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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This Houston suburb named one of 10 newest boomtowns in U.S.

Booming 'Burb

What do you get when you combine a city's surge in population, housing growth, and economy? For the Houston suburb of Conroe, it adds up to being America's No. 9 newest boomtown, according to a new survey from SmartAsset.

The personal finance website's just-released report analyzed more than 400 U.S. cities with populations of 65,000 or more to identify places experiencing rapid growth based on five-year changes in economic output, housing units, and labor force size.

Texas is home to the second-highest concentration of new boomtowns in America with 18 out of 75 located in the Lone Star State. Only Florida ranks higher than Texas by just one.

However, Texas nearly locked out the top five most bustling boomtowns in America. Austin suburb Georgetown topped the list, and its Central Texas neighbors New Braunfels (No. 2) and Leander (No. 4) ranked close behind. Dallas-Fort Worth mid-city Lewisville claimed the No. 5 spot. Lehi, Utah ranked in third place.

Conroe has soared in popularity as one of America's most sought-after suburbs over the last several years, boosted by its renter-friendliness and its livability among the millennial generation.

Conroe has seen a 37 percent increase in housing units from 2019 to 2024, with its labor force growing by 33 percent during that time. SmartAsset also determined that Montgomery County's economic output grew at compound annual rates of 4.9 percent.

The report says population booms and "expanding business activity" can create "visible momentum" for an up-and-coming city, but these fast changes can alter a city in ways residents may not expect.

"In recent years, some American cities stand out for attracting people, investment and development at a pace that sets them apart," the report said. "Boomtown status does not mean growth benefits everyone equally, but it does reflect a city’s expanding economic capacity and the new opportunities that come with it."

America's top 10 new boomtowns are:

  • No. 1 – Georgetown
  • No. 2 – New Braunfels
  • No. 3 – Lehi, Utah
  • No. 4 – Leander
  • No. 5 – Lewisville
  • No. 6 – Palm Coast, Florida
  • No. 7 – Nampa, Idaho
  • No. 8 – McKinney
  • No. 9 – Conroe
  • No. 10 – Frisco
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This article originally appeared on CultureMap.com.

Houston brain health co. secures $6.5M for rare disease study

neuro funding

Houston-based Goldenrod Therapeutics, part of Fannin Partners' portfolio, has announced the initial close of a $6.5 million series seed preferred stock round.

The round was led by Ataxia Ventures and an affiliate of Fannin, according to a news release.

Goldenrod Therapeutics plans to use the funding to support manufacturing, formulation optimization, IND-enabling studies and a Phase I study of its drug to treat brain inflammation, known as 11h.

The study will consider how 11h, which blocks the enzyme PDE4, could treat Friedreich’s ataxia (FA), a rare genetic disease that affects movement, speech and balance. To date, other PDE4 inhibitors have proven to regulate neuroinflammation and neuronal signaling, but have had adverse gastrointestinal side effects or have not reached enough of the central nervous system, according to Goldenrod.

The company says its 11h is expected to have "broad applicability" with limited emetric side effects.

“Our 11h program is a next-generation, orally bioavailable, brain-penetrant PDE4 inhibitor, where researchers overcame longstanding limitations associated with earlier PDE4 inhibitors," Dr. Dev Chatterjee, CEO of Goldenrod, said in the news release. "We believe this creates the potential for a best-in-class therapy for Friedreich’s Ataxia and a potential foundation for development across multiple neurodegenerative and neuroinflammatory disorders.”

11h was first developed at the University of Nebraska Medical Center (UNeMed). Houston-based Fannin Partners in-licensed the product 2020 and landed SBIR Phase I funding to support its initial development for opioid use disorder soon after.

Goldenrod has also received funding to study 11h's effectiveness for multiple sclerosis, methamphetamine addiction and cocaine addiction.

Goldenrod says it is developing 11h to target a variety of neurological and inflammatory conditions, including Alzheimer's disease, multiple sclerosis, ALS, substance use disorders, Batten disease, pain and traumatic brain injury.