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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Houston biotech VC firm's portfolio cos. score $5.3M in federal funding

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Three portfolio companies of Houston venture capital firm First Bight Ventures have received a combined $5.25 million from the U.S. Defense Department’s Distributed Bioindustrial Manufacturing Program.

“The allocation of funds by the federal government will be critical in helping grow biomanufacturing capacity,” Veronica Breckenridge (née Wu), founder of First Bight, says in a news release. “We are very proud to represent three dynamic companies that are awardees of this competitive and widely praised program.”

The three companies that were awarded Defense Department funds are:

  • Hayward, California-based Visolis, received $2.25 million to plan a facility for production of a chemical that can be transformed into rocket propellants, explosive binders, and sustainable aviation fuel.
  • Alameda, California-based Industrial Microbes received $1.55 million to plan a facility for converting ethanol feedstock into acrylic acid. This acid is a key component used in coatings, adhesives, sealants, lubricants, corrosion inhibitors, and wound dressings.
  • San Diego-based Algenesis received $1.5 million to plan and develop a facility that’ll produce diisocyanates, which are chemical building blocks used to make polyurethane products.

“This award is a testament to our commitment to advancing sustainable materials and will enable us to contribute to both national security and industrial resilience. Our planned facility represents a key step towards securing a domestic supply of critical components for polyurethanes,” says Stephen Mayfield, CEO of Algenesis.

Texas grocer H-E-B finally rolls out digital tap-to-pay services

hi, tech

Texas' favorite grocery store has some good news for shoppers who have a habit of forgetting their wallets. H-E-B is starting a phased rollout for digital tap-to-pay services, starting in San Antonio before spreading to the rest of the chain's stores.

The rollout began Monday, October 7. A release says it'll take "about a week" to spread to all stores in the region before making it ways across Texas. Although it is not known which stores will add the service on what date, the rollout includes all H-E-B stores, including Mi Tienda, H-E-B's Mexican grocery store that has locations in Houston.

With tap to pay, shoppers will finally be able to use smartphone-based systems such as Apple Pay, Samsung Pay, and Google Pay, as well as tapping a physical card.

Payments can be made with those apps, or "digital wallets," at cash registers and self-checkout lanes, as well as restaurants and pharmacies within H-E-B stores. They won't be accepted right away at H-E-B fuel pumps, but customers can use them to pay for gas if they bring their phones to the fuel station payment window.

This isn't exactly cutting-edge technology; Google Wallet launched in 2011, leading the market, and was followed by Apple Pay in 2014. But it's not ubiquitous either. In 2023, a poll by Forbes Advisor found that barely more than half of respondents used digital wallets more than traditional forms of payment.

H-E-B is on a bit of a payment revolutionizing kick, also launching a debit card in 2022 and a partnership in August of 2024 with the H-E-B-owned delivery service Favor for its fastest order fulfillment yet. Central Market and Joe V’s Smart Shop, two other H-E-B brands, also recently launched tap to pay.

“At H-E-B, we’re always exploring a broad range of technologies to enhance how customers shop and pay for products,” H-E-B vice president Ashwin Nathan said in a statement. “This has been one of the most requested services we have received from our customers and partners, and we are excited to now make this popular technology available at all our H-E-B locations.”

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This article originally ran on CultureMap.