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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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Welcome to Houston, Lalamove. Photo by @HoustonTips

Holiday shopping is in full swing, and the bane of everyone's existence — especially during a pandemic — is shipping.

For smaller and mid-sized local businesses, that means paying big-business prices to a national shipping company. And for consumers, it's waiting a week or more to receive your item, even if you paid for shipping.

Lalamove has a solution for both parties. The 24/7 on-demand delivery app recently launched in Houston and offers affordable, same-day delivery services for the local merchants we're all trying to support right now.

"Amidst COVID-19, it is more important than ever to shop local and support our small businesses," says Lalamove's international managing director, Blake Larson. "We look forward to providing our services to Houston businesses in need of a fruitful start to the holiday season."

Unlike other delivery options, Lalamove delivers everything from food to small packages to bulky furniture within the same day, and it operates on a base-plus-miles pricing model with no commissions.

Deliveries in a sedan start at $8.90, with $1 per additional mile. SUV pricing has a base fare of $16.90 plus $1.25 per mile. Other same-day delivery options with national shipping companies can be well over $100 dollars, depending on the size and weight of the package.

Neighborhood-to-neighborhood sedan pricing is more affordable than traditional same-day shipping: Museum District to Midtown is $9.90, Midtown to The Heights is $14.90, and Northside to East Downtown is $17.90.

This also contrasts with food delivery platforms that charge restaurants 15-30 percent commission on the entire order; with Lalamove, the delivery charge for a $25 meal is the same as a $150 meal.

Users and businesses can place an order via the Lalamove app or on its website, which is available 24/7. When placing your order, you are instantly matched with a driver and their car, based on your delivery needs. You can deliver to (or order from) up to 20 locations in one order with the multi-stop delivery feature, and can schedule a delivery in advance or book for right then.

Lalamove app Using Lalamove is simple. Graphic courtesy of Lalamove

Shoppers can request Lalamove's services with local boutiques and stores that don't normally offer delivery, and get instant gratification (and a much smoother holiday season) with same-day delivery.

Both sides can rest easy knowing that things will arrive in time for the holidays in a trusted, secure, and quick fashion.

To help small businesses provide fast, reliable delivery throughout the holidays, Lalamove is offering $10 off with promo code LACMHOU10. Business owners can try out the service, or customers can take advantage of Lalamove if they need delivery.

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