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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.

What's the latest in tech research in Houston? Here are three revolutionary research projects happening right under our noses. Getty Images

Tons of research happens daily at various Houston institutions — from life-saving medical developments to high tech innovations that will affect the greater business community.

In this Houston research roundup, three research projects from three Houston organizations are set to revolutionize their respective industries.

University of Houston researcher explores potential disruption in blockchain

blockchain

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A huge technology question mark within business has been blockchain — how it'll affect the sharing of information and industry as a whole. But, one University of Houston professor and his Texas A&M University colleagues are looking into that potential disruption in a recent paper.

"It's an emerging technology. It's evolving," says Weidong "Larry" Shi, associate professor of computer science at UH, in a UH news release.

Funded by the Borders, Trade, and Immigration Institute, the research has developed into the paper, which was published in the International Journal of Production Research.

A key focus of the research is how blockchain will affect cargo entering the United States, and identifies six pain points within adapting blockchain for cargo management: traceability, dispute resolution, cargo integrity and security, supply chain digitalization, compliance, and trust and stakeholder management, according to the release.

"The wide adoption of blockchain technology in the global SC (supply chain) market is still in its infancy," the article reads. "Industry experts project that on average, it may take about six years for the widespread adoption of blockchain."

Blockchain has the potential to prevent fraud within the global supply chain, among other things.

"The data can't be changed. Everyone (along the supply chain) has a copy. You can add information, but you can't change it," Shi says in the release.

The U.S. Army taps Rice University for network research

Photo by Jeff Fitlow/Rice University

Rice University and the U.S. Army have joined forces for a five-year, $30 million research agreement to modernize the Army — specifically for developing next-generation wireless networks and radio frequency (RF) electronics.

"[The Army Research Laboratory] and Rice will match the right people and capabilities to meet specific challenges, and the cooperative agreement is structured to allow the Army to partner widely across our campus," says Yousif Shamoo, Rice's vice president of research and lead on the ARL partnership, in a recent news release. "One exciting aspect of this partnership is the broader societal benefits. The technologies we're starting with are needed for Army modernization and they could also benefit millions of Americans in communities that still lack high-speed internet."

Without going into too much detail, the two entities are working to advance the Army's existing infrastructure to create networks that can sense attacks and protect themselves by adaption or stealth. The technology has the potential to affect the Army as well as civilians, says Heidi Maupin, the lead ARL contact for the Rice partnership.

"We want to deliver the capability of quickly deploying secure, robust Army communications networks wherever and whenever they're needed," Maupin says in the release. "The technology needed for that will benefit the world by transforming the economics of rural broadband, reducing response times to natural disasters, opening new opportunities for online education and more."

Research out of Baylor College of Medicine advancing information known about vision

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For humans, seeing is pretty simple — just open your eyes. But the process our eyes go through extremely complex, and scientists have had a hard time recreating the process — until now.

Researchers at Baylor College of Medicine in Houston and the University of Tübingen in Germany have developed a novel computational approach that accelerates the brain's ability to identify optimal stimuli. The complete study by the scientists was published in the journal Nature Neuroscience.

"We want to understand how vision works," says senior author Dr. Andreas Tolias, professor and Brown Foundation Endowed Chair of Neuroscience at Baylor. "We approached this study by developing an artificial neural network that predicts the neural activity produced when an animal looks at images. If we can build such an avatar of the visual system, we can perform essentially unlimited experiments on it. Then we can go back and test in real brains with a method we named 'inception loops."

To track neurons and how they work, the researchers tracked brain activity scanning thousands of images.

"Experimenting with these networks revealed some aspects of vision we didn't expect," says Tolias, founder and director of the Center for Neuroscience and Artificial Intelligence at Baylor, in a release. "For instance, we found that the optimal stimulus for some neurons in the early stages of processing in the neocortex were checkerboards, or sharp corners as opposed to simple edges which is what we would have expected according to the current dogma in the field."

The research is ongoing and will only continue to help dissect how the brain sees and interprets visual elements.