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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.

Slightly-to-moderately overqualified workers are more likely to be valuable and to reimagine their duties in ways that advance their institutions. Getty Images

You're a rocket scientist. You've worked for NASA. You won a Nobel Prize. Shouldn't your qualifications give you an edge on a software developer job?

According to typical hiring practice, the answer is no. You might not even get an interview for a job sweeping the floor. That's because, for years, research has warned that hiring applicants with too much experience or too many skills will saddle you with employees who don't appreciate their jobs.

Now there's good news for rocket scientists and others who happen to be overqualified for their work. According to a groundbreaking new study coauthored by Rice Business professor Jing Zhou, workers who are slightly to moderately overqualified are actually more likely to be active and creative contributors to their workplace. As a result, they're more likely to be assets. The study adds to a new body of research about the advantages of an overqualified workforce.

Zhou's findings have widespread implications. Worldwide, almost half of the people who work for a living report that they are overqualified for their jobs. That means Zhou's research, conducted with Bilian Lin and Kenneth Law of the Chinese University of Hong Kong, applies to a vast segment of the labor market.

To reach its conclusions, Zhou's team launched two separate studies in China. The first looked at six different schools with a total of 327 teachers and 85 supervisors. The second analyzed an electronic equipment factory with 297 technicians. Both studies revealed a strong link between perceived slight and moderate overqualification and the frequency of "task crafting," that is, expanding the parameters of the work in more innovative and productive ways.

In the school study, teachers who were slightly to moderately overqualified set up new online networks with students and parents. They also rearranged classrooms in ways that made students more engaged and productive. Meanwhile, in the factory, workers took tests to gauge their abilities in complex tasks designing a ship. The ones who were slightly to moderately overqualified built more complex versions that reflected their superior competencies.

The key to both sets of workers' superiority was their impulse to "job craft." Every worker leaves a personal imprint: meeting the bare minimum of criteria, pushing to exceed expectations, innovating or imagining new or more useful ways of getting the job done. Expert "job crafters" turn this impulse into an art. Some redraw their task boundaries or change the number of tasks they take on. Others reconfigure their work materials or redefine their jobs altogether. Still others rearrange their work spaces and reimagine their work procedures in ways that can catapult their productivity upward.

For overqualified workers, Zhou's team found, task crafting is a psychological coping mechanism – a welcome one. Workers want to show their superiors the true level of their skills. Doing so fortifies their self-esteem and intensifies their bonds with the company they work for. Far from being dissatisfied, these overqualified workers are more productive, keen to help their organizations and interested in finding ways to be proud of their work.

So how did the outlook on such workers go from shadowy to brilliant? Past research, it turns out, focused rigidly on the fit between worker experience and a task. It didn't consider the nuanced human motivations that go into working, nor the full range of creativity or flexibility possible in getting a job done.

Thus, older studies cautioned that overqualified workers are likely to feel deprived and resentful. Zhou's research shows the opposite: a statistical correlation between worker overqualification and high job performance.

Organizations do need to do their part for this alchemy to work. Above all, Zhou writes, it's crucial to build a strong bond between worker and institution. This is because workers who identify strongly with their workplace feel more confident that their job-crafting efforts will be well received; those who don't feel this strong bond often feel mistreated and give up the project of crafting their work.

Similarly, companies also need to grant workers flexibility to expand the scope or improve the process of their jobs. The outcome can be the evolution of the entire business in unexpected and often creative ways.

Not all super-qualified workers will be inspired to re-craft their tasks. When the gulf between skills and task is extreme, Zhou writes, workers are bored and job crafting loses its juice as an incentive. For more moderately overqualified employees, however, their expertise should rocket their CVs to the top of the stack. For seasoned workers, the evidence shows, a job is not just a job. It's an adventure in finding ways to be excellent.

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This story originally ran on Rice Business Wisdom.

Jing Zhou is the Mary Gibbs Jones Professor of Management and Psychology in Organizational Behavior at the Jones Graduate School of Business of Rice University.