Rather than re-hash the numbers, consider instead double-clicking on the search ad strength with the help of search engine marketers. Drawing on 3Q reports from Kenshoo and Merkle, including their associated conference calls, and a performance marketing event held after GOOGL's results last Thursday, which included a presentation from GOOGL, provides hypotheses on the continuing strength in paid search ad spending growth.
One hypothesis is that shift of budgets from non-digital advertising is a key driver of search ad spending growth. Merkle, for its part, quizzed last Thursday on what budget shifts were fueling the 24% growth that it saw in GOOGL U.S. ad spending in 3Q, up from 23% in 2Q, offered this view, noting the continuing shift to e-commerce. Others note how new disruptors, like Jet.com, help drive this type of shift to search advertising. If so, watch for continuing pressure on non-digital ad channels.
Another hypothesis is that changing usage of mobile search, as the mix of shopping activity shifts to younger users from older users, is helping to drive the click volume growth needed to more than offset cost-per-click declines. Google Sites paid click growth, which includes the impact of YouTube as well as Google search, was 55% in 3Q17, and 61% in 2Q17. Although company-specific changes in the search engine results page—including more ads and, in particular, more Product Listing Ads, which have higher click-through-rates than text ads --undoubtedly contribute to these high growth rates, some search engine marketers believe that user demographics is another tailwind on click growth.
As to Google's initiatives, the biggest lift may be coming from use of remarketing lists for search ads. This tool can, for example, allow advertisers to shift their bidding to new customers away from existing customers, particularly helpful for expensive keywords. SEMs note less current impact from tools like customer match.
FA-getabout competition to search advertising? First, Facebook has become a more response-oriented medium over the past two years, and thus a potentially greater competitor for paid search. The shift of advertising to the newsfeed from the right rail has contributed to the increasing use of FB by performance advertisers. However, one view from SEMs is that FB has not fundamentally changed its positioning against search for performance advertisers, as search captures not only purchase intent but purchase "inflection." That is to say, the share of users ready to exit the application and transact is much greater on search than social platforms. FB has improved its targeting of purchase intent, but its users still do not use FB primarily to purchase, i.e., at an inflection point where a performance ad would be particularly effective. The higher readiness to buy when searching may somewhat inure search to shifts of performance ad budgets to social.
Second, there may be reasons to question AMZN's impact on search advertising, above and beyond the relatively small scale of AMZN advertising at present. There has been much commentary about how the growing numbers of Amazon Prime users are more likely to begin their shopping journey on Amazon than on search engines. However, some SEMs think that Amazon product search may be more complementary than cannibalistic of paid search queries, even for Prime users. The rising mix of PLAs specifically may be helping GOOGL forestall loss of search ad clicks and budgets to Amazon.
Shifting to GOOGL's overall position in the advertising ecosystem, the increasing role in marketing of machine learning, and "deep learning" in particular, may be a relatively unheralded challenge for agencies' relationships with their clients. Per the performance advertising presentation, the majority of GOOGL's conversations around performance advertising with large marketers and agencies this year concern machine learning and artificial intelligence.
The applications of deep learning cut across a wide range of services traditionally provided by ad agencies. Deep learning reflects use of training of an algorithm for improved data classification (e.g., whether a picture is of a dog or a cat), and its applications include: marketing design (including copy testing), market trends, competitive intelligence, campaign measurement (attribution in particular), and brand tracking (including quantification of how brands relate to each other).
Many larger marketers may be missing that, as they increasingly incorporate chat bots and voice apps into their marketing, search advertising expertise—at Google and elsewhere—will become even more strategically valuable. The learnings from search for the responsiveness needed in chat bots and voice apps could give GOOGL further inroads into advising on technology trends important to major marketers. For an example of a good voice app, check out the one from Domino's.
Oh, and talk about pricing pressure—many of Google's machine learning tools are available for free. Google's machine learning APIs are available free of charge, and yet are still probably substantially under-utilized at present by the performance marketing teams at large marketers. For what can be done with the Cloud Vision API, for example, check out the Ad Vision report from IPG Mediabrands, which gave the results of tests of online ad effectiveness using machine learning. Additional low-cost solutions are available further up the value chain, including Cloud ML and TensorFlow, which are particularly valuable for data scientists and market researchers.
One implication of GOOGL's current business momentum is how its ad growth has funded and looks likely to continue to fund increasing technology inroads into not just the businesses of its FAANG brethren, but the ad agencies themselves. Of late, investor concern about the ad agency business model has centered on competition from consulting firms. Investors might better swing their gaze back to the darker clouds of the "frenemy thesis," which has been on the shelf for a while, perhaps too long.
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