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What the Quantified Self Movement Teaches Us About Analytics, Marketing and Privacy
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What the Quantified Self Movement Teaches Us About Analytics, Marketing and Privacy

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At the intersection of fitness, analytics and social media, a new trend of “self-quantification” is emerging. Devices and Applications like Jawbone UP, Fitbit, Nike Fuel Band, Runkeeper and even …

At the intersection of fitness, analytics and social media, a new trend of “self-quantification” is emerging. Devices and Applications like Jawbone UP, Fitbit, Nike Fuel Band, Runkeeper and even Foursquare are making it possible for individuals to collect tremendous detail about their lives: every step, every venue visited, every bite, every snooze. What was niche, or reserved for professional athletes or the medically-monitored, has become mainstream, and is creating a wealth of incredibly personal data.

This presentation discusses what this emerging trend teaching us about the practice of analytics, the marketing opportunities that arise and the implications for privacy around this very, very personal data.

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  • So before we begin, a little about us. Web Analytics Demystified is the global leader in digital analytics consulting. When I say that we “wrote the book on digital analytics”, that’s not hyperbole. We literally did.
    Web Analytics Demystified and its Senior Partners co-founded Web Analytics Wednesday and the Analysis Exchange (which teaches beginners how to do web analytics, while helping non-profits), penned the Web Analyst “Code of Ethics”, and our senior partner John Lovett is President of the Digital Analytics Association.
    My focus at Web Analytics Demystified is our analysis and analyst mentoring practice. This means I not only help our clients with hands-on analysis, but I also work with them on how to succeed at analysis as an organisation, from building the right team to providing the right education and training to growing their people and their practice.
  • A little about me -
    I was originally born in Australia, not that you can tell any more. (The accent has been fairly successfully beaten out of me.)
    I am a group fitness instructor for my little “side job” - I teach Les Mills BodyPump, RPM and BodyCombat.
    I also tweet way too much stuff about kittens, which is no surprise to those who I’m connected with on Twitter!
  • So what is “self quantification”? This term really sprung up out of nowhere of late, and simply put, refers to any kind of tracking or quantification of your life.
    The kinds of things people measure range from basics, like workouts or food intake, to the more detailed (and often, medically-related) like glucose levels and light exposure.
  • Here’s a little about what *I* measure -
    Steps, exercise/activities, heart rate, weight, sleep, running and location
  • Here is a glimpse of my “quantified life” -
    I use Jawbone UP to track my every-day movements like how many steps I take
    I also use UP to track my sleep - how much sleep I get, light vs. deep sleep, how long it takes me to fall asleep, how often I wake up, etc
    I use IFTTT (If This Then That) to push any Foursquare checkins to gyms to Jawbone UP
    I use Runkeeper to track my runs
    I use Withings wifi scale to track weight and resting heart rate
  • I have been doing all of this since about April. So you’re probably wondering -
    What have I learned? (aka, why go to all this trouble?)
    And what does this have to do with Analytics?
  • This is as true with “self analytics” as it is with “business analytics”! All the data in the world is useless if you don’t actually make any changes as a result.
    As an example, a friend of mine (who shall remain nameless) was also a Jawbone UP user. However, not too long ago he stopped using it, basically saying, “I don’t need it to tell me that I don’t sleep enough and am lazy.” If you don’t change something in response to the data, it’s useless.
  • By contrast -
    This is my total sleep time and steps taken trended against each other, and a raw data output from my Jawbone UP tracking.
    Essentially, it’s a whole bunch of data. Well, SO WHAT?
  • So how can I use this data to actually make a decision?
    In the example above:
    Chart at the top is my original sleep patterns - alarm set to go off JUST as I was typically going back in to a deep phase of sleep. Meant I woke up groggy and couldn’t get out of bed.
    So, I changed my alarm time to go off after my last deep sleep phase, and before my next phase typically started.
    I got less sleep but felt a TON better, because I wasn’t being woken from a groggy phase of sleep.
  • We all know - the best way to make someone care about analytics is to tie their bonus to meeting those numbers! There’s a reason sales guys have quotas they need to hit.
  • In the quantified self world, data prevents you from misleading yourself.
