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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Venus Aerospace closes $91 million Series B to scale hypersonic engine

flight funding

Houston-based Venus Aerospace has closed a $91 million Series B round and plans to scale the production of its hypersonic engine.

The round was led by Houston-based Mercury Fund with participation from Lockheed Martin Ventures, MESH, PEAK6, Draper Associates, Starboard Star Venture Capital, Green Sands Equity and other investors, according to a news release.

The investment comes about a year after Venus completed the first U.S. flight test of its high-thrust rotating detonation rocket engine (RDRE). The engine is expected to enable vehicles to travel four to six times the speed of sound from a conventional runway and is about 15 percent more efficient than traditional alternatives, according to the company.

Venus Aerospace says the latest round of funding will allow it to move the RDRE from demonstration to deployment and meet customer requirements for the near-term defense and space industries. The company says that the reusable RDRE is designed with a "common propulsion architecture" that can work for multiple industries and mission types.

“This financing marks an important step in moving Venus from breakthrough demonstration to scaled capability,” Sassie Duggleby, co-founder and CEO, said in the news release. “Our customers need propulsion systems that go farther, can be produced reliably and are built on supply chains they can trust. We are advancing that capability with American engineering and manufacturing talent to strengthen U.S. defense, expand space access and support the future of high-speed flight.”

Venus Aerospace raised a $20 million Series A in 2022, led by Wyoming-based Prime Movers Lab. At the time, the company said it would put the funding toward three main technologies: a next-generation rocket engine, aircraft shape and leading-edge cooling system.

The company also picked up an investment from Lockheed Martin Ventures, the investment arm of aerospace and defense contractor Lockheed Martin, in November 2025—in addition to funding from other investors over the years.

“Since our initial investment, Venus has progressed very quickly in its technology development," Chris Moran, vice president and general manager of Lockheed Martin Ventures, added in the release. "Our reinvestment in Venus recognizes Venus’ accomplishments to date and focus on speed to manufacture, cost management and reduction of supply chain constraints. Venus is working effectively to position its propulsion system for the production scale required by defense programs.”

"Venus is exactly the kind of company Houston capital should be backing," Blair Garrou, co-founder and managing partner at Mercury Fund, added in the release. "It combines multiple frontier technologies, domestic manufacturing and clear commercial and national security relevance. We believe this team is positioned to lead an important new chapter in defense and space, and we are proud to support a company building breakthrough technology here in Texas."

Venus Aerospace and Houston clean tech startup Vaulted Deep were named to the World Economic Forum's Technology Pioneers community earlier this summer. Read more here.

Intuitive Machines lands $148M as part of NASA Moon Base funding

to the moon

Houston-based Intuitive Machines has been awarded $148.3 million to deliver its Nova-C lander to the moon by 2028. The funding is part of $600 million that NASA recently awarded to three companies as part of the agency’s Moon Base Program.

The contracts aim to support sustained human presence and commercial operations on the Moon. Austin-based Firefly Aerospace was awarded $144.2 million by NASA for one mission and Pittsburgh-based Astrobotic netted $297.9 million for two lunar landings. Intuitive Machine's award is the company's sixth task order under NASA's Commercial Lunar Payload Services (CLPS) program.

“We’re building a proving ground for Moon Base operations,” Ryan Stephan, NASA’s Moon Base acting director of cargo landers, said in a news release. “Accelerating our Moon mission ordering cadence and launch opportunities enable us to move quickly to learn, iterate, and improve.”

Under the latest task order, Intuitie Machines will deliver three scientific and operational payloads to the moon, which include a:

  • Linear Energy Transfer Spectrometer (LETS) radiation monitor to gather critical environmental safety data
  • Advanced stereo cameras to analyze surface-plume interactions (SCALPSS)
  • Laser retroreflector array (LRA) for precise cislunar positioning

The funding breakdown includes a $68.6 million base contract and a $79.7 million performance incentive for Intuitive Machines.

The company says the funding will allow it to create a standardized and repeatable "lunar utility pipeline" for delivering cargo to the moon.

