Arvind Rangaswamy, Professor of Marketing, Penn State Smeal College of Business
Advertising 2020: Big Role for Big Data
The world of advertising is changing fundamentally and irreversibly. The advertising concepts, rules, methods, and processes optimized for the physical world are inadequate for the digital world, and vice versa. Many of the structural changes taking place in advertising are covered by other authors. Here, I focus on the promise and perils of big data as they apply to advertising.
The purpose of advertising will be the same in 2020 as it is today, i.e., to communicate with various target audiences to inform, engage, and influence them toward certain enduring actions that benefit the advertiser in some way. In 2020, people’s desire for free content and entertainment will remain unabated, and therefore, advertising will still flourish by providing these, even though advertising as we know it will have morphed considerably. By 2020, there will be further fragmentation of audiences due to the growing mix and diversity of media devices, channels, advertising networks, and platforms. And, consumers will have broader sets of lifestyles and interest groups to which they belong. Advertising to mass markets via TV, radio, and movies will remain useful for brand building as well as for encouraging specific actions (e.g., asking consumers to text a message to receive a coupon in response to a TV ad). At the same time, ad emphasis will shift to smart devices such as smartphones, interactive TV, PC’s and tablets which can deliver advertising contextualized to a specific consumer, time, event, and even intent of that consumer, directed either for brand building or for influencing specific actions.
Traditional TV advertising has exceled in efficiently reaching mass audiences, especially those consumers at the top of the decision funnel. Search advertising in digital media has exceled in reaching individual consumers efficiently, especially those at bottom of the decision funnel. But, advertising strategies are increasingly straddling both the physical and the digital worlds because of the availability of “big data” about consumers that encompasses their presence and behaviors in both worlds. Big data is defined as “…large pools of data that can be captured, communicated, aggregated, stored and analyzed” (McKinsey & Co. 2011). Ad Exchanges and DSPs (Demand Side Platforms) are already aggregating first-party data (i.e., data provided by an advertiser) with third-party data provided by other players in their eco-systems (e.g., cookies, social graphs, Foursquare check-ins, cell phone locations, as well as data from partner web sites). Over time, big data will encompass demographic, lifestyle, search, purchase, and other data either provided by consumers themselves, or obtained unobtrusively online. Service providers will also develop technologies to better integrate online data from multiple devices (e.g., PC, tablet, cell phone) with siloed offline data (e.g., TV viewing, magazine readership, store purchases, credit ratings, loyalty card data, and public records). With integrated data and associated technologies, consumer profiles will be frequently updated and individual consumers will be assigned to one or more micro-segments for purposes of advertising, akin to the notion of the “Daily You” (Turow, 2011). As a result, advertisers will be able to do two things they have not been able to do well in the past: (1) Develop an automated real-time understanding of each consumer in their target audience, and (2) Automatically contextualize advertising to communicate the right message to the right person at the right time. Such advertising is neither reactive nor proactive – it is opportunistic, matching the message with the context.
Consider, for example, a consumer in 2020 viewing a web page (or a program on interactive TV) displaying current weather conditions in London. If the advertiser recognizes that the IP address or physical location of this consumer is London, it can show ads for a play or another event taking place in London, or offer a coupon that could be redeemed at a London retailer. If the IP address is from a distant location, suggesting that a web visitor might be traveling to London, the advertisement could be for a hotel in London. If the system figures out (with the help of big data) that this consumer is female, the advertisement could be contextualized to emphasize the special features of the hotel that are designed for women travelers. If the web visitor is a high-income female art collector, the advertisement could be for a London art gallery. Ad contextualizing is already happening to some extent in the digital media, and will be commonplace by 2020. For example, advertisers today can submit automated bids in real-time to an Ad exchange to obtain display space for showing an ad to a consumer with a specific profile who is visiting a web site. Note that ad contextualization here is based only on recognizing the profile of the consumer, not his or her identity.
Done the right way, contextualized advertising can be cost-effective for advertisers as well as being more meaningful for consumers. There are, however, many challenges that need to be addressed now before there is widespread use of big data in a way that benefits both consumers and advertisers. Here are a few key challenges:
- Various stakeholders in society (e.g., advertisers, government, consumers, public advocacy groups) have to work together to establish acceptable legal, institutional, and technological controls to protect the privacy of consumers as well as the security of the data for the data “owners.” This will be a major challenge.
- Advertisers need to experiment with, and learn, the new rules of engagement that will earn them the consumers’ trust, when the consumers know that advertisers could be using various types of sensitive data about them. Recently, Target used data mining to figure out that a teen girl was pregnant, before this was known to her father, and used that knowledge to send coupons for cribs and baby clothes. The ensuing discussions in the blogosphere (e.g., NYTimes.com, February 18, 2012) provide cautionary insights about how advertisers should use sensitive data – consumer wrath can spread quickly today.
- The curricula at Business Schools and Communication Colleges need to be revamped; many are still focused primarily on traditional advertising and marketing. As a result, there is a projected acute shortage of talented managers and analysts who combine business skills with a deep understanding of big data and the technologies that will be driving advertising in 2020.
McKinsey and Company Report (May 2011), “Big data: The next frontier for innovation, competition, and productivity.”
Turow, Joseph (2011), The Daily You, Yale University Press: New Haven, CT.