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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Intuitive Machines forms partnership with Italian companies for lunar exploration services

to the moon

Houston-based space technology, infrastructure and services company Intuitive Machines has forged a partnership with two Italian companies to offer infrastructure, communication and navigation services for exploration of the moon.

Intuitive Machines’ agreement with the two companies, Leonardo and Telespazio, paves the way for collaboration on satellite services for NASA, a customer of Intuitive Machines, and the European Space Agency, a customer of Leonardo and Telespazio. Leonardo, an aerospace, defense and security company, is the majority owner of Telespazio, a provider of satellite technology and services.

“Resilient, secure, and scalable space infrastructure and space data networks are vital to customers who want to push farther on the lunar surface and beyond to Mars,” Steve Altemus, co-founder and CEO of Intuitive Machine, said in a news release.

Massimo Claudio Comparini, managing director of Leonardo’s space division, added that the partnership with Intuitive Machines is a big step toward enabling human and robotic missions from the U.S., Europe and other places “to access a robust communications network and high-precision navigation services while operating in the lunar environment.”

Intuitive Machines recently expanded its Houston Spaceport facilities to ramp up in-house production of satellites. The company’s first satellite will launch with its upcoming IM‑3 lunar mission.

Intuitive Machines says it ultimately wants to establish a “center of space excellence” at Houston Spaceport to support missions to the moon, Mars and the region between Earth and the moon.

Houston hospitals win $50M grant for ibogaine addiction treatment research

ibogaine funding

The Texas Health and Human Services Commission has awarded $50 million to UTHealth Houston in collaboration with The University of Texas Medical Branch at Galveston (UTMB Health) to co-lead a multicenter research trial to evaluate the effect of ibogaine, a powerful psychoactive compound, on patients suffering from addiction, traumatic brain injury and other behavioral health conditions.

The funding will establish a two-year initiative—known as Ibogaine Medicine for PTSD, Addiction, and Cognitive Trauma (IMPACT)—and a consortium of Texas health institutions focused on clinical trials and working toward potential FDA-approved treatments.

The consoritum will also include Texas Tech University, Texas Tech University Health Sciences Center El Paso, The University of Texas at Austin, The University of Texas Health Science Center at San Antonio, The University of Texas at Tyler, The University of Texas Rio Grande Valley, Texas A&M University, The University of North Texas Health Science Center, Baylor College of Medicine and JPS Health Network in Dallas.

Ibogaine is a plant-based, psychoactive substance derived from the iboga shrub. Research suggests that the substance could be used for potential treatment for patients with traumatic brain injuries, which is a leading cause of post-traumatic stress disorders. Ibogaine has also shown potential as a treatment for addiction and other neurological conditions.

UTHealth and partners will focus on ways that ibogaine can treat addiction and associated conditions. Meanwhile, UT Austin and Baylor College of Medicine will concentrate on using it to treat traumatic brain injury, especially in veterans, according to a news release from the institutions.

The consortium will also support drug developers and teaching hospitals to conduct FDA-approved clinical trials. The Texas Health and Human Services Commission will oversee the grant program.

“This landmark clinical trial reflects our unwavering commitment to advancing research that improves lives and delivers the highest standards of care,” Dr. Melina Kibbe, UTHealth Houston president and the Alkek-Williams Distinguished Chair, said in the news release. “By joining forces with outstanding partners across our state, we are building on Texas’ tradition of innovation to ensure patients struggling with addiction and behavioral health conditions have access to the best possible outcomes. Together, we are shaping discoveries that will serve Texans and set a model for the nation.”

The consortium was authorized by the passage of Senate Bill 2308. The bill provides $50 million in state-matching funds for an ibogaine clinical trial managed by a public university in partnership with a drug company and a hospital.

“This is the first major step towards the legislature’s goal of obtaining FDA approval through clinical trials of ibogaine — a potential breakthrough medication that has brought thousands of America’s war-fighters back from the darkest parts of depression, anxiety, PTSD, and chronic addiction,” Texas Rep. Cody Harris added in the release. “I am excited to walk alongside UTHealth Houston and UTMB as these stellar institutions lead the nation in a first-of-its-kind clinical trial in the U.S.”

