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 scores $120M in new cancer research and prevention grants

cancer funding

The Cancer Prevention and Research Institute of Texas has granted more than $120 million to Houston organizations and companies as part of 73 new awards issued statewide.

The funds are part of nearly $154 million approved by the CPRIT's governing board earlier this month, bringing the organization's total investment in cancer prevention and research to more than $4 billion since its inception.

“Today marks an important milestone for CPRIT and for every Texan affected by cancer,” CEO Kristen Doyle said in a news release. “Texas has invested $4 billion in the fight against one of the world’s greatest public health challenges. Over 16 years, that support has helped Texas lead the search for breakthrough treatments, develop new cancer-fighting drugs and devices, and—most importantly—save tens of thousands of lives through early cancer detection and prevention. Every Texan should know this effort matters, and we’re not finished yet. Together, we will conquer cancer.”

A portion of the funding will go toward recruiting leading cancer researchers to Houston. CPRIT granted $5 million to bring John Quackenbush to Baylor College of Medicine. Quackenbush comes from the Harvard T.H. Chan School of Public Health and is an expert in computational and systems biology. His research focuses on complex genomic data to understand cancer and develop targeted therapies.

The University of Texas M.D. Anderson Cancer Center also received $3 million to recruit Irfan Asangani, an associate professor at the University of Pennsylvania Perelman School of Medicine. His research focuses on how chromatin structure and epigenetic regulation drive the development and progression of cancer, especially prostate cancer.

Other funds will go towards research on a rare, aggressive kidney cancer that impacts children and young adults; screening programs for breast and cervical cancer; and diagnostic technology.

In total, cancer grants were given to:

  • The University of Texas M.D. Anderson Cancer Center: $29.02 million
  • Baylor College of Medicine: $15.04 million
  • The University of Texas Health Science Center at Houston: $9.37 million
  • Texas A&M University System Health Science Center: $1.2 million
  • University of Houston: $900,000

Additional Houston-based companies landed grants, including:

  • Crossbridge Bio Inc.: $15.01 million
  • OncoMAGNETx Inc.: $13.97 million
  • Immunogenesis Inc.: $10.85 million
  • Diakonos Oncology Corporation: $7.16 million
  • Iterion Therapeutics Inc.: $7.13 million
  • NovaScan Inc.: $3.7 million
  • EMPIRI Inc.: $2.59 million
  • Air Surgical Inc.: $2.58 million
  • Light and Salt Association: $2.45 million

See the full list of awards here.

U.S. News names 5 Houston suburbs as the best places to retire in 2026

Retirement Report

Houston-area suburbs should be on the lookout for an influx of retirees in 2026. A new study by U.S. News and World Report has declared The Woodlands and Spring as the fourth and fifth best cities to retire in America, with three other local cities making the top 25.

The annual report, called "250 Best Places to Retire in the U.S. in 2026" initially compared 850 U.S. cities, and narrowed the list down to a final 250 cities (up from 150 previously). Each locale was analyzed across six indexes: quality of life for individuals reaching retirement age, value (housing affordability and cost of living), health care quality, tax-friendliness for retirees, senior population and migration rates, and the strength of each city's job market.

Midland, Michigan was crowned the No. 1 best place to retire in 2026. The remaining cities that round out the top five are Weirton, West Virginia (No. 2) and Homosassa Springs, Florida (No. 3).

According to U.S. News, about 15 percent of The Woodlands' population is over the age of 65. The median household income in this suburb is $139,696, far above the national average median household income of $79,466.

Though The Woodlands has a higher cost of living than many other places in the country, the report maintains that the city "offers a higher value of living compared to similarly sized cities."

"If you want to buy a house in The Woodlands, the median home value is $474,279," the city's profile on U.S. News says. "And if you're a renter, you can expect the median rent here to be $1,449." For comparison, the report says the national average home value is $370,489.

Spring ranked as the fifth best place to retire in 2026, boasting a population of more than 68,000 residents, 11 percent of whom are seniors. This suburb is located less than 10 miles south of The Woodlands, while still being far enough away from Houston (about 25 miles) for seniors to escape big city life for the comfort of a smaller community.

"Retirees are prioritizing quality of life over affordability for the first time since the beginning of the COVID-19 pandemic," said U.S. News contributing editor Tim Smart in a press release.

The median home value in Spring is lower than the national average, at $251,247, making it one of the more affordable places to buy a home in the Houston area. Renters can expect to pay a median $1,326 in monthly rent, the report added.

Elsewhere in Houston, Pearland ranked as the 17th best place to retire for 2026, followed by Conroe (No. 20) and League City (No. 25).

Other Texas cities that ranked among the top 50 best places to retire nationwide include Victoria (No. 12), San Angelo (No. 28), and Flower Mound (No. 37).

The top 10 best U.S. cities to retire in 2026 are:

  • No. 1 – Midland, Michigan
  • No. 2 – Weirton, West Virginia
  • No. 3 – Homosassa Springs, Florida
  • No. 4 – The Woodlands, Texas
  • No. 5 – Spring, Texas
  • No. 6 – Rancho Rio, New Mexico
  • No. 7 – Spring Hill, Florida
  • No. 8 – Altoona, Pennsylvania
  • No. 9 – Palm Coast, Florida
  • No. 10 – Lynchburg, Virginia
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This article originally appeared on CultureMap.com.

Micro-nuclear reactor to launch at Texas A&M innovation campus in 2026

nuclear pilot

The Texas A&M University System and Last Energy plan to launch a micro-nuclear reactor pilot project next summer at the Texas A&M-RELLIS technology and innovation campus in Bryan.

Washington, D.C.-based Last Energy will build a 5-megawatt reactor that’s a scaled-down version of its 20-megawatt reactor. The micro-reactor initially will aim to demonstrate safety and stability, and test the ability to generate electricity for the grid.

The U.S. Department of Energy (DOE) fast-tracked the project under its New Reactor Pilot Program. The project will mark Last Energy’s first installation of a nuclear reactor in the U.S.

Private funds are paying for the project, which Robert Albritton, chairman of the Texas A&M system’s board of regents, said is “an example of what’s possible when we try to meet the needs of the state and tap into the latest technologies.”

Glenn Hegar, chancellor of the Texas A&M system, said the 5-megawatt reactor is the kind of project the system had in mind when it built the 2,400-acre Texas A&M-RELLIS campus.

The project is “bold, it’s forward-looking, and it brings together private innovation and public research to solve today’s energy challenges,” Hegar said.

As it gears up to build the reactor, Last Energy has secured a land lease at Texas A&M-RELLIS, obtained uranium fuel, and signed an agreement with DOE. Founder and CEO Bret Kugelmass said the project will usher in “the next atomic era.”

In February, John Sharp, chancellor of Texas A&M’s flagship campus, said the university had offered land at Texas A&M-RELLIS to four companies to build small modular nuclear reactors. Power generated by reactors at Texas A&M-RELLIS may someday be supplied to the Electric Reliability Council of Texas (ERCOT) grid.

Also in February, Last Energy announced plans to develop 30 micro-nuclear reactors at a 200-acre site about halfway between Lubbock and Fort Worth.

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This article originally appeared on our sister site, EnergyCapitalHTX.com.