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Beyond Page Views: Modern Analytics for Online Marketing

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  • BranchOut was a professional network built on top of Facebook. They raised $25 mil after hitting $25 million users in year. One year later, they are a chat app with 35 ratings on the App Store. They optimized for the wrong thing. Total users don’t matter. None of their users came back.Viddy was in a fierce battle to become the Instagram for video with Socialcam. While Socialcam sold, Viddy went for it, raising $30 mil at a $370 million valuation. It was the top free app on the App Store. Does anyone know how Apple determined rankings back then? It was logarhythmic model of downloads in the past seven days. Now, things got real nasty for Viddy. Their CEO got fired, they lost a third of their staff, and they had to return much of the money they raised to investors. Both of these companies optimized toward the wrong thing. I’m not saying analytics was at fault here, but this is what can happen when you make decisions based on the wrong data.
  • BranchOut was a professional network built on top of Facebook. They raised $25 mil after hitting $25 million users in year. One year later, they are a chat app with 35 ratings on the App Store. They optimized for the wrong thing. Total users don’t matter. None of their users came back.Viddy was in a fierce battle to become the Instagram for video with Socialcam. While Socialcam sold, Viddy went for it, raising $30 mil at a $370 million valuation. It was the top free app on the App Store. Does anyone know how Apple determined rankings back then? It was logarhythmic model of downloads in the past seven days. Now, things got real nasty for Viddy. Their CEO got fired, they lost a third of their staff, and they had to return much of the money they raised to investors. Both of these companies optimized toward the wrong thing. I’m not saying analytics was at fault here, but this is what can happen when you make decisions based on the wrong data.
  • BranchOut was a professional network built on top of Facebook. They raised $25 mil after hitting $25 million users in year. One year later, they are a chat app with 35 ratings on the App Store. They optimized for the wrong thing. Total users don’t matter. None of their users came back.Viddy was in a fierce battle to become the Instagram for video with Socialcam. While Socialcam sold, Viddy went for it, raising $30 mil at a $370 million valuation. It was the top free app on the App Store. Does anyone know how Apple determined rankings back then? It was logarhythmic model of downloads in the past seven days. Now, things got real nasty for Viddy. Their CEO got fired, they lost a third of their staff, and they had to return much of the money they raised to investors. Both of these companies optimized toward the wrong thing. I’m not saying analytics was at fault here, but this is what can happen when you make decisions based on the wrong data.
  • BranchOut was a professional network built on top of Facebook. They raised $25 mil after hitting $25 million users in year. One year later, they are a chat app with 35 ratings on the App Store. They optimized for the wrong thing. Total users don’t matter. None of their users came back.Viddy was in a fierce battle to become the Instagram for video with Socialcam. While Socialcam sold, Viddy went for it, raising $30 mil at a $370 million valuation. It was the top free app on the App Store. Does anyone know how Apple determined rankings back then? It was logarhythmic model of downloads in the past seven days. Now, things got real nasty for Viddy. Their CEO got fired, they lost a third of their staff, and they had to return much of the money they raised to investors. Both of these companies optimized toward the wrong thing. I’m not saying analytics was at fault here, but this is what can happen when you make decisions based on the wrong data.
  • BranchOut was a professional network built on top of Facebook. They raised $25 mil after hitting $25 million users in year. One year later, they are a chat app with 35 ratings on the App Store. They optimized for the wrong thing. Total users don’t matter. None of their users came back.Viddy was in a fierce battle to become the Instagram for video with Socialcam. While Socialcam sold, Viddy went for it, raising $30 mil at a $370 million valuation. It was the top free app on the App Store. Does anyone know how Apple determined rankings back then? It was logarhythmic model of downloads in the past seven days. Now, things got real nasty for Viddy. Their CEO got fired, they lost a third of their staff, and they had to return much of the money they raised to investors. Both of these companies optimized toward the wrong thing. I’m not saying analytics was at fault here, but this is what can happen when you make decisions based on the wrong data.
  • BranchOut was a professional network built on top of Facebook. They raised $25 mil after hitting $25 million users in year. One year later, they are a chat app with 35 ratings on the App Store. They optimized for the wrong thing. Total users don’t matter. None of their users came back.Viddy was in a fierce battle to become the Instagram for video with Socialcam. While Socialcam sold, Viddy went for it, raising $30 mil at a $370 million valuation. It was the top free app on the App Store. Does anyone know how Apple determined rankings back then? It was logarhythmic model of downloads in the past seven days. Now, things got real nasty for Viddy. Their CEO got fired, they lost a third of their staff, and they had to return much of the money they raised to investors. Both of these companies optimized toward the wrong thing. I’m not saying analytics was at fault here, but this is what can happen when you make decisions based on the wrong data.
  • What happens if you don’t use events and user data? For events, you start trying to triangulate if someone reached a page implying they did an event, and the page framework is deteriorating across the internet/mobile.For users, you’ll get multiple marketing channels claiming credit for user growth, and if you add it all up, it’s double the total new users you got to the service.
  • Transcript

