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4 ways to simplify PPC account structure for greater returns

by Edouard Destrem

October 27, 2021

Paid search has evolved over the last few years thanks to powerful new tools enabled by machine learning. But marketers are often reluctant to give up complex legacy account structures despite these relying on yesterday’s best practices.

Too many are still using match-type-specific ad groups, duplicate keywords, funnelling, manual bidding and other old school methods, because their investments in these traditional strategies have usually paid off.

In the modern world, however, incorporating artificial intelligence (AI) and machine learning into a simpler account structure can produce far greater returns.

The benefits of simplification

Thanks to the integration of AI and machine learning, a simplified account structure makes account management more straightforward, ensures enhanced performance, enables easier identification of insights for better decision making, and reduces margins of error.

But above all, a move to a simplified account structure is a move to automated - and therefore smart - bidding. Smart bidding sets unique bids for each individual auction and queries based on goals and conversion probability; it doesn’t require granular segmentation.

Bidding takes place at auction-time and query level, and evaluates a combination of thousands of signals (beyond keywords and query) per auction to set the best bid, then optimizes results based on data from all of the advertiser’s campaigns.

Granular campaign / ad group segmentation by dimension is no longer needed as all signals are measured and optimized in real time per auction and query.

Machine learning enables smart bidding to use data from each one of an advertiser’s present and future campaigns to set unique bids per auction, regardless of how the account is segmented. The result? Maximizing clicks, impressions, and conversions to optimize campaigns becomes an automated process.

Marketers looking to simplify their account structure should adopt the following four tactics:

1. Consolidate traffic

For maximum efficiency, it’s vital to consolidate traffic in every area, from accounts and match types to devices and audiences. Once these structural changes have been made, conflicting or redundant keywords and misspellings / plurals need to be removed, and impressions per ad group amalgamated by following best practices; alternatively, Google’s auto applied recommendation (AAR) can do this automatically.

2. Broaden match types

Two of Google’s oldest automatic bidding strategies, target cost-per-acquisition (CPA) and target return on ad spend (ROAS), were modified this year, partly in order to reduce the need for constant monitoring. They continue to be available, just not as primary bidding strategies. Instead, they will be options within Google’s maximize conversions and maximize conversions value strategies.

As a result, starting to use broad match types and smart bidding together can help advertisers to reach more relevant queries that meet their performance objectives. Broad match types allow a large number of queries to be reached with only one keyword, whereas many exact keywords are required to reach the same volume.

We’re not saying that years of granular keywords and phrases should be ditched, but when it comes to maximizing conversion, broader match types deliver great results.

3. Maximize coverage and reach with Dynamic Search Ads (DSA)

Google’s DSA crawl an advertiser’s website and identify which keywords they should bid on. To make the most of DSA, it should not be used alone, but merged in a normal keyword search campaign to cover more queries and deliver more pertinent, high-ranking results.

4. Leverage dynamic features for creatives

Responsive Search Ads (RSAs) allows ads to be created that adapt to show more text – and more relevant messages – to customers. Multiple headlines and descriptions can be entered when creating a responsive search ad that adapts to different devices.

Over time, Google Ads will show the most relevant combinations for customers, enabling advertisers to reach more potential customers while learning which combinations perform best.

Conclusion

By simplifying the PPC account structure with these strategies, campaigns become easier to manage: traffic can be consolidated into fewer and larger ad groups with no unnecessary keyword match type segmentation. Multiple campaigns can be consolidated into single campaigns that generate more traffic and create additional signals, feeding more data to every aspect of machine learning campaigns.

Tags

PPC
paid search
Artificial Intelligence
AI
Machine learning