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 student startup lands $1.85M to launch medical drone network

critical cargo

Students at Rice University have developed a medical cargo drone transport system to help deliver sensitive medical supplies and improve mobile healthcare efforts.

Haast Autonomous is the brainchild of graduating seniors Ege Halac, Jason Chen and Santiago Brent, who got their venture idea off the ground with help from the Liu Idea Lab for Innovation and Entrepreneurship (Lilie) Summer Venture Studio. The founders have developed the prototype at Rice’s Oshman Engineering Design Kitchen (OEDK) with fellow Rice researchers Felix Hasson, Ethan Javedan, Kenna Sanders and Caden Schmidt.

The startup has raised $1.85 million in pre-seed funding, according to Rice. The founders plan to focus on Haast full-time following graduation. They said they aim to launch pilot trials in 2027 and head to market later that year.

“We need better alternatives for a fast, safe and on-demand system of transport for life-critical cargo,” Halac said in a news release from Rice.

The Haast team has developed a custom aircraft with software that manages dispatch, routes, and chain of custody to assist in how materials move between sites in centralized medical systems. Generally, the transportation of medical supplies and materials between facilities and points of care relies on ground shipping or expensive air transport.

Haast Autonomous’ aircraft can take off and land vertically, and is designed around a mission profile of 50 to 62 miles. It can carry a payload of at least 5 pounds, with future versions intended to scale up in size. It also includes a built-in payload bay that regulates temperature, pressure, vibration and tilt to protect sensitive contents such as patient samples, antivenom or poisoning kits and radioligands or other therapies, according to Rice.

At first, the company envisioned the mission to be centered around transplants, but saw the product being best suited for a variety of operations.

“What we realized is that the platform we are building is suited for medicine, but it really underlies a much larger problem of mission-critical transport across industries,” Brent added in the news release. “We are building the fastest, most secure logistics chain for the world’s most sensitive cargo.”

Haast Autonomous was recognized at the 2026 Oshman Engineering Design Showcase and Competition, where it won Best Aerospace or Transportation Technology. It also performed well in the 2026 Napier Rice Launch Challenge.

In the future, Haast Autonomous plans to deploy a fleet of aircraft. The software will be designed to assist hospitals in requesting flights and tracking deliveries in real time.

“The drone is only part of the solution,” Chen also added in the release. “What matters is moving something from point A to point B in a way that fits into how hospitals already operate.”

Houston scientist wins prestigious Pew Scholar award for brain cancer research

standout scholar

Christina Tringides, an assistant professor of materials science and nanoengineering at Rice University, is one of 21 scientists to win a prestigious Pew Biomedical Scholar award.

She is the first faculty member from Rice to win the distinction, which provides $300,000 over four years for advances in biomedicine, according to the university. The awards are granted to researchers who are in the first few years at the assistant professor level.

In Tringides’ case, the funding will support her innovative new method of modeling glioblastoma, a common and extremely aggressive form of brain cancer. Thanks to producing its own blood supply, glioblastoma spreads quickly, weaving tendrils of blighted tissue throughout the brain. Because of this, surgery is difficult and conventional therapies ineffective.

Understanding the way glioblastoma spreads is crucial to the search for a cure. Tringides is using hydrogels that mimic the brain’s extracellular matrix. Using cultures and a microscopic labyrinth, her team can see how the cancer spreads, bonds with neurons and changes cell wall activity. Essentially, Tringides has devised an intelligence test for tumors in hopes of learning how to outsmart them.

“As cancer crawls through the maze, we can look at how it is interacting with the neurons more and more, and measure how electrical activity is changing as a result,” she said in a news release from Rice.

Examining how cancer cells grow can reveal which conditional changes slow them down. Finding ways to alter the structure of brain matter in a way that makes it inhospitable to the cancer could lead to therapies that would impede growth or even reverse it. Using her custom-made ersatz brain maze makes it easier to observe changes than it would be in a patient’s brain.

“Imaging synapses is time-intensive ⎯ it can involve large data files that are hard to visualize, but if we know that the only place where we might have a synapse is this tiny 1-by-4-by-10 micron channel, it makes it much faster and reliable to image them,” Tringides said.

Born in Ames, Iowa, Tringides received her doctorate in biophysics from Harvard before joining Rice in 2024 through a Cancer Prevention and Research Institute of Texas (CPRIT) recruitment award.

Her research was also one of the first four projects to receive research awards through the Rice Brain Institute and TMC Neuro Collaboration Seed Grant Program.

Texas residents earn 11th highest income in U.S., says 2026 study

Money Matters

A new WalletHub study comparing income disparities across America has ranked Texas residents No. 11 on the list of states with the highest earning residents in the nation.

The report, "States Where People Have the Highest Income (2026)," analyzed U.S. Census Bureau income data in all 50 states and the District of Columbia. The report evaluated the average annual income of the top five percent, the median annual household income, and the average annual income of the bottom 20 percent of residents in every state, all adjusted for the cost of living.

The report's data revealed the top five percent of Texans, the highest earners, make $520,378 on average yearly after adjusting for the cost of living. That's the seventh-highest income among the top five percent of earners nationwide.

Meanwhile, the median annual income of a Texas household is just under $76,000. The bottom 20 percent of Texas residents make $17,651 a year, the report found.

For additional context, the latest data from the Federal Reserve shows an American household's median yearly income is about $83,700. WalletHub analyst Chip Lupo also found that the highest earning 10 percent of individuals in the U.S. earn over 12 times more than those in the lowest-earning 10 percent, based on the latest Census data.

"By measuring the income of various percentiles against a state's median income, we can better identify where income disparities are more prevalent, which could help us better understand why residents of certain states struggle more to make ends meet," said Lupo.

Virginia is the state where residents earn the highest income in the U.S., WalletHub said. Based on the report's findings, the top five percent of Virginians make $545,097 on average per year after adjusting for the cost of living. The median annual income of a Virginia household comes out to $95,339, and the bottom 20 percent of residents make $19,671 annually on average.

Conversely, West Virginia is the state where people have the lowest income in the U.S. A West Virginia household makes a median annual income of $56,610, the third-lowest nationally, and the bottom 20 percent of residents make $13,260 on average per year, which is the fifth-lowest in the nation. The top five percent of West Virginians make $372,218 on average per year.

The top 10 states where residents have the highest income are:

  • No. 1 – Virginia
  • No. 2 – New York
  • No. 3 – New Jersey
  • No. 4 – Washington
  • No. 5 – Connecticut
  • No. 6 – Utah
  • No. 7 – Colorado
  • No. 8 – Minnesota
  • No. 9 – Illinois
  • No. 10 – Massachusetts

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This article originally appeared on CultureMap.com.