    • 1. Beyond Page Views: Modern Analytics for Online Marketing Casey Winters Online & Interactive Marketing Director GrubHub @onecaseman 1
    • 2. What are we talking about today? • The measurement, collection, analysis, and reporting of internet data for the purposes of optimizing web usage. • EASIER SAID THAN DONE! 2
    • 3. A little history • The early web was built on the concept of static pages • Each page was a chance to show more banner ads and increase advertiser impressions • So, websites optimized revenue by driving as many page views as possible • It doesn’t work that way anymore • Websites have many different business models that require different optimization methods • Many websites, especially apps, lack a page-based hierarchy thanks to new programming languages and browser capabilities 3
    • 4. So what are we optimizing towards now? Short-term metrics: • Leads • Purchases • Conversions • Feature Engagement • Virality Long-term metrics • Lifetime Value • Days active • Attrition 4
    • 5. Yet, most analytics tools still operate under this page-based framework and can’t measure any of those things easily • This creates a culture of focus on “vanity metrics” that don’t drive insights or optimization: – # Uniques – # Visits – Page views/visitor – # Downloads • For years, marketers and engineers have been tracking to hack these tools to track what they actually care about • Marketers were forced to learn SQL to retrieve the data they actually needed to do measure their performance 5
    • 6. Anyone remember BranchOut? 6
    • 7. Anyone remember BranchOut? 7
    • 8. Anyone remember Viddy? 8
    • 9. Anyone remember Viddy? 9
    • 10. Anyone remember Viddy? 10
    • 11. Anyone remember Viddy? 11
    • 12. Things you can miss in a page-based framework • New vs. repeat conversion rates • Lack of cross platform analytics e.g. iPhone app vs. website performance • Cross-platform usage • # of site uses before conversion occurs • Understanding lifetime value or retention • Mobile attribution • Understanding how online and offline ad impressions drive value 12
    • 13. So, what’s the new model? • Events instead of Pages • Individual User Data instead of Aggregate Numbers Key reports: • Cohort analysis • Conversion funnel • Attribution models 13
    • 14. The new key metrics • Daily/Weekly/Monthly Active Users • Average Frequency per Active User • Conversion rates – New vs. Existing – By marketing channel – By platform • Cost per Acquisition – By marketing channel • Lifetime Value – By marketing channel – By platform 14
    • 15. Out with the old and in with the new Old guard: • Adobe SiteCatalyst (Omniture) • Coremetrics • WebTrends • Google Analytics New breed: • Mixpanel • Kiss Metrics • RJ Metrics • Kontagent (mobile apps only) • Localytics (mobile apps only) 15
    • 16. A note on attribution • Many marketers are still using last touch attribution • As your marketing methods expand, this becomes less and less accurate • New tools look at correlations of patterns of marketing exposures (online impressions, search clicks, offline exposure) and model to create fractional attribution models using statistical inference – – – – Convertro Adometry Clearsaleing Visual IQ 16
    • 17. Mobile Marketing Attribution • Normal web funnel: – Ad click – Arrive on website – Website activity • Mobile funnel: – – – – – Ad click Arrive at App Store (untrackable for iOS) Download app (unattributable for iOS) Open app App usage • New solutions: – – – – Mobile App Tracking by Has Offers AppsFlyer Kontagent Mobile ad vendors e.g. Apsalar 17
    • 18. So what is big data for? • Big data describes data sets so large that databases can’t handle them • This is where things get beyond marketers’ ability to segment data and need technical help • Used for a lot of statistical correlation inside of massive amounts of related data • Tools: – Hadoop – MapReduce – Hive 18

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