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Beyond Page Views: Modern Analytics for
Online Marketing
Casey Winters
Online & Interactive Marketing Director
GrubHub
@on...
What are we talking about today?
• The measurement, collection, analysis, and reporting of
internet data for the purposes ...
A little history

• The early web was built on the concept of static pages
• Each page was a chance to show more banner ad...
So what are we optimizing towards
now?
Short-term metrics:
• Leads
• Purchases
• Conversions
• Feature Engagement
• Virali...
Yet, most analytics tools still operate under this
page-based framework and can’t measure any
of those things easily
• Thi...
Anyone remember BranchOut?

6
Anyone remember BranchOut?

7
Anyone remember Viddy?

8
Anyone remember Viddy?

9
Anyone remember Viddy?

10
Anyone remember Viddy?

11
Things you can miss in a page-based
framework
• New vs. repeat conversion rates
• Lack of cross platform analytics e.g. iP...
So, what’s the new model?
• Events instead of Pages
• Individual User Data instead of Aggregate Numbers

Key reports:
• Co...
The new key metrics
• Daily/Weekly/Monthly Active Users
• Average Frequency per Active User
• Conversion rates
– New vs. E...
Out with the old and in with the new
Old guard:
• Adobe SiteCatalyst (Omniture)
• Coremetrics
• WebTrends
• Google Analyti...
A note on attribution

• Many marketers are still using last touch
attribution
• As your marketing methods expand, this be...
Mobile Marketing Attribution
• Normal web funnel:
– Ad click
– Arrive on website
– Website activity

• Mobile funnel:
–
–
...
So what is big data for?
• Big data describes data sets so large that
databases can’t handle them
• This is where things g...
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Beyond Page Views: Modern Analytics for Online Marketing

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Beyond Page Views: Modern Analytics for Online Marketing

  1. 1. Beyond Page Views: Modern Analytics for Online Marketing Casey Winters Online & Interactive Marketing Director GrubHub @onecaseman 1
  2. 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. 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. 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. 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. 6. Anyone remember BranchOut? 6
  7. 7. Anyone remember BranchOut? 7
  8. 8. Anyone remember Viddy? 8
  9. 9. Anyone remember Viddy? 9
  10. 10. Anyone remember Viddy? 10
  11. 11. Anyone remember Viddy? 11
  12. 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. 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. 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. 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. 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. 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. 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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