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Heroconf London 2018_Automating Search Query Processing

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This talk covers several actionable tactics to tackle the challenge of automating search query processing or parts of the process.

Veröffentlicht in: Marketing
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Heroconf London 2018_Automating Search Query Processing

  1. 1. Automating Search Query Processing From Simple Tactics to Machine Learning Approaches Philipp Mainka & Christopher Gutknecht
  2. 2. Philipp Mainka Head of SEA @ Sixt 9 years in PPC 13+ Awarded Team Focus on Travel Munich-based Performance Marketing Data Nerd Christopher Gutknecht Head of Online Marketing @ norisk 10 years in PPC SEMY Award Jury Member Focus Ecommerce & Retail Munich-based Dad of 2,5 yr old Self Taught Rookie-Dev Two Nerds - Same Problem
  3. 3. Your Learning Outcome of This Talk 1 2 3 Understanding the challenges of query automation How to get started with little helper tools Being able to make an informed make-or-buy decision
  4. 4. Current Trend: Managed by Google vs Control? Automated Extension Smart Bidding Responsive Search Ads Dynamic Search Ads Suggested Ads Variant 'Exact' Universal App Campaigns ...
  5. 5. It’s Not About This Happening… YOU
  6. 6. It’s About Your Role in the Future of PPC HEAD CHEF WAITEROR
  7. 7. Time Campaign Mgt Bid Mgt Reporting & BI The Most Time-Consuming Tasks in PPC
  8. 8. Source: youtu.be/TcYZi9rEvgo The Search Query Workflow - Simple, Right?
  9. 9. The Search Query Reality: Entities Everywhere! COLOR GENDER PLUS SIZE SIZES CATEGORY CUSTOM MATERIAL Entities define the place in the account structure
  10. 10. Our Two Major Battlefields of PPC RETAIL + More Query sources + More Internal Structure + More Shopping & DSA TRAVEL + More Entity Combinations + More Entity Hierarchies + External APIs needed (Geo)
  11. 11. Matchtpye s New Keywords Entities recognition Bring into structure Find URL Automatic New Query Processing: Step-by-Step Write AdsCalculate Bids Matchtype & Settings
  12. 12. Use your entities Ask Google Starting with ML Supervised ML Entity Recognition: Start Simple, then Expand
  13. 13. ● Text normalization: lowercase, ascii, word sort, add nospace ● Partial matches: Partial, fuzzy, wordstem, typo, synonym ● False matches: withinString ● Cross-entity match: 'dress' = title or category? Product Feed Entities String Comparison Benchmark 80%
  14. 14. Entity Recognition in Retail: Step-by-Step FullMatch PartialMatch ● MaxMatch ○ Match 1 ○ Match 2 1. Brand? stripFromQuery 'jersey stretch plus size tops' 2. Category? ... n. Custom plusSize: plus size, oversize discount: sale, clearance year: 2018, 2019 … : ResultObject isTrue FuzzyMatch Example Script: bit.ly/hero_sqa
  15. 15. Entity Detection: Example Output (Video)
  16. 16. Use your entities Ask Google Starting with ML Supervised ML Start Simple and Expand: Ask Google
  17. 17. cloud.google.com/natural-language/ #AskGoogle 1: Try Google's Entity Recognition Query Cloud Natural Language API Get Entities
  18. 18. ● Example Call: here #AskGoogle 2: Validate Geo Entities URL Fetch Parse Results Validate Geo Entities ● Example Script: https://goo.gl/FLdtK4
  19. 19. ● Example Call: bit.ly/hero_sqa ● Example Script: bit.ly/hero_sqa Call Suggest API Get Edit Distance #AskGoogle 3: Typo Recognition
  20. 20. ● Example Script: bit.ly/hero_sqa #AskGoogle 4: Validate Synonyms Query Scrape Related Searches Compute Intersection
  21. 21. site: Query Scrape URLs ● If Onsite-Search not an option ● Free Tool: oneproseo.com/landingpagefinder/ (Limit: 100 req/day) ● Example script with Scraping API: bit.ly/hero_sqa #AskGoogle 5: Find a Target URL Semantic Validation
  22. 22. Use your entities Ask Google Starting with ML Supervised ML Start Simple and Expand: Enter Machine Learning
  23. 23. Why Python Is Amazing: Example
  24. 24. fuzzyWuzzy String Similarity Language Model Context Modeling Python NLP Packages to go Beyond 80% Accuracy
  25. 25. How Python Can Be Triggered From Ads Scripts SCRIPTS CLOUD FUNCTIONS ... ... ● Example Script: bit.ly/hero_sqa
  26. 26. Use your entities Ask Google Starting with ML Supervised ML Start Simple and Expand: Supervised ML
  27. 27. Rule based analysis: rent a van in miami Why Context Matters: Rules often don’t work VEHICLE_TYPE: van LOCATION: van
  28. 28. With machine Learning: rent a van in miami Why Context Matters: Resolve Ambiguity VEHICLE_TYPE: van LOCATION: van
  29. 29. Handling Ambiguous Entities Group into Sub-Entities Prioritisation & Deduplication
  30. 30. Clean Training Data I think it is a VEHICLE_TYPE bristol car & van hire Data Validation - The App Way!
  31. 31. Improving Model Accuracy Beyond 90% Accuracy for 50k processed queries80% 85% 90% + Stemming + Lemmatization + Variants + Ambiguity + Supervised + Language Model
  32. 32. How to Get Started with Query Automation Be the Head Chef: Start Experimenting!1 2 3 Start Simple and Expand Customise and Scale
  33. 33. Query Expansion Entity Exploration Value Prediction The Road Ahead: Automating Search Queries
  34. 34. {result: { 'First Name': 'Philipp', 'Last Name': 'Mainka', '@type: [ 'Person', 'Google Specialist' ], Employer: 'Sixt', Position: 'Head of SEA', Contact: 'linkedin.com/in/philipp- mainka-12b7b543/'} THANK YOU - Your Nerd Questions Please. {result: { 'First Name': 'Christopher', 'Last Name': 'Gutknecht', @type: [ 'Person', 'Google Specialist' ], Employer: 'norisk', Position: 'Head of OM', Contact: 'linkedin.com/in/chrisgutknecht/'}