  • This is going to sound odd coming from an analytics professional, but making good, data-informed decisions doesn’t actually require perfect data.
    Certainly, misleading data isn’t what we’re looking for! However, even in the digital analytics world, we understand that while we try to ensure quality data, it’s not 100% accurate (and never will be.)
  • Quantified Self data is no different.
    Steps are used as a proxy for activity, but there are many activities that steps can’t measure (for example, cycling!) or the devices can’t deal with (swimming, paddle boarding.)
    Does that mean it’s all useless?
  • So does that mean it’s all useless?
    No. It’s about the TREND.
    Am I more active today than yesterday? Than last week? Last month?
    It’s also why we look at other metrics, for a more comprehensive understanding. Yes, “steps” is the primary measure, however I also track “active time”, which DOES include manually input exercise like cycling.
  • Learning 4: Numbers without context are useless.
  • This is a perfect example:
    (build)
    8,943 - so I have a number. Great. What does that mean? is it “good”? Alone, I don’t know. It’s just a number, with no context to understand it.
    Well, what if I knew that my goal was 10,000?
    Therefore I know that I’m at 89% of my goal, AND I have an action item - move more!!
    In quantified self data, and in digital analytics, context is critical.
  • The digital world is a mash-up of experiences, from desktop to tablet to social and around again. Just like in the world of digital analytics, ONE quantified self data source will never give you the complete picture!
  • So here’s a little look at my “quantified self” ecosystem.
    I use Runkeeper to track running. Distance, speed and more.
    I use Withings to monitor weight and resting heart rate.
    I have Foursquare configured, via IFTTT (If This Then That) to push any gym or fitness checkins to Jawbone UP
    Jawbone UP’s UI is my central integration point, where all of this data comes together
    I also use IFTTT to push data from Jawbone in to Google Spreadsheets.
    Integrating data across all these tracking systems gives me a clearer “big picture” of how I’m doing
  • But while it’s great to have an integrated system, and one place to view all my data, there’s still a lot of value in having specialized data sources.
  • For example, Runkeeper houses VERY detailed data about one specific type of activity (running.)
    Whereas Jawbone UP’s UI provides a more summarized view of all my activity and how they interplay - sleep with daily movement with exercise. It’s my central integration point.
  • So now that I’ve talked way too much about myself, you’re probably wondering… “Who cares?”
    Why is this niche trend worthy of this much analysis?
  • First of all, what was a niche trend is starting to become mainstream
    The new iPhone 5S has an entire co-processor devoted to motion data that will significantly improve the capability of fitness, movement and quantified self apps
  • In 2012, there were 8MM fitness trackers, smart watches and smart glasses sold.
    That is expected to grow to 64 million within four years! This is a huge, exploding market.
    http://medcitynews.com/2013/10/2017-report-says-wearable-sales-will-jump-64m-units/#ixzz2i0aii7uB
  • Second, this isn’t just about health and fitness apps - it’s part of a general trend of consumers using data to make decisions
    Credit scores, sites like True Car and Kelley Blue Book (that crunch mass amounts of data to provide consumers with real-world buying information) and even consumer reviews are all examples of consumers using data.
    The reason this matters is that consumer’s personal use of data can help them become comfortable with businesses using their data - if done right.
  • However, we need to tread carefully.
    There is a delicate balance with the marketing opportunities that self-tracking provides, vs. the privacy considerations we must heed.
  • This wealth of data presents amazing marketing, targeting and personalisation opportunities
    For example, you’re supposed to replace your running shoes every 300-400 miles. Brands have an awesome opportunity to track your mileage and present “right time” messaging to encourage your next purchase. And that’s just the tip of the iceberg!
  • But, we need to be so careful here!
    Consumers are concerned enough about internet privacy.
    This is so much more than that - it’s VERY personal data - where I go, what I ate, what I weigh, even my genetic makeup
    There is a lot of potential for this to go very wrong. (Really, how much do you think it will endear consumers to get Weight Watchers or Jennny Craig ads in response to their latest weight reading…)
  • So what cautions do marketers need to heed with respect to this kind of data?
  • 1. This is not a “better to ask for forgiveness than permission”.
    Marketers are sometimes a little quick to jump on something without giving it the proper caution.
