Supercharging Paid Search Campaigns with Geo Bid Modifiers

AdWords and Bing are great at connecting with in-market consumers, but they don’t provide you with many tools or capabilities to effectively segment or target different types of consumers like you can do in display advertising or on Facebook.

However, both AdWords and Bing allow you to target and bid at the zip code level using geo bid modifiers. If you think of zip codes as a way to represent audience, you can leverage AdWords/Bing geo bid modifiers capabilities to target and optimize for different audience segments in paid search, creating thousands of new ways to target and optimize performance of paid search campaigns!

Introduction to Geo Bid Modifiers

AdWords and Bing allow you to set a higher or lower bid by zip code per campaign. For example, if you have a maximum bid of $1 on a keyword, and you set a +25% bid modifier for zip code 90210, then for that zip code your maximum bid for the keyword will be $1.25.

There are approximately 42,000 zip codes in the US. Think about the zip code you live in – you and your neighbors may have many things in common such as household income, age, presence of children, ethnicity, and even the kinds of sports you are interested in or the programs you watch on TV. And these characteristics can be very different from people in another zip code 1000 miles or 10 miles away.

The Data Scarcity Challenge

Determining the right bid by zip code sounds like a great idea, but unfortunately it’s not that easy. To have enough statistically significant data for each of the 42,000 zip codes, you would need hundreds of thousands conversions per month, for each of your products! The reality is, very few marketers have this kind of scale.

For example, below is the number of conversions by zip code for an online furniture retailer with approximately $1m of paid search spend over 6 months. The “Sales” metric indicates the number of conversions. As can be seen on the map, the vast majority of zip codes (in a densely populated area like the LA metro area) have 0 conversions, with a handful of zip codes accumulating one or more conversions. The zip codes with 0 conversions are not underperforming zip codes – they may just not have enough click volume to generate any conversions. Therefore, zip code performance alone is not a reliable method to predict future performance due to data scarcity.

cCleary Audience Planner heatmap

Heatmap created using cClearly’s Audience Planner

Leveraging External Data to Solve the Data Scarcity Challenge

The solution to the data scarcity challenge is to use external data points to find similarities between zip codes. For example, suppose you knew the percentage of people with a Bachelor’s Degree by zip code, you could aggregate all zip codes based on Bachelor’s Degree percentage, and then look at the conversion rate or return on ad spend (ROAS) on an aggregated basis.

The following graph shows the correlation between percentage of people with Bachelor’s Degree and ROAS and the correlation between Adult Smokers and ROAS, by zip code. As we can easily see, the ROAS increases if there are more people with a Bachelor’s Degree in the zip code and decreases the more Smokers there are in the zip code. This implies that you can raise the geo bid modifier in zip codes with lots of people with Bachelor’s Degrees and less Smokers, and decrease the geo bid modifier in zip codes with less people with Bachelor’s Degrees and lots of Smokers.

External Data_cClearly

Graph created using cClearly’s Audience Planner

Accessing and Using External Data

The previous section described a very simple example of geo bid modifiers optimization using two dimensions (bachelor’s degree and smoking). However, academic degree or health habits data is not available in AdWords or Bing. So how does one implement this strategy? There are a couple of options:

  • Do it yourself – There are various external data sources and datasets that can be used to do a similar analysis to the one described above which may include these and many other data points. Some of these data sources are free such as the U.S. Census Bureau and some are commercial datasets sold by various vendors.
    Keep in mind that most data points would not correlate with your KPI. So how do you pick the “right” data points that are likely to show correlation? One solution is using your prior knowledge about your target market or common sense to select data points. For example, if you know that your product is geared towards more affluent consumers, perhaps household income might show strong correlation to conversion rates. Once you have a thesis on what data points might be relevant, the next step is to find and acquire the dataset, cleanse and normalize the data and then evaluate the data points relative to your KPI using one or more statistical models.
  • Commercial technologies – an alternative solution is to use a commercial geo-optimization technology. Some of these technologies already have thousands of external datasets built into them, test multiple optimization algorithms and have automated the process of finding correlations and calculating geo bid modifiers through existing integrations with AdWords and Bing.

An Effective Solution

Geo bid modifiers are a very effective solution for increasing the effectiveness of your paid search spend by targeting the audience most likely to buy from you. If you haven’t seen significant performance improvements in your paid search campaigns recently, if you are looking to decrease your cost per acquisition, looking to improve your return on ad spend or if competition is heating up in your space, put this strategy at the top of your to do list for the next quarter.

Oren Netzer
About the Author

Oren Netzer

Oren Netzer is Co-Founder and CEO of cClearly, a marketing analytics and optimization technology provider. cClearly’s technology enables pre-targeting, geo optimization, and audience look-a-likes for paid search using thousands of external data sets, increasing performance by an average of 20%.

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