This document discusses how modern analytics have evolved beyond traditional page view-based metrics. It outlines how analytics tools now focus on events rather than pages to better measure outcomes like leads, purchases, conversions and user engagement. Key metrics include daily/weekly active users, conversion rates, and lifetime value. The document recommends switching from tools like Google Analytics to newer options like Mixpanel that can track user-level data and perform cohort analysis. It also addresses challenges in attributing mobile app conversions and how tools are emerging to better track mobile marketing performance.
Beyond Page Views: Modern Analytics for Online Marketing
1. Beyond Page Views: Modern Analytics for
Online Marketing
Casey Winters
Online & Interactive Marketing Director
GrubHub
@onecaseman
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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!
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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
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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
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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
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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
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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
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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
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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)
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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
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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
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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
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Notas do Editor
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.