The document discusses the effectiveness of geo-targeting in paid search campaigns, emphasizing that creating one campaign for multiple cities yields better results than creating individual campaigns for each city. Tests conducted revealed that using a single campaign with a list of city-level geo-targets resulted in increased impressions, clicks, and conversion rates, while also optimizing cost per click. The findings suggest that a balanced approach between overly granular and overly broad targeting is ideal for maximizing campaign performance.

1. How Granular Should You Be With Your Paid
Search Geo-Targeting?
Tests facilitated by:
Adiela Aviram, SEM Specialist
DAC Group

2.  Marketing through search at the local level is
best practice for businesses with physical
locations
 The most straightforward way to accomplish
this is to create one campaign for each city
and set the geo-target to that city alone

3.  But what if you’re in1,500 cities?
 What if you want to split your budget by
brand vs. non-brand?
◦ Could lead to a pretty messy campaign!

4.  Company with locations in 40 cities in Texas
 14 day test using 40 campaigns in 40 cities
 14 day test using 1 campaign in 40 cities
 Keywords, ad copy, max bid all stayed same

5. Metric
40 Campaigns – 40
Cities
1 Campaign – 40
Cities
% Change
Impressions 37,533 68,232 81.79%
Clicks 711 1,353 90.30%
CTR 1.89% 1.98% 4.68%
Avg. position 2.2 1.7 20.83%
Leads 12 123 925.00%
Conversion Rate 1.69% 9.09% 438.64%
Search Impr. share 62.41% 61.71% -1.12%
Search Lost IS (rank) 37.12% 31.51% -15.11%
 One campaign with multiple geo-targets yielded better results
 Gains in impressions, clicks, CTR, average position, lead volume,
CPL, conversion rate and lost impressions share due to rank
 Downside: slightly higher CPC and higher impression share loss due
to budget

6.  Company with 64 locations in California
 One campaign geo-targeted to all of
California for seven days
 For the following seven days, same campaign
was geo-targeted to the list of 64 cities
 All other variables remained the same

7.  With fewer impressions, city targeting achieved virtually the same
number of clicks (higher CTR, higher conversion rate)
 CPC was lowered, average position went in city-targeted campaigns
 Most notable: the gains in impression share and the drop in lost
impression share due to rank
Metric 1 Campaign – 1 state
1 Campaign – 64
Cities
% Change
Impressions 76,827 52,083 -32.21%
Clicks 882 879 -0.34%
CTR 1.15% 1.69% 47.01%
Avg. position 2.1 1.9 -12.53%
Leads 72 99 37.50%
Conversion Rate 8.16% 11.26% 37.97%
Search Impr. share 50.73% 59.94% 18.15%
Search Lost IS (rank) 49.27% 40.06% -18.69%

8.  from these two tests, we see that creating one
campaign for each city may be too much of a
granular choice for geo-targeting
 At the other extreme, broadening the geo-
targeting to cover the entire state may be too
broad
 The middle-ground seems to be the sweet spot
here. In both tests, one campaign with a list of
city level geo-targets had the best results

9. Adam Picker
apicker@dacgroup
DAC Group is a full-service, best-practice digital agency. We have a simple
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