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Using Big Data for Advertiser Insights

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Slides from Mike McMeekin's section of the Big Data + Big Math = Exponential Search Performance session at SMX Advanced 2013.

Slides from Mike McMeekin's section of the Big Data + Big Math = Exponential Search Performance session at SMX Advanced 2013.

Published in: Business, Technology

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  • 1. Using Big Data for Advertiser InsightsMike McMeekin, Director - Bing Ads Advertiser Insights and Analytics
  • 2. AGENDA• Introduction• Advertiser Forecasting• Ad Copy Quality• Advertiser Diagnostic
  • 3. Advertiser Insights & Analytics Team• Team of analysts, scientists, business folks• Use Yahoo! Bing Network’s vast data,combined with value-added Big Dataanalytical capabilities, to create new,helpful Advertiser insights• And build requirements for platformbased solutions• Drive transparency on AdvertiserOpportunities relative to our competition• Advertiser Forecasting• Ad Copy Quality testing• Advertiser Diagnostics• Impression SOV• Vertical click SOV• Keyword Correlations• New Ad Format Analytics• Keyword Attribution• Vertical and Competitive Insights• Mobile, Tablet Insights• Broad Match Prediction Tool• Site Links Recommendation Tool
  • 4. LONG TERMFORCASTING
  • 5. Advertiser ForecastingHelp advertisers plan in the medium to long term(3 - 18 months).Analyze and predict search query and impression volume, clickvolume, and cost for upwards of millions of bidded keywordsand billions of queriesApply scientific methods aiming to narrow down the expectedrange of the forecast.>90% Accuracy out to 12 months
  • 6. The Methodology: A Visualization
  • 7. IMPROVING ADCOPY TESTINGAND QUALITY
  • 8. Identifying Effective Ad Creative VariablesAlgorithmically group keywords into related “micro-markets”,evaluate all ads associated with those keywords for ad copyvariables that drive significantly higher Ad QualityControl for ad position, user signals, marketplace algo changes,term type (e.g. brand vs non-brand), advertiser domain, etc toreduce noise around ad performanceProvide specific ad testing recommendations by Vertical, Sub-Vertical, list of KW’s
  • 9. Automated Ad Copy Quality – ExamplesIn Both In Ad Title In Ad Description∆ pClick – Variables vs NoVariablespClick - No Variable110%increa75%increase44%increaseSub-Vertical Ad Title Ad Description Ad Quality Lift RankLodging - - Rates Fares 1Lodging - - Rates Destination 2Lodging $ or % Off - Saving 3
  • 10. KEYWORDCORRELATIONCOEFFICIENTS –IDENTIFYING NEWADVERTISEROPPORTUNITIES
  • 11. Finding Queries With Similar User IntentUse known keywords (e.g. “Father’s Day”) and compare to allother (i.e. billions) repeated queriesEvaluate trends in query volume, click volume, ad copyvariables, and other signals to identify similarities betweenindividual or segments of user queriesIntroduces opportunities to grab user Demand from othercategories of KW’s – i.e. a user who would have spent $400 oncamping gear may be willing to instead spend that on an iPadduring the Father’s Day season
  • 12. Valentine’s Day CorrelationCoefficient AnalysisGenerated a set of 1300 V-day related queriesMethodology:• Assume V-Day relevant querieswould have a similar trend.• Use known V-day query as“seed” and compared everyquery between 2/1 – 2/20/2012against the trend of “seed”• Correlation coefficient is used tomeasure the “similarity” of thetrend. (Metric is irrelevant ofvolume, only focusing on trend.)
  • 13. Trend comparison:Included all queries with coefficient > 0.7Valentine’s Day CorrelationCoefficient Analysis
  • 14. Thank YouMike McMeekinDirector, Advertiser Analytics and Insights@TheMikeMcMeekin