The structure of a paid search account impacts management, budgeting, reporting, and performance. Driven by automation and machine learning, the impact to all four has evolved slowly but significantly. Modern paid search accounts must balance new publisher expectations and capabilities with advertiser needs.
Automation and machine learning have become core and central product strategies for Google and Microsoft in recent years. Product updates have included:
New campaign types (e.g. Local, Discovery, MSAN) debuted and new features were launched
(e.g. Data Driven Attribution, Optimized Ad Rotation)
Keyword matching evolved and match types blurred (e.g. close variants, Broad Match
Natural language processing has improved (e.g. BERT, BigBird)
Search engines have learned to analyze various auction signals in real time (e.g. location, device, query, audience membership)
Dynamic ad formats have been prioritized (e.g. Responsive Search Ads)
Search behavior has also evolved: mobile queries overtaking desktop in 2015, queries becoming conversational (written and verbal), and the expectation for quick answers. This is a mere sample, but also evolving was the fundamental research process. Ad touches before a conversion are higher than ever because of fragmented, cross-channel consumer journeys. Cross-device conversion tracking measures the influence of up-stream queries on down-stream conversions, but it’s one piece of a large puzzle.
The confluence of machine learning and search behavior evolution lead us to today’s understanding of “modern search.” The focus is now on contextual and query-level signals over keywords, and automation over manual management. Although beneficial in many ways (always-on optimizations, smarter bidding, and time-saving1), the productization of these developments were built with ubiquity in mind. Therefore, large advertisers and agencies have needed to balance control and trust. How accounts are structured plays into this balance; account teams should “lean in” to automation while still managing it and the needs of the business.
What does a modern paid search account look like? It centers around two pillars:
1.Automation via machine learning. Machine learning is the process by which computers and algorithms self-educate
by testing combinations, collecting data, and validating results against a provided goal. The more data the machine has, the faster it learns and better it works. Embracing automation and features backed by machine learning brings an additional always-on, goal-focused player to your team.
2.Operational efficiency via consolidation. Because data feeds the machine and machine learning works at the ad group level, Google recommends ad groups have 3,000+ impressions per week for machine learning to work best. This means consolidation is advised. For example, we recommend combining match types, audience targets, and Dynamic Search Ads into one single campaign. Additionally, ad groups could have less strict keyword separation. Finally, we’ve learned that granular keyword segmentation is often unnecessary as automated bidding is auction-time, factoring in various signals beyond keywords.
Building on those pillars, it’s best to itemize the elements and settings that exist in an account and identify what must be done, should be done, and no longer needs to be done.
The principles of modern search aren’t new; machine learning has matured and been integrated into most aspects of Google and Microsoft paid search. However, rapid changes to search and shopping behavior during the pandemic expedited the push to modernize account structures. What agencies have long navigated is the ubiquity with which many automated features are built. Small businesses or beginners of Google Ads and Microsoft Advertising have lots to gain from simple campaign setups and management. Agencies and large advertisers, however, have resisted this progressive loss of control due to “black box” concerns. Additionally, purposeful campaign segmentations can account for what automation sometimes lacks in ad control and data visibility. That said, there are plenty of benefits for top players to reap. Simpler account setup means more time on strategy and less time managing comprehensive keywords and ads. Automated bidding has matured to understand auction-time signals and new auctions altogether (15% of Google queries today are brand new). Dynamic creative improves ad relevance with hyper-focused messaging. Data Driven Attribution more responsibly assigns credit to assistive touchpoints in a conversion journey. These benefits outweigh losses of control, even if certain elements are irrelevant or worrisome to certain advertisers and verticals. However, that only becomes problematic when not adopting a feature puts an advertiser at a disadvantage (not to be misconstrued as opt-out penalization). In some cases, though, it certainly would. Manual bidding can’t adjust the way automated bidding can, for instance.
Advertisers who’ve yet to modernize their account structure around automation and operational efficiency aren’t on the clock. Hesitancy to abandon the long-accepted approach of segmentation and control is understandable, but we know search behavior’s ability to evolve quickly will always yield constant changes. It’s advantageous if your paid search account is structured to respond to those changes. If a restructure is in your project plan, consider the advice here. If you’re curious but cautious, test into it with a smaller account or subset of campaigns. Not all advertisers and verticals will benefit the same way from the same changes. Pharmaceutical or Financial Services advertisers, for example, must consider legal requirements, and all advertisers want brand control. At minimum, it’s important to brainstorm how your paid search program can modernize within its constraints. It’s also important to understand the differences between publishers. For example, Google’s Discovery and Microsoft’s MSAN campaigns are similar but different, match types function differently across engines, and ad extensions aren’t always the same.
Although modernization improves workflow, it must be rooted in strategy. Data collection and visibility needs for the entire marketing plan should guide the use of automation on Google and Microsoft. Only then are paid search accounts reflective of broader goals and structured to achieve them.