In the first half of 2013, search advertising represented 43% of all digital ad revenue. The primary reason paid search remains so successful is that it creates a direct link between users and their intent.
For marketers, the ability to deliver an ad promoting running shoes to users searching for [best running shoes] represents a revenue opportunity that will command an estimated 45.1% share of US digital ad spend in 2014 — that’s more than display, email, or mobile messaging. To date, these channels have failed to match the performance characteristics of search. However, audience-oriented advertising combined with the power of “big data” is starting to change that.
At The Corner Of Intent & Audience
Across the digital landscape, advertisers increasingly have the ability to understand their customers not just by their online declarations (e.g., I need “new running shoes”), but also by their digital trail — the footprints of intent-based behavior.
A customer’s previous visits to a product page, mobile check-ins and social shares can be combined with offline, CRM and third-party data to create a powerful combination of attributes. When aggregated across a large population of individuals, this provides advertisers with a clear understanding of their customers across all channels, not just search.
Reshaping The Digital Landscape
Today, the integration of audience data in digital advertising exists most commonly in display. Across the Google Display Network, marketers can target users by interest categories; and as announced this month, audience-oriented advertising will become a focal point for Yahoo!’s new digital advertising model. However, just beyond these networks, there are four near-term applications of audience-oriented data that have far-reaching implications for performance marketers.
Performance marketers today can make some pretty good guesses about their customers based on search keywords and conversion tendencies. In structuring search campaigns, many advertisers build a “taxonomy of intent,” sometimes without explicitly knowing so. If a travel company creates a “budget travel” campaign and a “luxury travel” campaign, it’s easy to make assumptions about the differences in the users who gravitate toward one versus the other.
“Coloring” those insights with additional data — household income, marital status, age — will validate or potentially challenge those assumptions. For example, it may be that the average household income for “luxury travel” consumers is much lower than expected because they’re more aspirational buyers. This revelation might cause an advertiser to rethink creative strategy.
Armed with powerful insights on what types of customers they are acquiring, many advertisers may also rethink their bidding strategy on media. For many, the type of customer is just as important as the value of the conversion produced, and it is crucial to understand the projected lifetime value of a customer according to certain identifiable traits.
If an advertiser discovers that individuals in their forties are more valuable than individuals in their twenties, then media budgets should be reallocated accordingly. Consequently, the concept of “buying a demographic” in the world of display may be adapted to search by simply bidding up keywords that have a higher propensity to drive engagement from a more valuable demographic.
Retargeting users on search has only recently become a reality through Google Remarketing Lists for Search Ads (RLSA), but the impact has already altered the search landscape. In the simplest approach to search retargeting, an advertiser can implement distinct strategies for dealing with just two audiences: people who have been to my site before and people who have not.
An advertiser may decide that users who have already visited are closer to purchase and might bid more aggressively when that audience searches for a relevant keyword. The potential targeting options are far greater when one starts to layer additional first- or third-party data into the mix.
For example, knowing the age and gender of a potential customer might alter an advertiser’s creative or bidding strategy when retargeting that individual. Audience data will enable this level of granularity, and these same principles can also be applied to display and social advertising.
As advertisers get smarter about understanding their existing customers, it becomes easier to identify users that have a high probability of converting into new customers. Algorithmic tools have made it possible to not only analyze a segment of existing customers (e.g., “budget travelers”), but also to identify similar segments of the same population (e.g., “spring break planners”) who have the potential to convert at a similar rate. This level of insight enables marketers to quickly generate display or social ads targeted at those similar segments and acquire more like-minded customers.
What’s Your Audience?
Search linked with consumer-intent continues to prove highly effective, delivering impressive results for digital advertisers. However, advertisers’ ability to marry intent-based and audience-oriented data is redefining the way marketers engage with users and optimize their marketing mix, from targeting audience segments with the highest lifetime value customers to delivering gender-specific ad creative.
Tapping into user intent is no longer adequate for remaining competitive in the new digital landscape. Advertisers must increase their level of sophistication and continue investing in new, innovative technology to win the battle for revenue online.
Opinions expressed in the article are those of the guest author and not necessarily Search Engine Land.