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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Houston biotech co. raises $11M to advance ALS drug development

drug money

Houston-based clinical-stage biotechnology company Coya Therapeutics (NASDAQ: COYA) has raised $11.1 million in a private investment round.

India-based pharmaceuticals company Dr. Reddy’s Laboratories Inc. led the round with a $10 million investment, according to a news release. New York-based investment firm Greenlight Capital, Coya’s largest institutional shareholder, contributed $1.1 million.

The funding was raised through a definitive securities purchase agreement for the purchase and sale of more than 2.5 million shares of Coya's common stock in a private placement at $4.40 per share.

Coya reports that it plans to use the proceeds to scale up manufacturing of low-dose interleukin-2 (IL-2), which is a component of its COYA 302 and will support the commercial readiness of the drug. COYA 302 enhances anti-inflammatory T cell function and suppresses harmful immune activity for treatment of Amyotrophic Lateral Sclerosis (ALS), Frontotemporal Dementia (FTD), Parkinson’s disease and Alzheimer’s disease.

The company received FDA acceptance for its investigational new drug application for COYA 302 for treating ALS and FTD this summer. Its ALSTARS Phase 2 clinical trial for ALS treatment launched this fall in the U.S. and Canada and has begun enrolling and dosing patients. Coya CEO Arun Swaminathan said in a letter to investors that the company also plans to advance its clinical programs for the drug for FTD therapy in 2026.

Coya was founded in 2021. The company merged with Nicoya Health Inc. in 2020 and raised $10 million in its series A the same year. It closed its IPO in January 2023 for more than $15 million. Its therapeutics uses innovative work from Houston Methodist's Dr. Stanley H. Appel.

New accelerator for AI startups to launch at Houston's Ion this spring

The Collectiv Foundation and Rice University have established a sports, health and wellness startup accelerator at the Ion District’s Collectiv, a sports-focused venture capital platform.

The AI Native Dual-Use Sports, Health & Wellness Accelerator, scheduled to formally launch in March, will back early-stage startups developing AI for the sports, health and wellness markets. Accelerator participants will gain access to a host of opportunities with:

  • Mentors
  • Advisers
  • Pro sports teams and leagues
  • University athletics programs
  • Health care systems
  • Corporate partners
  • VC firms
  • Pilot projects
  • University-based entrepreneurship and business initiatives

Accelerator participants will focus on sports tech verticals inlcuding performance and health, fan experience and media platforms, data and analytics, and infrastructure.

“Houston is quickly becoming one of the most important innovation hubs at the intersection of sports, health, and AI,” Ashley DeWalt, co-founder and managing partner of The Collectiv and founder of The Collectiv Foundation, said in a news release.

“By launching this platform with Rice University in the Ion District,” he added, “we are building a category-defining acceleration engine that gives founders access to world-class research, global sports properties, hospital systems, and venture capital. This is about turning sports-validated technology into globally scalable companies at a moment when the world’s attention is converging on Houston ahead of the 2026 World Cup.”

The Collectiv accelerator will draw on expertise from organizations such as the Rice-Houston Methodist Center for Human Performance, Rice Brain Institute, Rice Gateway Project and the Texas Medical Center.

“The combination of Rice University’s research leadership, Houston’s unmatched health ecosystem, and The Collectiv’s operator-driven investment platform creates a powerful acceleration engine,” Blair Garrou, co-founder and managing partner of the Mercury Fund VC firm and a senior adviser for The Collectiv, added in the release.

Additional details on programming, partners and application timelines are expected to be announced in the coming weeks.

4 Houston-area schools excel with best online degree programs in U.S.

Top of the Class

Four Houston-area universities have earned well-deserved recognition in U.S. News & World Report's just-released rankings of the Best Online Programs for 2026.

The annual rankings offer insight into the best American universities for students seeking a flexible and affordable way to attain a higher education. In the 2026 edition, U.S. News analyzed nearly 1,850 online programs for bachelor's degrees and seven master's degree disciplines: MBA, business (non-MBA), criminal justice, education, engineering, information technology, and nursing.

Many of these local schools are also high achievers in U.S. News' separate rankings of the best grad schools.

Rice University tied with Texas A&M University in College Station for the No. 3 best online master's in information technology program in the U.S., and its online MBA program ranked No. 21 nationally.

The online master's in nursing program at The University of Texas Medical Branch in Galveston was the highest performing master's nursing degree in Texas, and it ranked No. 19 nationally.

Three different programs at The University of Houston were ranked among the top 100 nationwide:
  • No. 18 – Best online master's in education
  • No. 59 – Best online master's in business (non-MBA)
  • No. 89 – Best online bachelor's program
The University of Houston's Clear Lake campus ranked No. 65 nationally for its online master's in education program.

"Online education continues to be a vital path for professionals, parents, and service members seeking to advance their careers and broaden their knowledge with necessary flexibility," said U.S. News education managing editor LaMont Jones in a press release. "The 2026 Best Online Programs rankings are an essential tool for prospective students, providing rigorous, independent analysis to help them choose a high-quality program that aligns with their personal and professional goals."

A little farther outside Houston, two more universities – Sam Houston State University in Huntsville and Texas A&M University in College Station – stood out for their online degree programs.

Sam Houston State University

  • No. 5 – Best online master's in criminal justice
  • No. 30 – Best online master's in information technology
  • No. 36 – Best online master's in education
  • No. 77 – Best online bachelor's program
  • No. 96 – Best online master's in business (non-MBA)
Texas A&M University
  • No. 3 – Best online master's in information technology (tied with Rice)
  • No. 3 – Best online master's in business (non-MBA)
  • No. 8 – Best online master's in education
  • No. 9 – Best online master's in engineering
  • No. 11 – Best online bachelor's program
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This article originally appeared on CultureMap.com.