Whether online marketers are looking to localize their messaging or optimize their budget across best performing locations (or both combined), Google’s enhanced campaigns have made it possible to set location bid adjustments (LBAs) while still being able to target specific locations.
So what’s a good set-up for your program?
In addition to any other considerations which may impact your paid search account structure such as breaking out branded vs. non-branded campaigns, or categorizing campaigns by product categories and/or user intent, search marketers should definitely put some thinking into how localized they want their paid search effort to be. More specifically, one can assess the following scenarios:
The main benefit of the above hybrid structure is that marketers can get true localized search capabilities in those top locations, while keeping their paid search accounts manageable. Also, this type of structure allows for local testing – from local promotions to local mobile push and so on.
If you still haven’t got a chance to use those location bid adjustments, then you might want to consider putting together some cost and revenue geographic data from your favorite SEM platform and see whether there is room for improvement there. The odds are that there is a lot of room for improvement out there – the question is not whether there is any room for improvement or not, but how much, and is it worth the time spent optimizing local performance.
While estimating the time required for more localized campaigns can be discussed separately based on the overall business strategy and the above scenarios (localized campaigns vs. LBAs), the room for growth can be assessed by looking at the ROAS distribution across campaigns and locations, such as DMA-level ROAS for instance.
To simplify, if you are seeing the same ROAS across all DMAs, then there is no opportunity; however, if the local ROAS vary with locations, there is an opportunity. Essentially you want to see whether there are areas with a high ROAS where you’d want to bid up and get more revenue volume, and other areas with a low ROAS where you’d want to bid down and cut down cost.
Note that when measuring local ROAS, you might need to include other factors such as the impact of online advertising on offline sales, whether those occur over the phone, through an app, or in-store. It is obviously crucial to make bid and budget decisions based on true revenue numbers, as opposed to the short-term online returns you can see in AdWords, which might not reflect the true business value of your local traffic.
More specifically, once you have some relevant geographic data together, you can measure how ROAS are distributed by location, such as:
In light red those are locations with significantly lower or higher ROAS compared with the mean, more specifically the ROAS is more than 1 standard deviation away from the mean, which is not exactly statistically significant but it definitely indicates there’s room for improvement. More specifically:
For calculating location bid modifiers, you can use a previous post about it – long story short you want to calculate those location bid modifiers for each campaign/location and use the campaign average performance for reference, so if a given campaign is targeting the US and want to apply state-level LBAs, you can use the following formula for California:
[California LBA]=[California ROAS]/[Average Campaign ROAS]-1.
In addition to this previous post, you might want to consider the distance from the mean and apply softer LBAs when the local ROAS is close to the mean, and more aggressive LBAs when the local ROAS is further away from the mean.
After you have implemented location bid adjustments based on non-biased geographic data, that is without any LBAs in place, you’ll want to look into the LBA impact and what a good next step is. From my experience that’s where lots of marketers get stuck: most of them understand how to re-allocate their budget across locations based on historical geographic performance, however it can get tricky to measure geographic performance while accounting for LBA changes over time.
Ideally, you’d want to build cost and revenue models for each campaign/location pair, by LBA, where you are getting a sense of the cost and revenue change when applying different LBA values. In order to put this together, you can run a test across some campaigns/locations and look at the changes in clicks, average CPC, and conversions/revenue. In order to isolate the impact of LBAs from any other bid changes (base bids, mobile bid adjustments, etc..) you can set the intercept to zero assuming a LBA of 0% should not impact your test campaigns/locations:
In this example, using a linear regression which is far from perfect however a decent starting point:
Then you can build those models for each campaign/location pair, maybe move away from linear models for something more sophisticated, then compare the marginal returns by location and update your LBAs accordingly. That might seem like a lot of work but that will be worth the effort at least for those top cost locations where the ROAS is significantly lower or higher than average – hope you’ll find this useful!