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Shop.org 2017 Tech talk using external data for campaign optimization c_clearly_netzer

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Tech talk using external data for campaign optimization c_clearly_netzer

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Shop.org 2017 Tech talk using external data for campaign optimization c_clearly_netzer

  1. 1. Using External Data for Campaign Optimization Oren Netzer, Co-Founder and CEO, cClearly, Inc. oren@cclearly.com Booth 1804
  2. 2. Marketers Spend Thousands of Hours and Millions of Dollars Digging for Insights
  3. 3. Mostly Looking Inwards at the Data they Collect from their Ads, Apps, CRM and Website
  4. 4. The Next Frontier of Insights and Optimization Opportunities Exists in Outside Data Sources • Weather • Prices • Competitors • Store Locations • Audience Profiles
  5. 5. The Old Way – 3-9 months, $30K-$90K cost • Prerequisite: data scientist or analyst with some free time • Thesis (8-12 weeks) • Identify potential data sources (2 weeks) • Evaluate and decide on data source (2-4 weeks) • License data (4-8 weeks) • Cleanse and normalize data to fit your internal data hierarchy (2 weeks) • Run statistical models to find correlations and insights (2-6 weeks) • Now, how do we act on it?
  6. 6. The cClearly Way • Prerequisite: data scientist or analyst with some free time not required • Thesis (8-12 weeks) not required • Identify potential data sources (2 weeks) – thousands already built-in • Evaluate and decide on data source (2-4 weeks) • License data (4-8 weeks) • Cleanse and normalize data to fit your internal data hierarchy (2 weeks) • Run statistical models to automatically find correlations and insights • Now, how do we act on it? Actioned on automatically through integrations
  7. 7. Example: Store Location cClearly Algorithms cClearly identified that when the consumer is located less than 5 miles away from competitor store, conversion rate drops by 40% cClearly compiles list of all zip codes that have a competitor store within 5 mile radius cClearly lowers bid for all relevant zip codes across all marketing channels, resulting in a decrease in cost per conversion and an increase in conversion volume Paid Search Ads Display Ads Video Ads Facebook Ads cClearly Database Customer Data
  8. 8. Example: Zip Code based Audience Profiles • Zip codes with higher percentage of college educated, non-smokers convert significantly better People who have a Bachelor’s Degree have a higher ROAS for this Brand. In other words, more educated people provide a better return on ad spend. Graph shows that Smokers have a less favorable ROAS and this Brand should avoid them
  9. 9. Campaign Optimization by Zip Code • Bids applied at zip code level based on audience profiles
  10. 10. 20%-30% Average Performance Lift Oren Netzer, Co-Founder and CEO, cClearly, Inc. Oren@cclearly.com , booth 1804

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