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© Search Laboratory Ltd 2014. All rights reserved.
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Maximise Visibility & Minimise Waste:
PPC Optimisation Beyond Human
Capabilities
Stewart Robertson – Technical PPC Manager
Agenda
Granularity
Difficulties of scale
– Lots of products
– Staying up to date
Bidding at scale
– Low volume terms
– Comparison groups
– Smoothing
Summary
The benefits of a granular approach
Granular ad text
If you searched for ‘purple scarf’ which ad would YOU click?
Granular bidding
Which of these users is more valuable to you?
Granularity
Granularity means:
– More specific, better performing ad texts
– Bidding the correct amount for every possible
keyword
– Higher ROI, less waste, better returns
The problems of scale
Example
A company specialising in Red Armani Fedora Hats could
end up with ad groups such as these:
– Red Armani Fedora Hats
– Armani Fedora Hats
– Red Fedora Hats
– Red Fedoras
– Red Hats
– Fedoras
– Hats
– Headwear
We also might show ad text such as “Up to 25% off Red
Fedora Hats” or “Armani Red Fedoras from £30”, etc.
The problem of scale
But what do we do if the company also sells:
– 10 colours?
– 10 brands?
– 5 sizes?
Plus a similar range of top hats?
… And caps too?
Very quickly the numbers get too big to deal with
How do we solve it?
More people or less granularity?
Equivalently:
– More operating costs or lower return?
How about a third option…
Tailored automation
Unique
Bespoke
Flexible
Creation
Updating
Bidding
Error
checking
Integration
Keywords
Ads
Ad groups
Campaigns
Prices
Stock
Sales
Pauses
Keyword-level
Structured
Mathematical
Double checks
Manual & automated
Ad platform API
Process overview
Item Id Brand Product Classification Colour RRP Price
A0001 Armani Headwear > Hats > Fedoras Red £40.00 £30.00
A0002 Nike Headwear > Hats > Fedoras Red £38.00 £10.00
A0003 Haat Headwear > Hats > Fedoras Red £27.00 £27.00
A0004 D&G Headwear > Hats > Fedoras Red £412.00 £299.00
A0005 Prada Headwear > Hats > Fedoras Red £38.00 £30.00
A0006 Kangol Headwear > Hats > Fedoras Red £29.99 £29.99
A0007 Boss Headwear > Hats > Fedoras Red £99.00 £67.00
A0008 Nike Headwear > Hats > Fedoras Red £18.00 £14.00
Process overview
Red Armani Fedora Hats
Armani Fedora Hats
Red Fedora Hats
Red Fedoras
Red Hats
Fedoras
Hats
Headwear
Extract fields
from feed
•Convert into usable
text-strings
•Manually build
Synonyms
•Derive categories
from taxonomy or
classification
Plan targeting
item types
•Combinations of
fields
•Multipliers
Derive keyword
& ad logic
•Synonyms
•Multipliers
•Templates
•Thresholds
Write automation
•Parse feed into
targeting items
•Generate campaigns,
keywords, ads, etc.
•Sync with Ad Platform
Process overview
Process overview – key points
Product level alone is not normally enough
– Cover higher level categories
It’s not enough to have business-specific
templates
– we need business-specific logic
Setup needs a high level of human input
Ask the right questions!
Bidding at scale
The bidding problem
What is the conversion rate of a
keyword with 1 conversion from 7
clicks?
Only 95% sure that the Conversion Rate is
between 0.36% and 57.87%
The bidding problem
Determining the conversion rate on broad high-
volume terms is easy
The problem occurs in low-volume long tailed terms
How can we get the most accurate measure of
conversion rate?
– Look to other similar terms
– External data
Similar terms
Group keywords based on user intent:
“Red Armani Fedora”
“Blue Armani Fedora”
“Green Armani Fedora”
“Cheapest Armani Fedora”
“Discount Armani Fedora”
“Armani Fedora”
“Armani Fedora Hat”
Comparison groups - Smoothing
Pool statistics from the comparison group
– But don’t ignore the keyword’s own stats:
Keyword 3%
150 clicks
Group 1.5%
10,000 clicks
2%
2.5%
Keyword 3%
300 clicks
Group 1.5%
10,000 clicks
Comparison groups - Smoothing
So how do we incorporate external data? We believe our keyword out-
performs the group by 50%
2.5%
Keyword 3%
300 clicks
Group 1.5%
10,000 clicks
2.75%
Keyword 3%
300 clicks
Adjusted Group
Rate 2.25%
BidLabTM – How it works
Custom tree structure based on
intention
All keywords will use the most
relevant data
Very tightly-grouped in a natural
fashion
BidLabTM
Daily process
Download
Statistics
from API
Calculate
Conversion
Rates
Perform
Autobidding
Calculations
Post New
Bids to
Account via
API
Summary
Summary
Maximising visibility from PPC
campaigns depends on:
Granularity
Up-to-date, focussed,
relevant ad copy
Full product & category
coverage
Accurate bidding
Minimising waste means knowing how
to automatically:
Add or update hundreds of new
campaigns
Add or update hundreds of
thousands of ad texts & keywords
Accurately manage bids on
millions of low-volume keywords
Every single day!
Questions?

