is a partner at Bain & Company in San Francisco and heads the firm’s Advanced Analytics practice