Based on research by Wagner Kamakura (Rice Business) and Rex Y. Du (Houston)

Key takeaways:

• Spotting trends is indispensable for marketers trying to discern shifts in consumer tastes and behaviors.
• Trendspotting can take two approaches: qualitative, which analyzes large shifts in consumer wants, or quantitative, which tracks multiple indicators such as keyword searches or tweets.
• Researchers who use quantitative methods can identify structural shifts in longitudinal data and distill seasonal from non-seasonal or dynamic trends.

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 indicators from multiple sources, chosen systematically so you don’t miss key signals or overweight irrelevant ones.
• Second: Reduce the raw data to a few common factors, filtering out noise and errors.
• Third: Identify the underlying trends and what’s driving spikes or dips, separating cyclical effects from true shifts.
• Fourth: Validate the trends by comparing them with past patterns and other relevant variables.
• Fifth: Use historical data to project the trend lines forward, so managers can respond early to risks and opportunities.

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.

Kamakura and Du (2012). “Quantitative Trendspotting,” Journal of Marketing Research.