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PPC Optimisation Beyond Human Capabilities

  • 1. © Search Laboratory Ltd 2014. All rights reserved. Leeds T: +44 113 212 1211 London T: +44 207 147 9980 Maximise Visibility & Minimise Waste: PPC Optimisation Beyond Human Capabilities Stewart Robertson – Technical PPC Manager
  • 2. Agenda Granularity Difficulties of scale – Lots of products – Staying up to date Bidding at scale – Low volume terms – Comparison groups – Smoothing Summary
  • 3. The benefits of a granular approach
  • 4. Granular ad text If you searched for ‘purple scarf’ which ad would YOU click?
  • 5. Granular bidding Which of these users is more valuable to you?
  • 6. Granularity Granularity means: – More specific, better performing ad texts – Bidding the correct amount for every possible keyword – Higher ROI, less waste, better returns
  • 8. Example A company specialising in Red Armani Fedora Hats could end up with ad groups such as these: – Red Armani Fedora Hats – Armani Fedora Hats – Red Fedora Hats – Red Fedoras – Red Hats – Fedoras – Hats – Headwear We also might show ad text such as “Up to 25% off Red Fedora Hats” or “Armani Red Fedoras from £30”, etc.
  • 9. The problem of scale But what do we do if the company also sells: – 10 colours? – 10 brands? – 5 sizes? Plus a similar range of top hats? … And caps too? Very quickly the numbers get too big to deal with
  • 10. How do we solve it? More people or less granularity? Equivalently: – More operating costs or lower return? How about a third option…
  • 13. Item Id Brand Product Classification Colour RRP Price A0001 Armani Headwear > Hats > Fedoras Red £40.00 £30.00 A0002 Nike Headwear > Hats > Fedoras Red £38.00 £10.00 A0003 Haat Headwear > Hats > Fedoras Red £27.00 £27.00 A0004 D&G Headwear > Hats > Fedoras Red £412.00 £299.00 A0005 Prada Headwear > Hats > Fedoras Red £38.00 £30.00 A0006 Kangol Headwear > Hats > Fedoras Red £29.99 £29.99 A0007 Boss Headwear > Hats > Fedoras Red £99.00 £67.00 A0008 Nike Headwear > Hats > Fedoras Red £18.00 £14.00 Process overview Red Armani Fedora Hats Armani Fedora Hats Red Fedora Hats Red Fedoras Red Hats Fedoras Hats Headwear
  • 14. Extract fields from feed •Convert into usable text-strings •Manually build Synonyms •Derive categories from taxonomy or classification Plan targeting item types •Combinations of fields •Multipliers Derive keyword & ad logic •Synonyms •Multipliers •Templates •Thresholds Write automation •Parse feed into targeting items •Generate campaigns, keywords, ads, etc. •Sync with Ad Platform Process overview
  • 15. Process overview – key points Product level alone is not normally enough – Cover higher level categories It’s not enough to have business-specific templates – we need business-specific logic Setup needs a high level of human input Ask the right questions!
  • 17. The bidding problem What is the conversion rate of a keyword with 1 conversion from 7 clicks? Only 95% sure that the Conversion Rate is between 0.36% and 57.87%
  • 18. The bidding problem Determining the conversion rate on broad high- volume terms is easy The problem occurs in low-volume long tailed terms How can we get the most accurate measure of conversion rate? – Look to other similar terms – External data
  • 19. Similar terms Group keywords based on user intent: “Red Armani Fedora” “Blue Armani Fedora” “Green Armani Fedora” “Cheapest Armani Fedora” “Discount Armani Fedora” “Armani Fedora” “Armani Fedora Hat”
  • 20. Comparison groups - Smoothing Pool statistics from the comparison group – But don’t ignore the keyword’s own stats: Keyword 3% 150 clicks Group 1.5% 10,000 clicks 2% 2.5% Keyword 3% 300 clicks Group 1.5% 10,000 clicks
  • 21. Comparison groups - Smoothing So how do we incorporate external data? We believe our keyword out- performs the group by 50% 2.5% Keyword 3% 300 clicks Group 1.5% 10,000 clicks 2.75% Keyword 3% 300 clicks Adjusted Group Rate 2.25%
  • 22. BidLabTM – How it works Custom tree structure based on intention All keywords will use the most relevant data Very tightly-grouped in a natural fashion
  • 25. Summary Maximising visibility from PPC campaigns depends on: Granularity Up-to-date, focussed, relevant ad copy Full product & category coverage Accurate bidding Minimising waste means knowing how to automatically: Add or update hundreds of new campaigns Add or update hundreds of thousands of ad texts & keywords Accurately manage bids on millions of low-volume keywords Every single day!

Notas do Editor

  1. Stress these ads were all live & showing for the term “purple scarf” Highlight – matching what the user searched for. M&S Ad is still less than perfect. Can’t we say something about the purple scarves M&S stock rather than a generic line1 & line 2?
  2. Short v long tail
  3. Stress that all these groups & structure are selling ONE product and possibilities are endless. Size terms, ‘bold hats’, etc.
  4. Highlight that this hypothetical company’s target market is still VERY ‘niche’ but already we could be running to thousands of ad groups Note that lots of volume will exist in crossovers such as “Red hats”, etc. We need to retain the ability to bid on these with ad texts saying “Choose from over x red hats” or “Up to 25% off Red Hats”
  5. This dilemma is faced by PPC account managers all over the world
  6. MINIMAL human intervention on day to day basis Flexibilty to change ad messages, add kw multipliers etc. quickly & easily
  7. Define “Higher-Level” Make case for logic. “Choose from 8 different Red Fedoras” sounds good – “Choose from 8 different tshirts” does not! Lots of people do it at product level but not higher level categories
  8. Introduce the issue first! Need to value a click so need CVR
  9. This is WHY we don’t resell it Lot of manual work goes into optimising this