    This CAN’T take that approach. This data is too personal.
    Which means we need to… [next slide]
  • We must get it right the first time.
    If we blow it the first time, we’ll never get the opportunity back to fix it.
    The personal nature of this data means any breach of trust is going to feel so invasive, worse than typical “creepy” internet targeting
  • Getting it right the first time means we have to actually plan this out!
    We need to figure out what data we need
    We need to get feedback on what we’re doing, from actual consumers - a small beta to get feedback first is a great way to start
    Simply put, we need to execute flawlessly the first time. This is easier said than done!
  • The personal nature of this data means we won’t simply be able to use it and then allow “offended” consumers to opt out
    This will should be managed as an opt in
  • We can’t let this be creepy like so much marketing and targeting today.
    For any messaging, a consumer must understand why they’re seeing this message - what behaviour or data triggered this?
    They should have the ability to give feedback, both on an individual message basis, or as a whole.
    For example, Jawbone UP provides fun little daily “did you knows”, and every one of these messages provides the ability to give feedback that the message is inappropriate.
    And we can’t require a PhD in privacy settings!!
  • That means, none of this. In case you’re curious, this is Facebook’s current privacy policy, and it is almost ten thousand words long.
  • We need to privacy controls incredibly customisable and granular, to allow not only control over the sharing of specific types of data, but also with specific individuals or groups
    For example, I might be willing to share the results of my genetic testing with my spouse and my doctor, but I’m not sharing it with my insurance or publishing it to Facebook!
    I might be willing to share my activity information (steps, exercise) with my social network and businesses, but not my weight. (C’mon, I won’t even share that with the DMV…)
  • What this comes down to is that privacy is not “private” or “share” anymore!! This isn’t a switch users flip either on or off.
    [deviantart.com]
  • It’s a spectrum, from private only to me, to completely public
  • And there is an individual spectrum for every type of data I’m using!
    I need to have this kind of control over this data. Without it, I’m just going to default to “Nope” when it comes to giving marketers access to my data.
  • Not only do we need to really plan out what kind of data we need, we can’t become greedy data hoarders!
  • This is already being legislated!
    Companies can’t over-collect data (“in case we ever think of something to use it for”)
    Australia’s most recent privacy legislation is making it clear that data can only be used for it’s intended purpose, and users must have consented to that use of the data. It’s not okay to collect data and use it for a purpose other than the one consented to, and it’s not okay to collect data that you don’t need.
  • In the end, successful marketing and privacy treatment for quantified self data requires you to provide value to the customer, so they want you to message them on the basis of their data.
    It comes down to the idea of “marketing as a service” - so useful, you would pay for it.
    We need to provide value to the customer - more value to the customer than even to the business
    That’s how this will be successful.
  • Questions?
  • Transcript

    • 1. WHAT THE “QUANTIFIED SELF” MOVEMENT TEACHES US ABOUT ANALYTICS ABOUT MARKETING ABOUT PERSONALISATION ABOUT TARGETING ABOUT TECHNOLOGY ABOUT SOCIAL MEDIA ABOUT PRIVACY MICHELE KISS Partner, Web Analytics Demystified michele@webanalyticsdemystified.com @ michelejkiss
    • 2. WEB ANALYTICS DEMYSTIFIED Global leader in digital analytics consulting We literally wrote the book(s) on digital analytics! @michelejkiss
    • 3. @michelejkiss
