Improve Account Manageability Segmentation of Data & Bid Optimization

1. Improve Account Manageability
Segmentation of Data & Bid Optimization

2. Data can be easily divided
taking help with these actions
on AdWords at
- Campaign Level
- Ad group Level
- Keyword level
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3. Segmenting Your Paid Search Program
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4. IdentifyActionableSegmentsInYour
Account
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Branded vs. All Campaigns: Temporarily pause all non-branded campaigns in order to
improve efficiency while securing a big chunk of the revenue.
Top vs. All campaigns: Only keep your top product campaigns when scaling down, or have
all campaigns active when scaling up. This only makes sense if your account structure was
designed adequately.
Active Match Types: Pause broad match type across the board (or even phrase + broad) in
order to decrease ad spend and improve efficiency.
Search Network: The search partners tend to account for roughly 20% of the ad spend and
10-15% of the revenue, so you might want to temporarily disable the Google search
network.
Device Targeting: Opt out from mobile impressions, or set conservative/aggressive mobile
bid modifiers depending on your mobile strategy and goals.
Location Targeting: Opt out from some poor-performing locations, or set
conservative/aggressive location bid modifiers depending on your budget and goals.
Day-Parting: Opt out from poor-performing days or times of the week, or bid up at the best-
performing times of the week.

5. AnalyzeSegments’Performance
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Now that we have identified multiple segments in the account which can easily be
toggled on or off, we can further analyze each segment’s performance in terms of both
ad spend and revenue so you can determine the best strategy given your budget and
efficiency goals.
More specifically, each segment accounts for a given percentage of the account ad
spend and revenue, so you want to calculate those percentages by pulling a couple of
reports: by campaign, by match type, by hour and day of the week, by search partners,
by device, by location, etc.
For each segment, you want to determine the impact when tweaking different settings,
given that 100% indicates maximum ad spend and revenue. In the above example,
exact match type accounts for only 65% of the ad spend and 87% of the revenue — so
you could pause all phrase and broad keywords to save 35% of ad spend and see a
limited 13% dip in revenue in the meantime.

6. Analyze Segments’ Performance
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Another example would be the search network analysis. In this instance,
Google.com accounts for 89% of the ad spend and 92% of the revenue. So you
could turn off the search network and easily anticipate the impact on ad spend
and revenue.

7. Analyze Segments’ Performance
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The same type of
analysis goes for each
segment, hence the table
beside with multiple
scenarios.

8. Simulate & Predict
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We now have broken down the account in different segments and calculated how much ad spend
and revenue they account for as a percentage of the account. We can now simulate and compare
multiple combinations using the above table, factor in seasonality and pick the most-profitable
scenario accordingly.
Say your ad spend was $25k and the revenue $300k last week, and you are currently running at
full speed with all layers set to the most aggressive set-up (100%). You’re expecting a 9% increase
in ad cost and revenue due to seasonality, and you also need to keep the ad spend under $15k
next week. In that case, you’d probably pick the following set-up (based on the chart above):
A2: All Campaigns
B2: Active Match Types = Exact + Phrase
C2: Search Network = Google + Search Partners
D2: Balanced Mobile Bid Modifiers
E2: Balanced Location Bid Modifiers
F2: Balanced Hourly Bid Modifiers
G3: All Week
H3: Branded & Non-Branded Campaigns

9. Geographic Segmentation & Bidding
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10. Segmenting Your Paid Search Account By Geography
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Segment by country for language targeting/landing page purposes – this pretty much
goes without saying. Note that some folks are testing a different way of geo-targeting
wherein you exclude everything but a specific location, as opposed to targeting a
specific location. This workaround might need some more in-depth testing before any
general conclusions can be drawn.
Segment by time zone for day-parting optimization purposes. For instance, in the
US, it makes sense to segment your top campaigns by time zone to be able to set
accurate day-parting modifiers and leverage those peak hours locally.
Segment by physical store for custom messaging purposes (if applicable). This is a
time-consuming task, but necessary if you want to have full control over the ads
served in specific locations.
Leverage location bid modifiers at the campaign level (they are not available at the
ad group level). That will allow you to bid more aggressively in the best-performing
locations — and, conversely, save money in poor-performing locations — without
altering the actual account structure.

11. Determining Mobile Bid Multipliers
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You can relate it to the Desktop/Tablet CPA target, such as:
Mobile CPA target = X *Desktop&Tablet CPA, where X is a multiplier which reflects
the role of mobile impressions for your business.
For instance, you might be able to determine that you are ok with your mobile CPA
being twice as great as your Desktop&Tablet CPA since those mobile impressions are
more about brand awareness, not so much about immediate conversions.
Then, for a given campaign, say your Desktop&Tablet CPA is $29.46 vs. $118.28 on
mobile, and you are ok with the mobile CPA being twice as great as on
Desktop&Tablet, then your mobile bid multiplier can be defined as
Mobile Bid Multiplier = (Mobile CPA Target * 100 / Historical Mobile CPA )– 1,
such as 2 * $29.46 * 100 / $118.28 – 1 = – 50%.
As a result, your mobile bid multiplier should be negative 50% for this particular
campaign to achieve your mobile goal.

12. Determining Geo Bid Multipliers
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Your geo bid multipliers can be determined such as
Geo Bid Multiplier = (Avg. Campaign CPA / Geo CPA) – 1.
For instance, if your campaign-level CPA is $18.40, while your New York CPA is
$13.27, you might want to invest more in New York and set your New York bid
multiplier to ($18.40 / $13.27) – 1 = 39%.
Check the file to
understand