Last month, Google announced changes to the way keyword match types would behave moving forward, with the widely used Broad Match Modifier (BMM) to be sunsetted in July 2021. Since Feb 18th, Phrase matching capabilities have been expanded to encompass the previous BMM remit, effectively merging legacy BMM keywords into the new phrase match.
The Broad match product itself has been a focus of Google's development, with recent iterations incorporating additional signals, such as landing pages and implied sentiment, into the auction dynamics.
Imminently following this news was the announcement that Responsive Search Ads, the format which uses machine learning to optimise ad copy to be the most relevant user experience, will be the default ad type. Whilst Expanded Text Ads (ETAs) will be still available, RSAs will be the recommended setting moving forward, indicating that they can expect a similar fate as the "Standard Ads' in coming months/years.
These changes are the latest in a long line of Google's products to lean on machine learning capabilities to become more predictable in the bid to reach users across their inventory, and have been a work in progress for years.
Increasing mobile device usage created the need to match user expectations around speed and relevancy of information, with consumers operating in a state of "need to have it now". Google's own 'micro moment' methodology provides the framework for much of the tech development.
It is clear to see from a timeline of key events how this has rapidly increased over the past decade:
So, this leaves advertisers with the new age of matching;
As technology continues to evolve, so does best practice. Increasing automation and more predictive targeting methods mean that more complex, thorough keyword mapping is no longer beneficial.
The latest guidance is centred around consolidating campaigns according to the objective whilst keeping themes streamlined to ensure creative relevance.
Google's advice is to focus keywords on broader match types, enabling smart bidding to align with their goal. The bidding mechanism is controlled at query level rather than keyword when smart bidding is used, and so the rationale here is there will be a minimal incremental gain to repeating keywords in different match types, and the ease of management will be improved.
Campaign types like Dynamic Search Ads are set out to help advertisers increase their coverage based on their website content without having to do the hard work and build out individual groupings.
The changes aim to increase relevancy and maximise coverage potential for advertisers, and when used in conjunction with smart bidding methods, in the right way, they can provide great results. However, there are some considerations for advertisers.
Increased reliance on automated solutions reduces the manual control advertisers have over their targeting, optimisation, and messaging display. As a result, brands with regimented requirements over their brand image and messaging may find some discrepancies.
Instances of this have occurred within formats such as Smart Display and Smart Shopping, prompting further product development to re-gain advertiser control of the way the ads are displayed, and similarly the ability to 'pin' headlines within Responsive Search ads.
Google has said that whilst Phrase match coverage will expand to broader coverage, it will continue to retain the words' ordering within the keyword target, though only when deemed relevant. This means that the machine will still be responsible for determining what is/is not relevant, and therefore removing much of the previous control advertisers had over their targets.
The methodology will not impact negative keywords, so this remains a key tool that advertisers can use to optimise and ensure brand safety measures
Large scale campaigns with high volumes of conversion points are likely to benefit most from the consolidated structure and incremental boost they can capture from smart bidding AI, as well as the additional resource hours they can gain back. Smaller campaigns, however, may struggle to meet conversion thresholds for smart bidding efficacy, in which case they may need to broaden keyword matching unnecessarily or opt to shift optimisation goals away from their priority choice. Doing so poses a potential performance risk, either by lack of scale as a result of not shifting focus or efficiency by pivoting goal type.
The expanded remit, the onus on the use of broader match types, and AI-led creative solutions will help improve advertiser reach whilst reducing the need for manual keyword mining and segmentation. However, it may reach audiences outside the target as a byproduct.
As the search landscape continues to immerse into a world of machine learning and we bid farewell to the widely used broad match modifier (BMM) match type, more and more of the features advertisers previously had control over, such as bid optimisation, ad/extension copy, and ad rotation are all predominantly automated.
While these changes aim to increase relevancy and maximise coverage potential for advertisers, and when used in conjunction with smart bidding methods, in the right way, can provide great results, however they do impact advertisers.
To prepare for this roll out, advertisers should;
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