According to eMarketer, U.S. companies are becoming increasingly aware of the “Big Data,” concept where all of the relevant internal and external data available to businesses is applied to the decision making process for marketing. The biggest concern about Big Data is the uncertainty around how a business will effectively manage and make use of such a large store of information.
Forty-five percent of respondents in a Connotate survey said that the top challenge faced with Big Data was that they “must devote significant manpower to collecting and analyzing data. Too much information to effectively leverage for business” came in second at 44 percent of respondents, and other concerns ranged from the lack of value afforded by common data sources to too many data sources to track.
These concerns are laying the groundwork for a push toward a more comprehensive data analytics solution, yet many businesses are unaware of their options.
One way that some marketers and advertisers are dealing with the overload of consumer data is through the use of predictive modeling. A solution like Semcasting’s IP Audience Zones can use reliable, statistically valid algorithms to divide U.S. consumers into Zones of potential online audience members who have demonstrated a pattern of behaving the same way. IP Audience Zones has packaged an overwhelming amount of consumer demographics and transaction data to identify audiences who will be favorable for advertisers in the long run. IP Zones ad targeting draws on over 750 location-based psychographic and demographic data points in order to narrow down the best potential audience pools who are the likeliest candidates to respond to a campaign. This puts invaluable information in the hands of marketers, who can target the bulk of their ad spend toward the right audience members at the right time without getting lost in a sea of irrelevant browsing history and then having to select audience segments that are typically based on a single purchase or small number of inferred data points.