Recently, the University of Houston also received a $2.6 million gift from the estate of Dr. William A. Gibson to support and expand its opioid addiction research, which includes the development of a fentanyl vaccine that could block the drug's ability to enter the brain. Read more here.

Tesla no longer world's biggest EV maker as sales fall for second year

Tesla Talk

Tesla lost its crown as the world’s bestselling electric vehicle maker as a customer revolt over Elon Musk’s right-wing politics, expiring U.S. tax breaks for buyers and stiff overseas competition pushed sales down for a second year in a row.

Tesla said that it delivered 1.64 million vehicles in 2025, down 9% from a year earlier.

Chinese rival BYD, which sold 2.26 million vehicles last year, is now the biggest EV maker.

It's a stunning reversal for a car company whose rise once seemed unstoppable as it overtook traditional automakers with far more resources and helped make Musk the world's richest man. The sales drop came despite President Donald Trump's marketing effort early last year when he called a press conference to praise Musk as a “patriot” in front of Teslas lined up on the White House driveway, then announced he would be buying one, bucking presidential precedent to not endorse private company products.

For the fourth quarter, Tesla sales totaled 418,227, falling short of even the much reduced 440,000 target that analysts recently polled by FactSet had expected. Sales were hit hard by the expiration of a $7,500 tax credit for electric vehicle purchases that was phased out by the Trump administration at the end of September.

Tesla stock fell 2.6% to $438.07 on Friday.

Even with multiple issues buffeting the company, investors are betting that Tesla CEO Musk can deliver on his ambitions to make Tesla a leader in robotaxi services and get consumers to embrace humanoid robots that can perform basic tasks in homes and offices. Reflecting that optimism, the stock finished 2025 with a gain of approximately 11%.

The latest quarter was the first with sales of stripped-down versions of the Model Y and Model 3 that Musk unveiled in early October as part of an effort to revive sales. The new Model Y costs just under $40,000 while customers can buy the cheaper Model 3 for under $37,000. Those versions are expected to help Tesla compete with Chinese models in Europe and Asia.

For fourth-quarter earnings coming out in late January, analysts are expecting the company to post a 3% drop in sales and a nearly 40% drop in earnings per share, according to FactSet. Analysts expect the downward trend in sales and profits to eventually reverse itself as 2026 rolls along.

Musk said earlier last year that a “major rebound” in sales was underway, but investors were unruffled when that didn't pan out, choosing instead to focus on Musk's pivot to different parts of business. He has has been saying the future of the company lies with its driverless robotaxis service, its energy storage business and building robots for the home and factory — and much less with car sales.

Tesla started rolling out its robotaxi service in Austin in June, first with safety monitors in the cars to take over in case of trouble, then testing without them. The company hopes to roll out the service in several cities this year.

To do that successfully, it needs to take on rival Waymo, which has been operating autonomous taxis for years and has far more customers. It also will also have to contend with regulatory challenges. The company is under several federal safety investigations and other probes. In California, Tesla is at risk of temporarily losing its license to sell cars in the state after a judge there ruled it had misled customers about their safety.

“Regulatory is going to be a big issue,” said Wedbush Securities analyst Dan Ives, a well-known bull on the stock. “We're dealing with people's lives.”

Still, Ives said he expects Tesla's autonomous offerings will soon overcome any setbacks.

Musk has said he hopes software updates to his cars will enable hundreds of thousands of Tesla vehicles to operate autonomously with zero human intervention by the end of this year. The company is also planning to begin production of its AI-powered Cybercab with no steering wheel or pedals in 2026.

To keep Musk focused on the company, Tesla’s directors awarded Musk a potentially enormous new pay package that shareholders backed at the annual meeting in November.

Musk scored another huge windfall two weeks ago when the Delaware Supreme Court reversed a decision that deprived him of a $55 billion pay package that Tesla doled out in 2018.

Musk could become the world's first trillionaire later this year when he sells shares of his rocket company SpaceX to the public for the first time in what analysts expect would be a blockbuster initial public offering.