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PPC Optimisation Beyond Human Capabilities
PPC Optimisation Beyond Human Capabilities
PPC Optimisation Beyond Human Capabilities
PPC Optimisation Beyond Human Capabilities
PPC Optimisation Beyond Human Capabilities
PPC Optimisation Beyond Human Capabilities
PPC Optimisation Beyond Human Capabilities
PPC Optimisation Beyond Human Capabilities
PPC Optimisation Beyond Human Capabilities
PPC Optimisation Beyond Human Capabilities
PPC Optimisation Beyond Human Capabilities
PPC Optimisation Beyond Human Capabilities
PPC Optimisation Beyond Human Capabilities
PPC Optimisation Beyond Human Capabilities
PPC Optimisation Beyond Human Capabilities
PPC Optimisation Beyond Human Capabilities
PPC Optimisation Beyond Human Capabilities
PPC Optimisation Beyond Human Capabilities
PPC Optimisation Beyond Human Capabilities
PPC Optimisation Beyond Human Capabilities
PPC Optimisation Beyond Human Capabilities
PPC Optimisation Beyond Human Capabilities
PPC Optimisation Beyond Human Capabilities
PPC Optimisation Beyond Human Capabilities
PPC Optimisation Beyond Human Capabilities
PPC Optimisation Beyond Human Capabilities
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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 …

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.

Published in: Marketing, Lifestyle, Technology
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  • 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?
  • Short v long tail
  • Stress that all these groups & structure are selling ONE product and possibilities are endless. Size terms, ‘bold hats’, etc.
  • 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”
  • This dilemma is faced by PPC account managers all over the world
  • MINIMAL human intervention on day to day basis

    Flexibilty to change ad messages, add kw multipliers etc. quickly & easily
  • 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
  • Introduce the issue first!

    Need to value a click
    so need CVR
  • This is WHY we don’t resell it
    Lot of manual work goes into optimising this
  • Transcript

    • 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
    • 7. The problems of scale
    • 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…
    • 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. Process overview
    • 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!
    • 16. Bidding at scale
    • 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
    • 23. BidLabTM Daily process Download Statistics from API Calculate Conversion Rates Perform Autobidding Calculations Post New Bids to Account via API
    • 24. Summary
    • 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. Questions?

    ×