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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TMC, Memorial Hermann launch partnership to spur new patient care technologies

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Texas Medical Center and Memorial Hermann Health System have launched a new collaboration for developing patient care technology.

Through the partnership, Memorial Hermann employees and physicians will now be able to participate in the TMC Center for Device Innovation (CDI), which will assist them in translating product innovation ideas into working prototypes. The first group of entrepreneurs will pitch their innovations in early 2026, according to a release from TMC.

“Memorial Hermann is excited to launch this new partnership with the TMC CDI,” Ini Ekiko Thomas, vice president of information technology at Memorial Hermann, said in the news release. “As we continue to grow (a) culture of innovation, we look forward to supporting our employees, affiliated physicians and providers in new ways.”

Mentors from Memorial Hermann, TMC Innovation and industry experts with specialties in medicine, regulatory strategy, reimbursement planning and investor readiness will assist with the program. The innovators will also gain access to support systems like product innovation and translation strategy, get dedicated engineering and machinist resources and personal workbench space at the CDI.

“The prototyping facilities and opportunities at TMC are world-class and globally recognized, attracting innovators from around the world to advance their technologies,” Tom Luby, chief innovation officer at TMC Innovation Factor, said in the release.

Memorial Hermann says the partnership will support its innovation hub’s “pilot and scale approach” and hopes that it will extend the hub’s impact in “supporting researchers, clinicians and staff in developing patentable, commercially viable products.”

“We are excited to expand our partnership with Memorial Hermann and open the doors of our Center for Device Innovation to their employees and physicians—already among the best in medical care,” Luby added in the release. “We look forward to seeing what they accomplish next, utilizing our labs and gaining insights from top leaders across our campus.”

Google to invest $40 billion in AI data centers in Texas

Google is investing a huge chunk of money in Texas: According to a release, the company will invest $40 billion on cloud and artificial intelligence (AI) infrastructure, with the development of new data centers in Armstrong and Haskell counties.

The company announced its intentions at a meeting on November 14 attended by federal, state, and local leaders including Gov. Greg Abbott who called it "a Texas-sized investment."

Google will open two new data center campuses in Haskell County and a data center campus in Armstrong County.

Additionally, the first building at the company’s Red Oak campus in Ellis County is now operational. Google is continuing to invest in its existing Midlothian campus and Dallas cloud region, which are part of the company’s global network of 42 cloud regions that deliver high-performance, low-latency services that businesses and organizations use to build and scale their own AI-powered solutions.

Energy demands

Google is committed to responsibly growing its infrastructure by bringing new energy resources onto the grid, paying for costs associated with its operations, and supporting community energy efficiency initiatives.

One of the new Haskell data centers will be co-located with — or built directly alongside — a new solar and battery energy storage plant, creating the first industrial park to be developed through Google’s partnership with Intersect and TPG Rise Climate announced last year.

Google has contracted to add more than 6,200 megawatts (MW) of net new energy generation and capacity to the Texas electricity grid through power purchase agreements (PPAs) with energy developers such as AES Corporation, Enel North America, Intersect, Clearway, ENGIE, SB Energy, Ørsted, and X-Elio.

Water demands

Google’s three new facilities in Armstrong and Haskell counties will use air-cooling technology, limiting water use to site operations like kitchens. The company is also contributing $2.6 million to help Texas Water Trade create and enhance up to 1,000 acres of wetlands along the Trinity-San Jacinto Estuary. Google is also sponsoring a regenerative agriculture program with Indigo Ag in the Dallas-Fort Worth area and an irrigation efficiency project with N-Drip in the Texas High Plains.

In addition to the data centers, Google is committing $7 million in grants to support AI-related initiatives in healthcare, energy, and education across the state. This includes helping CareMessage enhance rural healthcare access; enabling the University of Texas at Austin and Texas Tech University to address energy challenges that will arise with AI, and expanding AI training for Texas educators and students through support to Houston City College.

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This article originally appeared on CultureMap.com.

TMCi names 11 global startups to latest HealthTech Accelerator cohort

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Texas Medical Center Innovation has named 11 medtech startups from around the world to its latest HealthTech Accelerator cohort.

Members of the accelerator's 19th cohort will participate in the six-month program, which kicked off this month. They range from startups developing on-the-go pelvic floor monitoring to 3D-printed craniofacial and orthopedic implants. Each previously participated in TMCi's bootcamp before being selected to join the accelerator. Through the HealthTech Accelerator, founders will work closely with TMC specialists, researchers, top-tier hospital experts and seasoned advisors to help grow their companies and hone their clinical trials, intellectual property, fundraising and more.

“This cohort of startups is tackling some of today’s most pressing clinical challenges, from surgery and respiratory care to diagnostics and women’s health," Tom Luby, chief innovation officer at Texas Medical Center, said in a news release. "At TMC, we bring together the minds behind innovation—entrepreneurs, technology leaders, and strategic partners—to help emerging companies validate, scale, and deliver solutions that make a real difference for patients here and around the world. We look forward to seeing their progress and global impact through the HealthTech Accelerator and the support of our broader ecosystem.”

The 2025 HealthTech Accelerator cohort includes:

  • Houston-based Respiree, which has created an all-in-one cardiopulmonary platform with wearable sensors for respiratory monitoring that uses AI to track breathing patterns and detect early signs of distress
  • College Station-based SageSpectra, which designs an innovative patch system for real-time, remote monitoring of temperature and StO2 for assessing vascular occlusion, infection, and other surgical flap complications
  • Austin-based Dynamic Light, which has developed a non-invasive imaging technology that enables surgeons to visualize blood flow in real-time without the need for traditional dyes
  • Bangkok, Thailand-based OsseoLabs, which develops AI-assisted, 3D-printed patient-specific implants for craniofacial and orthopedic surgeries
  • Sydney, Australia-based Roam Technologies, which has developed a portable oxygen therapy system (JUNO) that provides real-time oxygen delivery optimization for patients with chronic conditions
  • OptiLung, which develops 3D-printed extracorporeal blood oxygenation devices designed to optimize blood flow and reduce complications
  • Bengaluru, India-based Dozee, which has created a smart remote patient monitor platform that uses under-the-mattress bed sensors to capture vital signs through continuous monitoring
  • Montclair, New Jersey-based Endomedix, which has developed a biosurgical fast-acting absorbable hemostat designed to eliminate the risk of paralysis and reoperation due to device swelling
  • Williston, Vermont-based Xander Medical, which has designed a biomechanical innovation that addresses the complications and cost burdens associated with the current methods of removing stripped and broken surgical screws
  • Salt Lake City, Utah-based Freyya, which has developed an on-the-go pelvic floor monitoring and feedback device for people with pelvic floor dysfunction
  • The Netherlands-based Scinvivo, which has developed optical imaging catheters for bladder cancer diagnostics