What Lessons Can the Sponsored Search Markets Learn from Financial Markets
Their central thesis is that the increasing availability of information about products, brands or media through user-generated content on social media platforms will increase the efficiency of sponsored search markets over time, just as information technology has created efficiency in financial markets. They suggest a new framework to break down keywords into attributes that determine economic value so companies can identify opportunities to exploit the mispricing of words to increase returns on search spending.
“With the vast amount of real-world data that is being generated through social media, we have the keys to the kingdom,” says Dhar, an expert in financial and electronic markets, and predictive modeling. “We can use well understood measures from finance—demand, liquidity, volatility—to determine the economic value of content, identify keyword mispricing and ultimately maximize profitability and returns.”
“Right now, there are keywords out there that are cheap, just like undervalued stocks,” says Ghose, whose research focuses on monetizing the economic value of user-generated content. “We can expect trading activity in sponsored search markets to become increasingly sophisticated as advertisers tease out mispricing and value from the markets, and measure expected returns more accurately.”
Their market analogy is based on the premise that both financial and sponsored search markets share common characteristics:
Financial exchanges and sponsored search markets are both auctions
Both are motivated to maximize liquidity (typically measured by volume), which attracts buyers and sellers in a virtuous cycle—Google is a market, not unlike an exchange, advertisers as sellers, and searchers as buyers
A portfolio is optimized for maximum expected response per unit of spending by careful selection of keywords, similar to classical portfolio optimization which attempts to maximize return per unit of risk
Both have “winners” in the auction—the best bid or ask in financial markets, versus the rank in sponsored search where there are, in effect, multiple winners
Based on their conjecture about the relative inefficiency of sponsored search markets, the professors specify several testable hypotheses (bulleted below), the answers to which can be used to develop a keyword portfolio optimization strategy. The hypotheses relate keyword prices to their buzz volume in social media, and other attributes including their specificity and the baseline frequency of use.
Assess consumer search volume of keywords, an indicator of advertiser demand that will be reflected in the price, as a capital asset pricing model for search
Measure the speed and magnitude of changes in keyword prices as a result of changes in volume of buzz around the keyword
Explore if bid prices for branded keywords within a search engine change faster than generic keywords, given an equivalent change in volume (or liquidity)
Confirm whether the prices for popular keywords exhibit lower volatility than “long tail” or niche words, which are likely to adjust more infrequently, but with changes of higher magnitude
The full article describing sponsored search market efficiency is scheduled to appear in Information Systems Research, and can be found on SSRN at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1660646
Contacts
To speak with the study authors, contact:
NYU Stern’s Office of Public Affairs
Jessica Neville, 212-998-0666
jneville@stern.nyu.edu
or
Rika Nazem, 212-998-0678
rnazem@stern.nyu.edu
October 27, 2010: http://www.businesswire.com/news/home/20101027006360/en/Lessons-Sponsored-Search-Markets-Learn-Financial-Markets