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Rice researcher delves into the importance of trendspotting in consumer behavior

Keeping on track with trends is crucial to growing and developing a relationship with your customers, these Rice University researchers found. Getty Images

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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From software and IoT to decarbonization and nanotech, here's what 10 energy tech startups you should look out for. Photo via Getty Images

This week, energy startups pitched virtually for venture capitalists — as well as over 1,000 attendees — as a part of Rice Alliance for Technology and Entrepreneurship's 18th annual Energy and Clean Tech Venture Forum.

At the close of the three-day event, Rice Alliance announced its 10 most-promising energy tech companies. Here's which companies stood out from the rest.

W7energy

Based in Delaware, W7energy has created a zero-emission fuel cell electric vehicle technology supported by PiperION polymers. The startup's founders aim to provide a more reliable green energy that is 33 percent cheaper to make.

"With ion exchange polymer, we can achieve high ionic conductivity while maintaining mechanical strength," the company's website reads. "Because of the platform nature of the chemistry, the chemical and physical properties of the polymer membranes can be tuned to the desired application."

Modumetal

Modumetal, which has its HQ in Washington and an office locally as well, is a nanotechnology company focused on improving industrial materials. The company was founded in 2006 by Christina Lomasney and John Whitaker and developed a patented electrochemical process to produce nanolaminated metal alloys, according to Modumetal's website.

Tri-D Dynamics

San Francisco-based Tri-D Dynamics has developed a suite of smart metal products. The company's Bytepipe product claims to be the world's first smart casing that can collect key information — such as leak detection, temperatures, and diagnostic indicators — from underground and deliver it to workers.

SeekOps

A drone company based in Austin, SeekOps can quickly retrieve and deliver emissions data for its clients with its advance sensor technology. The company, founded in 2017, uses its drone and sensor pairing can help reduce emissions at a low cost.

Akselos

Switzerland-based Akselos has been using digital twin technology since its founding in 2012 to help energy companies analyze their optimization within their infrastructure.

Osperity

Osperity, based in Houston's Galleria area, is a software company that uses artificial intelligence to analyze and monitor industrial operations to translate the observations into strategic intelligence. The technology allows for cost-effective remote monitoring for its clients.

DroneDeploy

DroneDeploy — based in San Francisco and founded in 2013 — has raised over $92 million (according to Crunchbase) for its cloud-based drone mapping and analytics platform. According to the website, DroneDeploy has over 5,000 clients worldwide across oil and gas, construction, and other industries.

HEBI Robotics

Pittsburgh-based HEBI Robotics gives its clients the tools to build custom robotics. Founded 2014, HEBI has clients — such as NASA, Siemens, Ericsson — across industries.

CarbonFree Chemicals

CarbonFree Chemicals, based in San Antonio and founded in 2016, has created a technology to turn carbon emissions to useable solid carbonates.

SensorUp

Canadian Internet of Things company, SensorUp Inc. is a location intelligence platform founded in 2011. The technology specializes in real-time analysis of industrial operations.

"Whether you are working with legacy systems or new sensors, we provide an innovative platform that brings your IoT together for automated operations and processes," the company's website reads.

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