Author: / On: March 18, 2014 / Categories: News

This article was originally written by Cari Barcas and published by U-T San Diego on March 17, 2014. Read original article here.

The field of data collection is advancing at a rapid pace along with the momentum of technological progress, presenting companies with fresh opportunities and new challenges for leveraging the information they can gather about their customers.

As the quantity of information collected has increased exponentially in recent years, companies have access to unprecedented levels of insight into consumer behavior, which can help them predict everything from buying habits to brand loyalty and more. It’s often called “predictive modeling.”

The challenge is how to best collect and process these massive amounts of data, and then how to act on that data.


One San Diego company taking advantage of big data is marketing research firm Luth Research, where experts report a rising demand from clients for their customers’ behavioral data.

“The big data era calls for a new set of data collection capabilities,” said Luth’s executive vice president of research, Becky Wu. “Luth has shifted our direction to invest in developing technologies that can be downloaded to consumers’ devices computer, mobile phone and tablets, and track their behaviors with their permission.

“Behavior data provides a wealth of factual information that doesn’t rely on individuals’ memory,” she said. “Furthermore, we integrate that with surveys, which are now used to focus on the why and attitudes behind the consumer behaviors.”


Dean Abbott, predictive analytics consultant and president of San Diego-based Abbott Analytics, uses data collection systems to help build models that predict consumer behavior.

Abbott seized upon the evolving field of data collection in early 2010 by co-founding Smarter Remarketer, where he serves as chief scientist. The recent recipient of a $7 million investment from venture capital firm Battery Ventures, Smarter Remarketer collects detailed behavioral data from retailer websites to give businesses a better sense of what customers want from online shopping experiences.

“Big data means we have more information on a more granular level (or level of detail) to make decisions on more specific populations of data, whether the granularity means a specific (product) or people living in a ZIP code,” Abbott said.


He said various types of data collection helps companies connect with consumers and give companies a fuller picture of those consumer habits.

“For example, if instead of collecting only data on a consumer’s interaction with a website, if you can also measure how they interact with your brand through an iPhone or Android app, you have more sources of data to provide a more complete view of the customer,” Abbott said. “The more complete view of the customer enables retailers to communicate a more relevant message to the customer based on their behavior.”


Big data also allows businesses to observe and predict increasingly subtle trends that would not have been evident even five years ago.

“Statistical significance is based on counts,” Abbott explained. “If we observe that 10 people buy a product online, we can’t be very sure of the reason why they purchased that product. However, if 1,000 people buy a particular project, we have a much better idea which factors are most influential in the purchase, whether that be their age, prior interactions on the web site, how they found out about the product, if they used a coupon or promotional code, etc.”

All of these “predictive” behaviors can help a company make decisions on how and what to market, who to target and much more.

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