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Rice University research uses data to spot your best sales team members

By accounting for both known and unknowable factors, managers can identify salespeople with traits that work best in different types of sales. Getty Images

When you're a manager, decisions barrage you each day. What product works? Which store layout entices? How will you balance the budget? Many of these decisions ultimately hinge on one factor: the skills of your sales force.

Often, when managers evaluate their salespeople they contend with invisible factors that may not show up in commissions or name-tagged sales rosters — intangibles such as product placement, season or simply a store's surrounding population. This makes it hard to fully evaluate a salesperson, or to spot which workers can teach valuable skills to their peers and improve the whole team.

But what if you could plug a few variables into a statistical model to spot your best sellers? You could then ask the star salespeople to teach coworkers some of their secrets. New research by Rice Business professor Wagner A. Kamakura and colleague Danny P. Claro of Brazil's Insper Education and Research Institute offers a technique for doing this. Blending statistical methods that incorporate both known and unknown factors, Kamakura and Claro developed a practical tool that, for the first time, allows managers to identify staffers with key hidden skills.

To test their model, the researchers analyzed store data from 35 cosmetic and healthcare retail franchises in four South American markets. These particular stores were ideal to test the model because their salespeople were individually responsible for each transaction from the moment a customer entered a store to the time of purchase. The salespeople were also required to have detailed knowledge of products throughout each store.

Breaking down the product lines into 11 specific categories, and accounting for predictors such as commission, product display, time of year and market potential, Kamakura and Claro documented and compared each salesperson's performance across products and over time.

They then organized members of the salesforce by strengths and weaknesses, spotlighting those workers who used best practices in a certain area and those who might benefit from that savvy. The resulting insight allowed managers to name team members as either growth advisors or learners. Thanks to the model's detail, Kamakura and Claro note, managers can spot a salesperson who excels in one category but has room to learn, rather than seeing that worker averaged into a single, middle-of-the-pack ranking.

If a salesperson is, for example, a sales savant but lags in customer service, managers can use that insight to help the worker improve individually, while at the same time strategizing for the store's overall success. Put into practice, the model also allows managers to identify team members who excel at selling one specific product category — and encourage them to share their secrets and methods with coworkers.

It might seem that teaching one employee to sell one more set of earbuds or one more lawn chair makes little difference. But applied consistently over time, such personalized product-specific improvement can change the face of a salesforce — and in the end, a whole business. A good manager uses all the tools available. Kamakura and Claro's model makes it possible for every employee on a sales team to be a potential coach for the rest.

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This story originally ran on Rice Business Wisdom.

Based on research from Wagner A. Kamakura, the Jesse H. Jones Professor of Marketing at Jones Graduate School of Business at Rice University.

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Houston-based Adapt2 Solutions has created AI-backed technology to help energy companies make strategic predictions in these unprecedented times. Getty Images

Among the many complications presented by the coronavirus pandemic is coping with power needs. Movie theaters, malls, schools, and stadiums are among the places where energy use has been uneven at best. And the unevenness promises to continue as a lot of locations turn the lights back on but their operating hours remain in flux.

Houston-based Adapt2 Solutions Inc. believes its software can help energy companies power their way through the pandemic-driven haziness of power demand from commercial and residential customers.

"Today's energy companies need the speed and flexibility that cloud-native technology provides to fully leverage the massive amounts of data available to them," Jason Kram, executive vice president of Adapt2 Solutions, said in a December 2019 release.

Kram says that by capitalizing on artificial intelligence, machine learning, and cloud computing, his company's predictive analytics models forecast unexpected fluctuations in power capacity. Amid the pandemic, this technology enables energy companies to map out demand at a time when they're balancing strained revenue and squeezed spending is paramount, according to Kram.

Armed with this forecast data, Adapt2 Solutions' customers — including utility companies, energy traders, and power generators — can more easily plot power production, sales, and purchases, Kram tells InnovationMap. This data can be applied to conventional power, renewable energy, and battery-stored power.

"In times of disruption, big data can inform decision-making for energy companies to optimize energy-market operations with timely and reliable data," Kram says.

Adapt2 Solutions' load forecasting feature generates the predictive analytics models. This feature is embedded within the company's Adapt2 Bid-to-Bill flagship product, which helps energy companies manage front-office and back-office operations. Its other products are Adapt2 Green, designed for the renewable energy market, and Adapt2 Trade-to-Tag, aimed at improving management of energy trades.

"With Adapt2's AI-enabled solutions, we strive to help more customers focus on their core operations and bring business units together on a single platform to create an integrated approach," Kram says.

The company's customers include Consolidated Edison Inc. (ConEd), Duke Energy Corp., the East Kentucky Electric Cooperative, Exelon Corp., Invenergy LLC, Sempra Energy, the Tri-State Generation and Transmission Association, Tyr Energy LLC, and Vistra Energy Corp.

Adapt2 Solutions employs about 40 people, Kram says, and plans to grow its revenue and headcount by 25 percent to 40 percent this year. He says Adapt2 Solutions has managed to turn a profit even though it hasn't taken any outside funding since Francisco Diaz founded the company in 2008.

In March, Inc. magazine placed Adapt2 Solutions at No. 222 on its inaugural list of the fastest-growing private companies in Texas. The company's revenue shot up 72 percent from 2016 to 2018.

"The growth in our business reflects a growth in our customers' business, further validating that we have taken the right steps to help energy enterprises better respond to market and technology changes," Diaz said in a March release.


Jason Kram is the executive vice president of Adapt2 Solutions. Photo courtesy of Adapt2 Solutions

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