Driving Consumer Insight With Mobile Analytics
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In 2013, OpenMarketing's Marcia Kadanoff delivered an in-depth presentation at SES New York on mobile marketing focusing on how mobile analytics have changed the way companies measure consumer insight and engagement.
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Driving Consumer Insight With Mobile Analytics
- 1. Driving Consumer Insight with Mobile Analytics Marcia Kadanoff Open Marketing CEO & Founder @openmk New York | March 25–28 #SESNY
- 2. New York| March 25–28, 2013 | #SESNY What We’re Going To Talk About • Types of analytics to look at for mobile versus web • Products available to help you get actionable customer insight • Analyses and testing needed to drive customer insight @openmk
- 3. New York| March 25–28, 2013 | #SESNY Why Mobile Apps? @openmk
- 4. New York| March 25–28, 2013 | #SESNY Differences You Need To Know About WEB ANALYTICS MOBILE APP ANALYTICS Session tracking done primarily thru cookies Session tracking done primarily thru UDID and Javascript (Android) and with sessions (iOS) Human user interface is keyboard and Human user interface is gestural and touch- mouse based based Web measurement model is centered Measurement model is less about referrals around page views, referrals, search, and and search and more about engagement visits and loyalty Unique visitors are tied to individual or Unique visitors are difficult if not impossible server IP addresses to measure; instead we look at sessions @openmk
- 5. New York| March 25–28, 2013 | #SESNY Easily 40 Analytics Products Specific To Mobile @openmk
- 6. New York| March 25–28, 2013 | #SESNY Vanity Metrics • Number of app downloads • Total number of sessions • Total number of first time users DAU – Unique Users @openmk
- 7. New York| March 25–28, 2013 | #SESNY For Mobile Applications • Downloads are not enough • Need to drive 1x usage – an astounding 25% of people download an app and use it 1x only • Need to drive 3x usage – what the industry defines as loyalty @openmk
- 8. New York| March 25–28, 2013 | #SESNY Good Analytics Starts By Asking the Right Questions Acquisition A How do users find you? Activation A Do users have a great first experience? Retention R Do users use it subsequently? Revenue R How do we make money? Refer R Do users tell others? Source: Dave McClure’s AARRR model @openmk
- 9. New York| March 25–28, 2013 | #SESNY Cohort Funnel Trend Acquisition % who download the product by day Activation % of users who % of users who Changes in this activate the product download the behavior over time by date of download product, use it 1x, and fill out a profile Retention % of users who use % of users who use Change in the the app 3x by date the app 1x who go number of loyal of download on to use the app 3x users over time Revenue % of users who use % of users who Change in ARPU over the app 11x time move from step 3 to time and go on to step 4 in the complete in app purchase funnel purchase Referral % of users who leave % of users who post Changes in a review by date of a product review sentiment over time activation after being exposed to 2 photos @openmk
- 10. New York| March 25–28, 2013 | #SESNY Actionable Customer Insight More Free $$$ Less @openmk
- 11. New York| March 25–28, 2013 | #SESNY How We Assess Various Mobile Analytics Products Configurable Trend Funnel Cohort Campaign Drill Down Cost Dashboard Analysis Analysis Analysis Analytics Segments Google ★★ ★★ ★★ ★★ ★★ N/A Free Analytics Flurry ★★ ★★ ★★ ★★ ★★ N/A Free Apsalar ★★ ★★★ ★★★ ★★★ ★★★ ★★★ Free Localytics ★★★ ★★★ ★★★ ★★★ ★★★ ★★★ $95/month For 1 app MixPanel ★★★ ★★★ ★★★ ★★★ Unclear ★★★ Enterprise Model Kontagent ★★★ ★★★ ★★★ ★★★ ★★★ ★★★ Enterprise Model @openmk
- 12. New York| March 25–28, 2013 | #SESNY Flurry Free / Market Leader • Excels at in-application engagement • Custom events What’s Lacking • Meaningful Segmentation • Support for Cohort Analysis • Customer-centered Funnels @openmk
- 13. New York| March 25–28, 2013 | #SESNY TheFind @openmk
- 14. New York| March 25–28, 2013 | #SESNY TheFind • Quite happy with their investment in mobile analytics to date • Stopped spending anything on paid media – virtually all their demand creation is done using SEO and email marketing • They know their business. They know their average number of downloads, and can judge the impact of a particular promotion on downloads and subsequent usage • Cohort analysis particularly painful and challenging @openmk
- 15. New York| March 25–28, 2013 | #SESNY Funnel Analysis @openmk
- 16. New York| March 25–28, 2013 | #SESNY Another View of Engagement – From Localytics @openmk
- 17. New York| March 25–28, 2013 | #SESNY Link Between Engagement and Monetization @openmk
- 18. New York| March 25–28, 2013 | #SESNY Cohort Analysis • The best kind of analysis for decision making • Almost impossible to do without the right tool behind you • One of the big motivators to move up to a mid-market analytics tool @openmk
