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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Nominations are now open for the 2025 Houston Innovation Awards

Calling All Innovators

Calling all Houston innovators: The Houston Innovation Awards return this fall to celebrate the best and brightest in the Houston innovation ecosystem right now.

Presented by InnovationMap, the fifth annual Houston Innovation Awards will take place November 5 at TMC Helix Park.

The awards program will honor the top startups and innovators in Houston across 10 categories, and we're asking you to nominate the most deserving Houston innovators and innovative companies today.

This year's categories are:

  • Minority-founded Business, honoring an innovative startup founded or co-founded by BIPOC or LGBTQ+ representation.
  • Female-founded Business, honoring an innovative startup founded or co-founded by a woman.
  • Energy Transition Business, honoring an innovative startup providing a solution within renewables, climatetech, clean energy, alternative materials, circular economy, and beyond.
  • Health Tech Business, honoring an innovative startup within the health and medical technology sectors.
  • Deep Tech Business, honoring an innovative startup providing technology solutions based on substantial scientific or engineering challenges, including those in the AI, robotics, and space sectors.
  • Startup of the Year (People's Choice), honoring a startup celebrating a recent milestone or success. The winner will be selected by the community via an interactive voting experience.
  • Scaleup of the Year, honoring an innovative later-stage startup that's recently reached a significant milestone in company growth.
  • Incubator/Accelerator of the Year, honoring a local incubator or accelerator that is championing and fueling the growth of Houston startups.
  • Mentor of the Year, honoring an individual who dedicates their time and expertise to guide and support budding entrepreneurs.
  • Trailblazer, honoring an innovator who's made a lasting impact on the Houston innovation community.

Nominations may be made on behalf of yourself, your organization, and other leaders in the local innovation scene. The nomination period closes on August 31, so don't delay — nominate today at this link, or fill out the embedded form below.

Our panel of esteemed judges will review the nominations, and determine the finalists and winners. Finalists will be unveiled on September 30, and the 2025 Houston Innovation Awards winners will be announced live at our event on November 5.

Tickets will go on sale this fall. Stay tuned for that announcement, as well as more fanfare leading up to the 2025 Houston Innovation Awards.

Nominate now:

Interested in Innovation Awards sponsorship opportunities? Please contact sales@innovationmap.com.

MD Anderson launches $10M collaboration to advance personalized cancer treatment tech

fighting cancer

The University of Texas MD Anderson Cancer Center and Japan’s TOPPAN Holdings Inc. have announced a strategic collaboration to co-develop TOPPAN Holdings’ 3D cell culture, or organoid, technology known as invivoid.

The technology will be used as a tool for personalized cancer treatments and drug screening efforts, according to a release from MD Anderson. TOPPAN has committed $10 million over five years to advance the joint research activities.

“The strategic alliance with MD Anderson paves a promising path toward personalized cancer medicine," Hiroshi Asada, head of the Business Innovation Center at TOPPAN Holdings, said in a news release.

Invivoid is capable of establishing organoid models directly from patient biopsies or other tissues in a way that is faster and more efficient. Researchers may be able to test a variety of potential treatments in the laboratory to understand which approach may work best for the patient, if validated clinically.

“Organoids allow us to model the three-dimensional complexity of human cancers in the lab, thus allowing us to engineer a powerful translational engine—one that could not only predict how patients will respond to therapy before treatment begins but also could help to reimagine how we discover and validate next-generation therapies," Dr. Donna Hansel, division head of pathology and laboratory medicine at MD Anderson, added in the news release. “Through this collaboration, we hope to make meaningful progress in modeling cancer biology for therapeutic innovation.”

The collaboration will build upon preclinical research previously conducted by MD Anderson and TOPPAN. The organizations will work collaboratively to obtain College of American Pathologists (CAP) and Clinical Laboratory Improvement Amendments (CLIA) certifications for the technology, which demonstrate a commitment to high-quality patient care. Once the certifications are obtained, they plan to conduct observational clinical studies and then prospective clinical studies.

“We believe our proprietary invivoid 3D cell culture technology, by enabling the rapid establishment of organoid models directly from patient biopsies, has strong potential to help identify more effective treatment options and reduce the likelihood of unnecessary therapies,” Asada added in the release. “Through collaboration on CAP/CLIA certification and clinical validation, we aim to bring this innovation closer to real-world patient care and contribute meaningfully to the advancement of cancer medicine."