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.

Keeping on track with trends is crucial to growing and developing a relationship with your customers, these Rice University researchers found. Getty Images

Rice researcher delves into the importance of trendspotting in consumer behavior

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Every business wants to read consumers' minds: what they love, what they hate. Even more, businesses crave to know about mass trends before they're visible to the naked eye.

In the past, analysts searching for trends needed to pore over a vast range of sources for marketplace indicators. The internet and social media have changed that: marketers now have access to an avalanche of real-time indicators, laden with details about the wishes hidden within customers' hearts and minds. With services such as Trendistic (which tracks individual Twitter terms), Google Insights for Search and BlogPulse, modern marketers are even privy to the real-time conversations surrounding consumers' desires.

Now, imagine being able to analyze all this data across large panels of time – then distilling it so well that you could identify marketing trends quickly, accurately and quantitatively.

Rice Business professor Wagner A. Kamakura and Rex Y. Du of the University of Houston set out to create a model that makes this possible. Because both quantitative and qualitative trendspotting are exploratory endeavors, Kamakura notes, both types of research can yield results that are broad but also inaccurate. To remedy this, Kamakura and Du devised a new model for quickly and accurately refining market data into trend patterns.

Kamakura and Du's model entails taking five simple steps to analyze gathered data using a quantitative method. By following this process of refining the data tens or hundreds of times, then isolating the information into specific seasonal and non-seasonal trends or dynamic trends, researchers can generate steady trend patterns across time panels.

Here's the process:

  • First, gather individual indicators by assembling data from different sources, with the understanding that the information is interconnected. It's crucial to select the data methodically, rather than making random choices, in order to avoid subjectively preselecting irrelevant indicators and blocking out relevant ones. Done sloppily, this first step can generate misleading information.
  • Distill the data into a few common factors. The raw data might include inaccuracies, which must be filtered out to lower the risk of overreacting or noting erroneous indicators.
  • Interpret and identify common trends by understanding the causes of spikes or dips in consumer behavior. It's key to separate non-cyclical and cyclical changes, because exterior events such as holidays or weather can alter behavior.
  • Compare your analysis with previously identified trends and other variables to establish their validity and generate insights. Looking at past performance through the filter of new insights can offer managers important guidance.
  • Project the trend lines you've identified using historical tracking data and their modeling framework. These trend lines can then be extrapolated into near-future projections, allowing managers to better position themselves and be proactive trying to reverse unfavorable trends and leverage positive ones.

It's important to bear in mind that the indicators used for quantitative trendspotting are prone to random and systematic errors, Kamakura writes. The model he devised, however, can filter these errors because it keeps them from appearing across different series of time panels. The result: better ability to identify genuine movements and general trends, free from the influence of seasonal events and from random error.

It goes without saying that the information and persuasiveness offered by the internet are inevitably attended by noise. For marketers, this means that without filtering, some trends show spikes for temporary items – mere viral jolts that can skew market research.

Kamakura and Du's model helps sidestep this problem by blending available historical data analysis, large time panels and movements while avoiding errors common to more traditional methods. For managers longing to glimpse the next big thing, this analytical model can reveal emerging consumer movements with clarity – just as they're becoming the future.

(For the mathematically inclined, and those comfortable with Excel macros and Add-Ins, who want to try trendspotting on their own tracking data, Kamakura's Analytical Tools for Excel (KATE) can be downloaded for free at http://wak2.web.rice.edu/bio/Kamakura_Analytic_Tools.html.)

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This article originally appeared on Rice Business Wisdom.

Wagner A. Kamakura is Jesse H. Jones Professor of Marketing at Jones Graduate School of Business at Rice University.

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Houston hospital names leading cancer scientist as new academic head

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Houston Methodist Academic Institute has named cancer clinician and scientist Dr. Jenny Chang as its new executive vice president, president, CEO, and chief academic officer.

Chang was selected following a national search and will succeed Dr. H. Dirk Sostman, who will retire in February after 20 years of leadership. Chang is the director of the Houston Methodist Dr. Mary and Ron Neal Cancer Center and the Emily Herrmann Presidential Distinguished Chair in Cancer Research. She has been with Houston Methodist for 15 years.

Over the last five years, Chang has served as the institute’s chief clinical science officer and is credited with strengthening cancer clinical trials. Her work has focused on therapy-resistant cancer stem cells and their treatment, particularly relating to breast cancer.

Her work has generated more than $35 million in funding for Houston Methodist from organizations like the National Institutes of Health and the National Cancer Institute, according to the health care system. In 2021, Dr. Mary Neal and her husband Ron Neal, whom the cancer center is now named after, donated $25 million to support her and her team’s research on advanced cancer therapy.

