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PPC Optimisation Beyond Human Capabilities

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Take a look at our Technical PPC Manager, Stewart Robertson's,' slides from his presentation on automating large scale PPC campaigns taken from our recent PPC Best Practice in 2014 and Beyond conference.

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PPC Optimisation Beyond Human Capabilities

  1. 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. 2. Agenda Granularity Difficulties of scale – Lots of products – Staying up to date Bidding at scale – Low volume terms – Comparison groups – Smoothing Summary
  3. 3. The benefits of a granular approach
  4. 4. Granular ad text If you searched for ‘purple scarf’ which ad would YOU click?
  5. 5. Granular bidding Which of these users is more valuable to you?
  6. 6. Granularity Granularity means: – More specific, better performing ad texts – Bidding the correct amount for every possible keyword – Higher ROI, less waste, better returns
  7. 7. The problems of scale
  8. 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. 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. 10. How do we solve it? More people or less granularity? Equivalently: – More operating costs or lower return? How about a third option…
  11. 11. 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
  12. 12. Process overview
  13. 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. 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. 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!
  16. 16. Bidding at scale
  17. 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. 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. 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. 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. 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. 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
  23. 23. BidLabTM Daily process Download Statistics from API Calculate Conversion Rates Perform Autobidding Calculations Post New Bids to Account via API
  24. 24. Summary
  25. 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!
  26. 26. Questions?
  • marcgulinski

    Oct. 19, 2014

Take a look at our Technical PPC Manager, Stewart Robertson's,' slides from his presentation on automating large scale PPC campaigns taken from our recent PPC Best Practice in 2014 and Beyond conference.

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