"We are shifting the paradigm from custom aerospace engineering to commercial mass production of lunar infrastructure," Steve Altemus, CEO of Intuitive Machines, said in a separate news release. "Our flight-proven Nova-C platform allows us to build, test, and deploy multiple landers in parallel using Industry 4.0-powered manufacturing. This contract directly advances our core mission to provide persistent, reliable, and commercial baseline of transport, connectivity, and operations that allows our customers to stay longer and achieve more on the Moon."

NASA also shared that it is exploring plans to send PROMISE, a rover based on the Mars Perseverance and Curiosity rovers, to the moon and it plans to seek proposals for additional lunar lander missions, technology demonstrations, a communications and navigation satellite network, and new science payloads to support its lunar outpost. NASA is developing its Moon Base near the lunar South Pole. The agency expects it to come to fruition sometime after 2032.

Intuitive Machines had received its last CLPS award for $180.4 million in March 2026. It will be the first mission to utilize the company's larger cargo lunar lander, Nova-D. The company was also recently awarded a $1 million grant from Maryland Gov. Wes Moore to expand its robotics operations in the state.

UT team develops wearable technology for atmospheric water harvesting

In The Air

Engineers at the University of Texas at Austin have developed a prototype jacket that harvests clean drinking water directly from the atmosphere, and it works even in the driest desert conditions.

The research, published in Science Advances, marks the latest milestone in nearly a decade of work by materials scientist and chair professor Guihua Yu and his team at the Cockrell School of Engineering's Walker Department of Mechanical Engineering and Texas Materials Institute. The wearable technology marks a significant leap: instead of a bulky, stationary machine, this jacket does the work.

Photo courtesy of UT Austin

"We have been working on atmospheric water harvesting technology for a number of years," Yu says. "This current version is even more wearable. We're transitioning from conventional, more stationary water harvesting to something truly portable and personal."

Yu's lab first published work on hydrogel-based water harvesting around 2019, and the jacket is the latest evolution of that platform, now called AirGel. Last year, the broader AirGel invention won the top prize in the graduate category of the National Collegiate Inventors Competition.

The jacket is woven with specially engineered hydrogel fibers; ultra-porous materials that attract and absorb moisture from the surrounding air much like a household desiccant. Unlike a desiccant, the material doesn't require intense heat to release that water. The hydrogel is thermally responsive, meaning a modest rise in temperature — even from mild solar heating — is enough to release the water it has captured.

Condenser test in AustinSo, somebody would be wearing the jacket, or perhaps carrying this gel-like textile as a blanket, as it passively absorbs moisture from the air. Then they would detach the textile panels and place them into a small, portable collector unit; essentially a compact heater. The water evaporates out of the textile, condenses inside the collector, and drips out as clean, drinkable water.

"It immediately becomes drinkable because it already goes through the distillation process," Yu explains.

In trials, the jacket produced between 400 and 900 milliliters of water per day depending on humidity, or roughly 14-30 ounces, nearly a quart, depending on the air's humidity. With one kilogram of the textile, the researchers found they could generate approximately 3.7-4 liters of water in arid conditions, and potentially double that in humid ones. So far, the team has tried the jacket out in very dry, semi-dry, and humid areas, and the jacket was able to pull water from each climate.

Lead researcher Chuxin Lei, a postdoctoral researcher on Yu's team and co-author on the paper, says the goal was to rethink who this technology could serve.

Portable bag contents

"Many current [atmospheric water harvesting] systems are still built as rigid or stationary platforms, making them less suitable for people who are moving, working outdoors, or operating in some remote environment. This lead us to ask whether we could build a water harvesting system that could become more like clothing — light, wearable, flexible, and naturally suited for personal use," Lei says.

The potential applications are wide-ranging. Yu's team has previously worked with the Department of Defense on water solutions for soldiers, where water logistics can be dangerous and costly. The technology could also serve hikers, emergency responders, disaster relief workers, and agricultural and field workers. Anyone who needs clean water on the go and far from infrastructure.

The team also sees a potential future where the technology complements large-scale centralized water systems rather than replacing them.

"Our solution cannot be a universal solution for all," Yu acknowledges. "But I think it's an extremely important alternative."

For now, the jacket is still a laboratory prototype, but Yu and Lei are optimistic. With the right industry partnerships, they say, the technology could realistically reach commercial scale within three to five years.

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This article originally appeared on CultureMap.com, written by Natalie Grigson.