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

Houston voices

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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Rice lands $15M U.S. Army award to launch next-gen wireless research center

defense funding

The U.S. Army Research Office has awarded Rice University $15 million to establish a new center for next-generation sensing and communications.

The five-year research center—dubbed the Center for Large Aperture Secure Sensing, Imaging and Communications (CLASSIC)—will unite researchers from universities and national laboratories to develop advanced antenna technologies for future wireless systems. Edward Knightly, the Sheafor-Lindsay Professor of Electrical and Computer Engineering at Rice, will lead the center that “combines expertise in wireless networking, antennas, radar, artificial intelligence, circuits and physics to address growing demands on wireless systems,” according to Rice.

“The challenges we’re tackling require advances that span physics, hardware, communications and computing," Knightly said in a news release. “By combining those strengths in a single center, we can accelerate the development and demonstration of technologies that would not be possible through individual efforts alone.”

Joining Knightly will be Ashutosh Sabharwal of Rice, Sensen Li of the University of Texas at Austin, Hou-Tong Chen of Los Alamos National Laboratory, Danijela Cabric of UCLA, Josep M. Jornet and Tommaso Melodia of Northeastern University, Daniel M. Mittleman of Brown University, and Willie Padilla of Duke University.

Industry partners include Booz Allen Hamilton, Intel, Keysight, Lockheed Martin, MITRE, Northrop Grumman, Qualcomm and Raytheon.

CLASSIC researchers will investigate how large-scale antenna arrays (ELSAAs) can expand the capabilities of wireless systems where technology is limited.

ELSAAs use thousands of coordinated antenna elements to direct radio waves. Researchers aim to develop ways to use the technology to help maintain steady communication when signals are blocked or disrupted and to detect and generate detailed images of concealed objects.

Along with ELSAAs, the center will work to develop sensing techniques for threat detection, study wireless jamming and build resilient high-speed wireless networks. Researchers will ultimately validate the technology in labs and via drone-based field trials.

CLASSIC will also work on developing an AI-driven modeling framework that will simulate complex electromagnetic environments in real time.

“This award demonstrates Rice’s leadership in tackling complex national research challenges through collaboration across disciplines and institutions,” David Sholl, executive vice president for research at Rice, added in the release.

UH scores $18M NIH grant for chronic disease research

research funding

The University of Houston has received a coveted $18.8 million grant from the National Institutes of Health to launch a program to address the root causes of chronic disease.

Only 22 institutions nationwide receive this NIH award, and the 5-year process aligns with the newly established UH Health’s mission to expand healthcare innovations in Texas and beyond. The initiative will be housed in the UH Population Health department.

"This generous funding allows us to directly confront the root causes of chronic illness that place a heavy burden on so many families," Dr. Jonathan McCullers, vice president for health affairs at UH, said in a news release. "With the recent launch of UH Health, we have an unprecedented opportunity to translate scientific discovery into healthier outcomes for our communities by bringing together experts from across the university to improve health where it matters most.”

Through the program, UH researchers from different areas of expertise will work together to address the challenges of chronic illness by looking at biological, social and behavioral factors.

According to the university, chronic diseases like heart disease, diabetes, strokes and others are the leading cause of illness, disability and death in the U.S. They account for 90 percent of the nation’s $5.3 trillion in annual healthcare spending.

Bettina Beech, chief of population health and translational science at UH, serves as principal investigator for the program.

“Chronic disease management largely happens during the 8,700 hours each year that people are not visiting their healthcare provider,” Beech added in the news release. “While healthcare is indispensable, it only accounts for 20 percent of how health is created — genetics accounts for another 10 percent, and the other 70 percent is determined by behavior, social conditions and environment.”

With the funds from the grant, UH will also be able to expand research infrastructure, add to community partnerships, support complementary research, and invest in early-career investigators, according to the news release. UH also aims to develop solutions that could help ease the economic burden of chronic disease.

Report: Where Texas ranks among best and worst states to live in 2026

Texas Talk

After earning its worst-ever ranking last year, Texas has improved slightly on an evaluation of the best states to live, but it's still at the bottom of the pack.

Each year, WalletHub's analysts compare all 50 states using 51 livability metrics to measure their affordability, economy, education and health, quality of life, and safety. Factors that were weighed include the cost of living, homeownership rates, population and income growth rates, wealth gaps, public school system quality, road quality, crime rates, and many others.

The Lone Star State landed at No. 36 in 2026, making it the 15th worth state to live right now. That's on par with its 2024 ranking, and it's a two-spot improvement over its 2025 performance.

While Texas residents can brag about living in a state with the No. 1 highest number of restaurants per capita and the 7th best quality of life in the country, that's about it. Texas earned middling-to-poor scores among the four remaining livability rankings: safety (No. 33), affordability (No. 35), economy (No. 37), and education and health (No. 40).

Here's how Texas fared in other nationwide rankings in the study:

  • No. 27 – Income Growth
  • No. 30 – Housing Costs
  • No. 39 – Percentage of Population in Poverty
  • No. 42 – Percentage of Adults in Fair or Poor Health
  • No. 46 – Homeownership Rate
  • No. 49 – Percentage of Population Aged 25 and Older with a High School Diploma or Higher
  • No. 48 – Average Weekly Work Hours
  • No. 50 – Percentage of Insured Population

Texas has a lot of work to do to improve its livability for all of its residents, but especially for women, according to several other 2026 WalletHub studies. Texas is the fourth-worst state for women, the ninth-worst state for working mothers, and the seventh-worst place to have a baby based on limited access to maternal and pediatric healthcare.

At the very bottom of the report is New Mexico, ranking 50th overall, with Louisiana (No. 49), Mississippi (No. 48), Alaska (No. 47), and Arkansas (No. 46) rounding out the bottom five.

After holding on as the No. 1 best state to live for a few years in a row, Massachusetts now ranks No. 4 and was overtaken by Idaho (No. 1), New Jersey (No. 2), and Wisconsin (No. 3). New Hampshire rounds out the top five best states to live.

WalletHub's top 10 best states to live in 2026 are:

  • No. 1 – Idaho
  • No. 2 – New Jersey
  • No. 3 – Wisconsin
  • No. 4 – Massachusetts
  • No. 5 – New Hampshire
  • No. 6 – Wyoming
  • No. 7 – Utah
  • No. 8 – Minnesota
  • No. 9 – Pennsylvania
  • No. 10 – Florida
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This article originally appeared on CultureMap.com.