- 19. New York| March 25–28, 2013 | #SESNY Another View of Cohort Analysis • This time with drill down analysis @openmk
- 20. New York| March 25–28, 2013 | #SESNY Same Cohort – Different Views • Revenue changes from day 1 (baseline) to day 2 @openmk
- 21. New York| March 25–28, 2013 | #SESNY Evaluate Products • Dashboard view • Support for specific analyses you need • Ability to get to go beyond vanity metrics with more emphasis on engagement and ultimately revenue • Referral code for campaign tracking • Integration – how easy or hard it is – particularly with the other data sources that matter to you • Pricing model – FREE generally means free but Enterprise products are priced differently – on purpose • Worry more than a little about the cross device problem @openmk
- 22. New York| March 25–28, 2013 | #SESNY Best Practice: Chose A Product That Includes Campaign Management Functionality Built In @openmk
- 23. New York| March 25–28, 2013 | #SESNY Enterprise Products @openmk
- 24. New York| March 25–28, 2013 | #SESNY Remember on Mobile • Almost all tracking is done with anonymous device fingerprint tracking, which is about 95% accurate – more so for Android, less so for Apple iOS • Apple no longer allows tracking by Device ID (UDID) and is expected to disallow tracking by Mac ID • The leader in cross device tracking & analytics for mobile is a company called Drawbrid.ge – worth checking out • Sources • http://www.mobilemarketer.com/cms/opinion/columns/12380.html • http://media.mobileapptracking.com/docs/MAT-App_to_App_tracking.pdf @openmk
- 25. New York| March 25–28, 2013 | #SESNY Sephora Typical Email Sephora.com Traffic Open Device Over +50% of Sephora 1/3 of all Sephora.com emails are opened on traffic is from mobile mobile or tablet and tablet devices devices Source: Kaleidoscope Kontagent @openmk
- 26. New York| March 25–28, 2013 | #SESNY Impact Analysis @openmk
- 27. New York| March 25–28, 2013 | #SESNY Closing Thoughts • On mobile – because of the app stores - tracking through to the purchase event can be hard • Sometimes you have no choice but to use engagement as a proxy variable • Don’t forget good old fashioned A/B testing particularly of landing pages @openmk
Notas del editor
- The only metrics that entrepreneurs should invest energy in collecting are those that help them make decisions. Unfortunately, the majority of data available in off-the-shelf analytics packages are what I call Vanity Metrics. They might make you feel good, but they don’t offer clear guidance for what to do. Source: Eric Reis, Author of the Lean Start Up, Serial Entrepreneur, and lecturer at HBS.
- There are funnels and there are funnels – this particular funnel is from Apsalar – which does a great job at funnel analysis. Their particular thing is that while the analytics app is free and they do support campaign analytics and refer codes – which you can use to track individual campaigns – their real business is in mobile retargeting. This funnel analysis shows you the well documented link between engagement inside mobile applications and the ability to monetize that application. In fact, here you had to get to level 3 of this trivia game before people were willing to pay for it.
- The first column shows the date at which the “Sign Up” occurred. The “People” column shows how many people signed up on that day (e.g. 10,324 on Feb 5th, 2013) and the percentages represent the percent of people who come back after x-amount of days (where x is 1 to 12 in this chart). So for the Feb 5th cohorts, 1.84% of them came back and consumed more content two days after signing up.In addition and without a lot of digging, you can clearly see that the segment of visitors who signed up on February 8th are super engaged in the first seven days, and they are coming back for more every other day. On the other hand, those who signed up on February 6th, behave completely different. They are interested initially and then their interest taper off.
- Here we grouped users together by the first time they have launched the app, and then calculated the percentage of users went on to make a purchase. For example, in the first row (12/25), there were 34,851 users of which 1.11% made a purchase on Day 1. But as you move to the 2nd and 3rd day, the percentage drops sharply to 0.39% and 0.22%, respectively, before it evens out to roughly 0.10% per day.
- If you want to increase revenue, you can: i) test a personalized incentive program, perhaps on day 3 or 4, or ii) make a change to the app designed to drive more sales beyond the first day. Again, cohort analysis can be used to determine if the incentive program or app change has a positive effect by looking at the cohort groups and their spending after these changes, and comparing them to the prior cohorts.