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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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Karl Ecklund, left, and Paul Padley of Rice University have received a $1.3 million grant from the Department of Energy to continue physics research on the universe. Photo by Jeff Fitlow/Rice University

Two Rice University physicists and professors have received a federal grant to continue research on dark matter in the universe.

Paul Padley and Karl Ecklund, professors of physics and astronomy at Rice, have received a $1.3 million grant from the Department of Energy for their research to continue the university's ongoing research at the Large Hadron Collider, or LHC, a particle accelerator consisting of a 17-mile ring of superconducting magnets buried beneath Switzerland and France.

"With this grant we will be able to continue our investigations into the nature of the matter that comprises the universe, what the dark matter that permeates the universe is, and if there is physics beyond what we already know," Padley says in a press release.

This grant is a part of the DOE's $132 million in funding for high-energy physics research. The LHC has received a total of $4.5 million to date to continue this research. Most recently, Ecklund and Padley received a $3 million National Science Foundation grant to go toward updates to the LHC.

"High-energy physics research improves our understanding of the universe and is an essential element for maintaining America's leadership in science," says Paul Dabbar, undersecretary for science at the DOE, in the release. "These projects at 53 different institutions across our nation will advance efforts both in theory and through experiments that explore the subatomic world and study the cosmos. They will also support American scientists serving key roles in important international collaborations at institutions across our nation."

In 2012, Padley and his team discovered the Higgs boson, a feat that was extremely key to the continuance of exploring the Standard Model of particle physics. Since then, the physicists have been working hard to answer the many questions involved in studying physics and the universe.

"Over many decades, the particle physics group at Rice has been making fundamental contributions to our understanding of the basic building blocks of the universe," Padley says in the release. "With this grant we will be able to continue this long tradition of important work."

Paul Padley and his team as made important dark matter findings at the Large Hadron Collider in Europe. Photo via rice.ed

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