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March 4, 2015
HOW TO REACH YOUR GOALS WITH SMART BIDDING
Lauryan Feijen
Online Advertising Strategist
@LauryanF
searchmarketingexpo.com
@LauryanF #SMX #23B
ABOUT ME
o PPC since 2009
o Responsible for Quality & Learning @
o Excel nerd
o Music lover & musician
o Husband & dad
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@LauryanF #SMX #23B
HOW CAN WE MANAGE OUR BIDS
o DEFINE OUR TARGET
o REACHING OUR TARGET
o OVERCOME OBSTACLES
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WE NEED TO KNOW WHERE TO GO
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WHAT’S IN YOUR LUGGAGE?
o CPA VS ROAS
o VOLUME VS EFFICIENCY
o BRAND VS NON-BRAND
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MANUAL (ADWORDS INTERFACE)
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MANUAL (WITH EXCEL)
CPA BIDDING
ROAS BIDDING
𝑵𝒆𝒘 𝒃𝒊𝒅 = 𝐶𝑃𝐴 𝑔𝑜𝑎𝑙 𝑥 𝐻𝑖𝑠𝑡𝑜𝑟𝑖𝑐𝑎𝑙 𝑐𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜𝑛 𝑟𝑎𝑡𝑒
𝑵𝒆𝒘 𝒃𝒊𝒅 =𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑣𝑎𝑙𝑢𝑒 𝑝𝑒𝑟 𝑐𝑙𝑖𝑐𝑘
𝑅𝑂𝐴𝑆 𝑡𝑎𝑟𝑔𝑒𝑡
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@LauryanF #SMX #23B
MANUAL (WITH EXCEL)
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SCRIPTS AND AUTOMATED RULES
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FLEXIBLE BID STRATEGIES
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3RD PARTY PLATFORMS
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OVERCOME OBSTACLES
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POSSIBLE OBSTACLES KW already in top position New bid exceeds top-of-page bid estimates New bid less than first-page bid estimates Keyword has insufficient data
OVERCOME OBSTACLES
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TOP-OF-PAGE BID ESTIMATES
Workaround Cut off bids X% over the ToP bid estimate. Example 20%
Cut off
$ 1.50 New bid
$ 1.00 ToP est.
$ 1.20 New bid
(ToP x 120%)
OVERCOME OBSTACLES
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FIRST PAGE BID ESTIMATES How to handle o Check Quality Score o Other reason for keyword to be online? o Consider pausing the keyword Keep in mind FP bid estimates aren’t always accurate
OVERCOME OBSTACLES
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KEYWORDS WITH INSUFFICIENT DATA Fact: Not all keywords in your account convert How do we bid for keywords with insufficient conversion data? The question becomes: “How probable is it the keyword will convert in the future?”
OVERCOME OBSTACLES
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KEYWORDS WITH INSUFFICIENT DATA Probability of keyword X converting in future Using AdGroup, Campaign or Account averages Using ‘buckets’ of keyword themes Filtering out extremes
OVERCOME OBSTACLES
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OVERCOME OBSTACLES
BUCKETING KEYWORDS Cheap flights Barcelona Flight tickets Paris Cheap tickets Los Angeles New York flight tickets Low cost flights to Paris Economy flight Hong Kong
1 – Cheap 2 – European 3 – Intercontinental 4 – Flights vs Tickets
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FILTERING OUT EXTREMES Example: Bayesian rating system
Example Ex. 1 Ex. 2 Ex. 3 Ex. 4 Ex. 5
Avg clicks 1,250 1,250 1,250 1,250 1,250
Avg conv rate 1.50% 1.50% 1.50% 1.50% 1.50%
Kw clicks 500 1.000 2.000 1 500
Kw conv rate 0.50% 0.50% 0.50% 100.00% 0.00%
Result 1.21% 1.06% 0.88% 1.58% 1.07%
OVERCOME OBSTACLES
Avg. Clicks × Avg. Conv. Rate + Keywords clicks × Keyword Conv. Rate
Avg. clicks + Keyword clicks
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