Photo of Dr. Xueming Luo
Dr. Xueming Luo

There’s a crucial strategy in online advertising that could revolutionize the way marketing agencies target online consumers, according to Fox School of Business researcher.

Dr. Xueming Luo studied how the strategy of competitor-poaching in online advertising influences consumer behavior. His most-recent publication on the topic was named Best Track Paper in Social Media & Digital Marketing at the 2015 American Marketing Association Winter Educator Conference Feb. 14 in San Antonio, Texas. It also received the conference’s honorable-mention distinction among all submissions.

Competitor-poaching in online advertising is responsible for why consumers can search the term “iPhone” using Google’s search engine, and corresponding ads for the Samsung Galaxy, Apple’s closest competitor, will appear, said Luo, Professor of Marketing, Strategy, and Management Information Systems. In his research, Luo uncovered that this strategy results in “clicks wasted,” as consumers glance over the competitor’s ads while remaining loyal to their initial preferences.

“It’s a double-edged sword,” Luo said. “You can increase the impression of the competitor’s brand, but you cannot get consumers to purchase the poaching brand.”

This effect is partly seen because online consumers often develop specific brand loyalties by word of mouth or from reviews that sites like Amazon and Google provide, he said. Firms, Luo found, seek to continually build brand equity and increase positive socialization around their products in order to thwart attempts at online poaching.

“Online poaching impresses non-loyal customers, but fails to get more sales conversion from customers who have high loyalty to the brand under attack” Luo said.

Asking a consumer why they want or prefer a certain product or brand, and how price influences their decisions, can help clarify what incentivizes shoppers, Luo said. Marketing agencies should then target their competitor’s keywords with advertisements that include discounts, he suggested, to capture consumer curiosity.

“To switch consumers from a brand, you need a deeper incentive, such as a 30-percent discount,” Luo said. “If you do this the wrong way, you’ll waste your money. That method can only engender clicks, but not sales conversion.”

This research, Luo said, is a part of his greater interest in how online marketing interweaves big-data analytics, mobile strategies, and consumer insights. As founder of the Global Center on Big Data in Mobile Analytics, which is housed at the Fox School, Luo is interested in investigating how big data gleaned from search engines reveal varying patterns in the evolving sphere of online ads and mobile targeting.

“This is a great way to outsmart competitors and connect customers for superior company performance,” Luo said.

This study conceptualizes product uncertainty and examines its effects and antecedents in online markets for used cars. Using a unique dataset comprised of secondary transaction data from used cars on eBay Motors matched with primary data from 331 buyers who bid on these used cars. The results distinguish between product and seller uncertainty, show that product uncertainty has a stronger effect on price premiums than seller uncertainty, and identify the most influential information signals that reduce product uncertainty.

While online product reviews are useful for learning about products, and the average rating is used to infer product quality, we identify two self-selection biases that render the average rating a poor proxy for product quality. This is because consumers with extreme (either positive or negative) views are more likely to write their reviews compared to consumers with more moderate reviews, resulting in suppression of moderate ratings. Also, consumers who are positively predisposed toward a product are more likely to purchase a product and write a (positive) review, thus inflating the average rating. We show that these two self-selection biases decrease consumer surplus by rendering the average rating a biased estimator of product quality. This study suggests the need for overcoming these biases in online product reviews to allow consumers to make sound purchasing decisions.

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