Omnicom's Resolution Agency Explains the Ways Machine Learning Has Vastly Strengthened Search's Power

By Omnicom Media Group InSites Archives
Cover image for  article: Omnicom's Resolution Agency Explains the Ways Machine Learning Has Vastly Strengthened Search's Power

As one of the oldest forms of digital marketing, you'd be forgiven for thinking that paid search has already reached its peak innovation period. After all, it's been nearly 25 years since marketers discovered that search, the nexus of relevancy and recency, could be one of the most efficient tools to drive awareness, leads and sales of products and services. Yet search continues to evolve far beyond the relative simplicity of the past, driven by technological innovation, extraordinary advancements in machine leaning and artificial intelligence. There are new ways and apertures through which consumers search, such as voice, AR, QR codes and more.

Alphabet (parent company of Google) has announced that its latest artificial intelligence architecture for search, the multitask unified model (MUM), is 1,000 times more powerful than the previous architecture. That news came out in its third-quarter 2021 quarterly earnings call held in October. MUM can understand information between many contexts including images and text. That opens a myriad of new opportunities and permutations in search.

Microsoft's Bing has greatly increased its shopping capabilities through a partnership with Shopify and new categorizations in its "Departments" tab.

These are just a small subset of examples of the ever-changing dynamic nature of search. Marketers must modernize their search strategy to meet a very evolved search ecosystem that is changing exponentially.

Resolution, Omnicom Media Group's performance agency, has doubled down on its mission to modernize its clients' paid search strategies. It recommends paid search marketers use the following as starter guidelines to improve their search strategies:

Automated bidding is now able to understand auction-time signals (such as device type, query, audience membership and location), and it can synthesize these inputs in real-time. Additionally, AI can capture new auctions—searches that have never previously occurred. (Google estimates that 15% of all their queries today are brand new!)

  1. Embrace automation and features backed by machine learning. Given the amount of control that marketers have exerted around the nuance of search buys over the decades, it's understandable that it might be difficult to hand over the keys to the machine. But by relinquishing a bit of that control to let AI and machine learning do the bulk of the grind, marketers are freeing up time with simpler account setup. Additionally, less human micromanagement on individual keywords and ad units allows the business leaders to turn their focus to bigger, more valuable strategic ideation. Automated bidding is now able to understand auction-time signals (such as device type, query, audience membership and location), and it can synthesize these inputs in real-time. Additionally, AI can capture new auctions -- searches that have never previously occurred. (Google estimates that 15% of all their queries today are brand new!) A huge benefit for the modern paid search campaign is the ability for systems to manage bidding and budget pacing concurrently, eliminating the depletion of a marketer's budget before its time.
  2. Let the machine make creative choices. Automated paid search features such as responsive search ads and optimized ad rotation allow AI to test several different creative lines. As a result, it understands the type of verbiage that will trigger a response from a particular type of consumer. It utilizes engagement data to optimize the campaign. This occurs at a far greater rate than human bandwidth, allowing for greater efficiency and driving scale.
  3. Consolidate your campaigns. For machine learning to optimize at its best capacity, it needs a base level of data to scale the correct decisioning. For example, Google recommends ad groups have 3,000+ impressions per week to do the job. Resolution Agency recommends combining match types, audience targets and dynamic search ads within campaigns.
  4. Prioritize queries over keywords. Of course, there may be strategic reasons for requesting presence on specific contextually and brand-relevant search terms. However, in general, Resolution has determined, across its portfolio, that granular keyword segmentation is often unnecessary because automated bidding is auction-time, factoring in various signals beyond keywords. Additionally, evolutions to what search engines consider as "close variants" (e.g. synonyms, plurals, etc.) have blurred the lines between keyword match types.
  5. Test, learn and expand. While marketers should take all the above into account, Resolution recognizes that each product, service and campaign objective is unique and nuanced. Its final recommendation is to test what works for your business situation, but definitely experiment with modern tools. Once you have the learnings from the test, don't put them in a box, but use that knowledge to further enhance and adapt your paid search strategy. Perhaps you can apply those insights in a broader way to your digital marketing.

Search behavior's ability to evolve quickly will always yield constant changes. Marketers need to rely on organizations that are built on a culture of innovation and have developed expertise in balancing new publisher expectations and capabilities with advertiser needs. Modernization improves workflow, but it must be rooted in strategy.

By adhering to these guidelines, Resolution believes that marketers will achieve a more robust, agile and effective paid search strategy. Optimization of both man and machine will lead to positive business outcomes. With continued exponential dynamic changes to the digital search ecosystem, the best of both is needed.

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