In her new role, Chang will work to expand clinical and translational research and education across Houston Methodist in digital health, robotics and bioengineered therapeutics.

“Dr. Chang’s dedication to Houston Methodist is unparalleled,” Dr. Marc L. Boom, Houston Methodist president and CEO, said in a news release. “She is committed to our mission and to helping our patients, and her clinical expertise, research innovation and health care leadership make her the ideal choice for leading our academic mission into an exciting new chapter.”

Chang is a member of the American Association of Cancer Research (AACR) Stand Up to Cancer Scientific Advisory Council. She earned her medical degree from Cambridge University in England and completed fellowship training in medical oncology at the Royal Marsden Hospital/Institute for Cancer Research. She earned her research doctorate from the University of London.

She is also a professor at Weill Cornell Medical School, which is affiliated with the Houston Methodist Academic Institute.

Texas A&M awarded $1.3M federal grant to develop clean energy tech from electronic waste

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Texas A&M University in College Station has received a nearly $1.3 million federal grant for development of clean energy technology.

The university will use the $1,280,553 grant from the U.S. Department of Energy to develop a cost-effective, sustainable method for extracting rare earth elements from electronic waste.

Rare earth elements (REEs) are a set of 17 metallic elements.

“REEs are essential components of more than 200 products, especially high-tech consumer products, such as cellular telephones, computer hard drives, electric and hybrid vehicles, and flat-screen monitors and televisions,” according to the Eos news website.

REEs also are found in defense equipment and technology such as electronic displays, guidance systems, lasers, and radar and sonar systems, says Eos.

The grant awarded to Texas A&M was among $17 million in DOE grants given to 14 projects that seek to accelerate innovation in the critical materials sector. The federal Energy Act of 2020 defines a critical material — such as aluminum, cobalt, copper, lithium, magnesium, nickel, and platinum — as a substance that faces a high risk of supply chain disruption and “serves an essential function” in the energy sector.

“DOE is helping reduce the nation’s dependence on foreign supply chains through innovative solutions that will tap domestic sources of the critical materials needed for next-generation technologies,” says U.S. Energy Secretary Jennifer Granholm. “These investments — part of our industrial strategy — will keep America’s growing manufacturing industry competitive while delivering economic benefits to communities nationwide.”

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This article originally appeared on EnergyCapital.

Biosciences startup becomes Texas' first decacorn after latest funding

A Dallas-based biosciences startup whose backers include millionaire investors from Austin and Dallas has reached decacorn status — a valuation of at least $10 billion — after hauling in a series C funding round of $200 million, the company announced this month. Colossal Biosciences is reportedly the first Texas startup to rise to the decacorn level.

Colossal, which specializes in genetic engineering technology designed to bring back or protect various species, received the $200 million from TWG Global, an investment conglomerate led by billionaire investors Mark Walter and Thomas Tull. Walter is part owner of Major League Baseball’s Los Angeles Dodgers, and Tull is part owner of the NFL’s Pittsburgh Steelers.

Among the projects Colossal is tackling is the resurrection of three extinct animals — the dodo bird, Tasmanian tiger and woolly mammoth — through the use of DNA and genomics.

The latest round of funding values Colossal at $10.2 billion. Since launching in 2021, the startup has raised $435 million in venture capital.

In addition to Walter and Tull, Colossal’s investors include prominent video game developer Richard Garriott of Austin and private equity veteran Victor Vescov of Dallas. The two millionaires are known for their exploits as undersea explorers and tourist astronauts.

Aside from Colossal’s ties to Dallas and Austin, the startup has a Houston connection.

The company teamed up with Baylor College of Medicine researcher Paul Ling to develop a vaccine for elephant endotheliotropic herpesvirus (EEHV), the deadliest disease among young elephants. In partnership with the Houston Zoo, Ling’s lab at the Baylor College of Medicine has set up a research program that focuses on diagnosing and treating EEHV, and on coming up with a vaccine to protect elephants against the disease. Ling and the BCMe are members of the North American EEHV Advisory Group.

Colossal operates research labs Dallas, Boston and Melbourne, Australia.

“Colossal is the leading company working at the intersection of AI, computational biology, and genetic engineering for both de-extinction and species preservation,” Walter, CEO of TWG Globa, said in a news release. “Colossal has assembled a world-class team that has already driven, in a short period of time, significant technology innovations and impact in advancing conservation, which is a core value of TWG Global.”

Well-known genetics researcher George Church, co-founder of Colossal, calls the startup “a revolutionary genetics company making science fiction into science fact.”

“We are creating the technology to build de-extinction science and scale conservation biology,” he added, “particularly for endangered and at-risk species.”