    • 4. WHAT IS “SELF QUANTIFICATION”? WHAT DO PEOPLE MEASURE? STEPS ACTIVITIES BLOOD PRESSURE RESTING HEART RATE WEIGHT GLUCOSE SLEEP & DRINK LEVELS FOOD RUNNING LOCATION LIGHT EXPOSURE @michelejkiss
    • 5. WHAT DO I MEASURE? STEPS ACTIVITIES BLOOD PRESSURE RESTING HEART RATE WEIGHT GLUCOSE SLEEP & DRINK LEVELS FOOD RUNNING LOCATION LIGHT EXPOSURE @michelejkiss
    • 6. @michelejkiss
    • 7. WHAT HAVE I LEARNED? (And what does this have to do with analytics?) @michelejkiss
    • 8. 1. DATA IS ONLY VALUABLE IF YOU USE IT TO MAKE BETTER DECISIONS @michelejkiss
    • 9. SO WHAT? @michelejkiss
    • 10. @michelejkiss
    • 11. 2. MEASUREMENT HOLDS YOU ACCOUNTABLE @michelejkiss
    • 12. “I went to the mall. That’s, like, totally cardio.” Distance 6.08mi Pace 9:37min/mile Calories burned 657 @michelejkiss
    • 13. 3. GOOD DECISIONS DON ’T REQUIRE PERFECT DATA @michelejkiss
    • 14. how many “steps” did I take on my bike?! “steps” ≈ proxy for all activity @michelejkiss
    • 15. DOES THAT MEAN IT’S ALL USELESS? NO! We care about the trend. We care about more than just one data point @michelejkiss
    • 16. 4. NUMBERS WITHOUT CONTEXT ARE USELESS @michelejkiss
    • 17. 8,943 10,000 89% ... move more @michelejkiss
    • 18. 5. IT’S NOT ABOUT ONE DATA SET... IT’S ABOUT THE WHOLE PICTURE @michelejkiss
    • 19. MY ECOSYSTEM... @michelejkiss
    • 20. 6. BUT USE THE RIGHT TOOL FOR THE JOB! @michelejkiss
    • 21. • Detailed data about one specific activity • • Summarised view of all activities Central point of integrated data
    • 22. WHO CARES?
    • 23. WHO CARES? 1. Niche Mainstream @michelejkiss
    • 24. 8.3 million fitness trackers, smart watches, and smart glasses sold in 2012 Expected to grow to 64 million within four years! @michelejkiss
    • 25. They even make them for dogs…!! @michelejkiss
    • 26. WHO CARES? 2. Part of a general trend of consumer use of data Consumer’s personal use of data can help them become comfortable with business data use @michelejkiss
    • 27. NEED CAREFUL BALANCE Marketing opportunity Privacy considerations @michelejkiss
    • 28. @michelejkiss
    • 29. Very personal data @michelejkiss
    • 30. WHAT CAUTIONS MUST WE HEED? @michelejkiss
    • 31. 1. CAN’T BE “DO FIRST, ASK PERMISSION LATER” @michelejkiss
    • 32. 2. GET IT RIGHT THE FIRST TIME If we blow it the first time, we will never get another chance @michelejkiss
    • 33. REQUIRES ... • • • • • Planning Defining data needs Consumer feedback Betas Flawless execution @michelejkiss
    • 34. 3. MUST HAVE CONSENT • Opt-in, not opt-out @michelejkiss
    • 35. 4. CRYSTAL-CLEAR TRANSPARENCY [Don’t be creepy!] • What did I do to trigger this message? • Easy, comprehensive feedback options - On an individual message - As a whole • Don’t require a PhD in Privacy Settings ... @michelejkiss
    • 36. THAT MEANS ... Speak clear, plain English! Not this (9,176 words) @michelejkiss
    • 37. 5. CUSTOMISABLE PRIVACY Specific data sources • My weight or genetic test results vs. ‘active time’! With who • Family vs. doctor vs. friends vs. businesses @michelejkiss
    • 38. PRIVACY IS NOT AN ON / OFF @michelejkiss
    • 39. IT’S A SPECTRUM Totally private Totally public @michelejkiss
    • 40. Only me Doctor, Spouse Close family & friends All friends Companies Public Weight Exercise Location @michelejkiss
    • 41. 6. DON’T GET GREEDY @michelejkiss
    • 42. • Don’t over collect • Use only for intended purpose! Privacy Amendment (Enhancing Privacy Protection) Act 2012 6 Australian Privacy Principle 6—use or disclosure of personal information Use or disclosure 6.1 If an APP entity holds personal information about an individual that was collected for a particular purpose (the primary purpose), the entity must not use or disclose the information for another purpose (the secondary purpose) unless: (a) the individual has consented to the use or disclosure of the information @michelejkiss
    • 43. CUSTOMER VALUE > BUSINESS VALUE @michelejkiss
    • 44. MICHELE KISS Partner, Web Analytics Demystified michele@webanalyticsdemystified.com @ michelejkiss

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