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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Rice research breakthrough paves the way for advanced disease therapies

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Bioengineers at Rice University have developed a “new construction kit” for building custom sense-and-respond circuits in human cells, representing a major breakthrough in the field of synthetic biology, which could "revolutionize" autoimmune disease and cancer therapeutics.

In a study published in the journal Science, the team focused on phosphorylation, a cellular process in the body in which a phosphate group is added to a protein, signaling a response. In multicellular organisms, phosphorylation-based signaling can involve a multistage, or a cascading-like effect. Rice’s team set out to show that each cycle in a cascade can be treated as an elementary unit, meaning that they can be reassembled in new configurations to form entirely novel pathways linking cellular inputs and outputs.

Previous research on using phosphorylation-based signaling for therapeutic purposes has focused on re-engineering pathways.

“This opens up the signaling circuit design space dramatically,” Caleb Bashor, assistant professor of bioengineering and biosciences and corresponding author on the study, said in a news release. “It turns out, phosphorylation cycles are not just interconnected but interconnectable … Our design strategy enabled us to engineer synthetic phosphorylation circuits that are not only highly tunable but that can also function in parallel with cells’ own processes without impacting their viability or growth rate.”

Bashor is the deputy director for the Rice Synthetic Biology Institute, which launched last year.

The Rice lab's sense-and-respond cellular circuit design is also innovative because phosphorylation occurs rapidly. Thus, the new circuits could potentially be programmed to respond to physiological events in minutes, compared to other methods, which take hours to activate.

Rice’s team successfully tested the circuits for sensitivity and their ability to respond to external signals, such as inflammatory issues. The researchers then used the framework to engineer a cellular circuit that can detect certain factors, control autoimmune flare-ups and reduce immunotherapy-associated toxicity.

“This work brings us a whole lot closer to being able to build ‘smart cells’ that can detect signs of disease and immediately release customizable treatments in response,” Xiaoyu Yang, a graduate student in the Systems, Synthetic and Physical Biology Ph.D. program at Rice who is the lead author on the study, said in a news release.

Ajo-Franklin, a professor of biosciences, bioengineering, chemical and biomolecular engineering and a Cancer Prevention and Research Institute of Texas Scholar, added “the Bashor lab’s work vaults us forward to a new frontier — controlling mammalian cells’ immediate response to change.”

Greentown Labs names new CEO to lead pioneering climate tech incubator

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Houston and Boston climate tech incubator Greentown Labs has named Georgina Campbell Flatter as the organization’s incoming CEO.

Flatter will transition to Greentown from her role as co-founder and executive director of TomorrowNow.org, a global nonprofit that studies and connects next-generation weather and climate technologies with communities most affected by climate change.

“We are at a transformational moment in the energy transition, with an unprecedented opportunity to drive solutions in energy production, sustainability, and climate resilience,” Flatter said in a news release. “Greentown Labs is, and has always been, a home for entrepreneurs and a powerhouse of collaboration and innovation.”

Previously, Flatter worked to launch TomorrowNow out of tomorrow.io, a Boston-based AI-powered weather intelligence and satellite technology company. The organization secured millions in climate philanthropy from partners, including the Gates Foundation, which helped deliver cutting-edge climate solutions to millions of African farmers weekly.

Flatter also spent 10 years at the Massachusetts Institute of Technology (MIT), where she was a senior lecturer and led global initiatives at the intersection of technology and social impact. Her research work includes time at Langer Lab and Sun Catalytix, an MIT – ARPA-E-funded spin-out that focused on energy storage solutions inspired by natural photosynthesis. Flatter is also an Acumen Rockefeller Global Food Systems Fellow and was closely involved with Greentown Labs when it was founded in Boston in 2011, according to the release.

“It’s rare to find an individual who has impressive climate and energy expertise along with nonprofit and entrepreneurial leadership—we’re fortunate Georgie brings all of this and more to Greentown Labs,” Bobby Tudor, Greentown Labs Board Chair and Chairman of the Houston Energy Transition Initiative, said in a news release.

Flatter will collaborate with Kevin Dutt, Greentown’s Interim CEO, and also continue to serve on Greentown’s Board of Directors, which was recently announced in December and contributed to a successful $4 million funding round. She’s also slated to speak at CERAWeek next month.

“In this next chapter, I’m excited to build on our entrepreneurial roots and the strength of our ever-growing communities in Boston and Houston,” Flatter added in a news release. “Together, we will unite entrepreneurs, partners, and resources to tackle frontier challenges and scale breakthrough technologies.”

Greentown also named Naheed Malik its new chief financial officer last month. The announcements come after Greentown’s former CEO and president, Kevin Knobloch, announced that he would step down in July 2024 after less than a year in the role.

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This article originally appeared on our sister site, EnergyCapital.

Houston firm invests $150M in leading 'lab on a chip' medical diagnostics co.

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Houston-based health technology investment firm Hamershlag Private Capital Management Limited (HPCM) announced a $150.15 million venture investment in Patho Care LLC.

Patho Care is a “lab on a chip” medical diagnostics company known for its noninvasive point-of-care testing platforms, such as its Raman spectroscopy-based platform.

Its digital point-of-care testing devices are programmable, mobile, and reusable and can detect current or future respiratory bacterial or viral infections. The company says the technology is more cost-effective and provides results faster than traditional diagnostic methods.

“Patho Care LLC is a distinguished leader in healthcare diagnostics through the utilization of a novel approach with spectroscopy and this investment aligns with HPCM’s strategy of partnering with high-potential companies in dynamic industries,” L. Mychal Jefferson, Chairman of Hamershlag, said in a news release.

The transaction was structured as an acquisition and recapitalization using newly issued common stock and cash, which will work through a newly formed entity, PathoCare Holdings Inc. The deal will also facilitate the repayment of Patho Care LLC's existing financial obligations and settle Patho Care’s outstanding notes, helping ensure the company’s financial readiness, according to the release.

The investment will help Patho Care LLC improve operational efficiencies, broaden its service offerings and continue to innovate in the diagnostic testing space. The companies hope the collaboration will help “unlock new growth opportunities while maintaining the company’s legacy of excellence in an emerging technology,” according to a news release.

“Our commitment to delivering transformative value through innovative investments underscores our confidence in Patho Care’s vision and capabilities,” Jefferson added.