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The Reinvention of Your Analytics Skills!

Last week, myself and 7,000+ of my friends attended Adobe’s Summit 2014 in Salt Lake City. The overarching theme of the event was “the reinvention of marketing”, which got me thinking about how digital analytics professionals can continue to reinvent themselves and their skills.

Digital analytics is a rapidly evolving field, progressing swiftly from log files, to basic page tagging, to cross-device tracking. The “web analysts” of just a few years ago have progressed from pulling basic reports to advanced segmentation, optimisation and personalisation and modeling in R.

So as technology continues to develop, how can analysts and marketers stay up to date on their skills?

1. Attend trainings and conferences like Adobe Summit. These events are a great opportunity to learn how other companies are leveraging technologies, and spark creative ideas. If you struggle to justify budget, propose attending low cost events like DAA Symposiums or our ACCELERATE, or consider submitting a speaking submission to share your own insights (as speaking normally earns you a free conference pass.)

2. Read up! There is no shortage of blogs and articles that discuss new trends in digital. Try to carve out a small amount of time each day or week to read a few.

3. Network and discuss. Local events like DAA Symposiums, Web Analytics Wednesdays and Meet Ups are great places to meet people and discuss trends and challenges.

4. Join the social conversation. If you can’t attend local events (or, not as often as you would like) use social media as another source of inspiration and conversation. Twitter, Linked In groups or the new DAA forums are great places to start.

5. Online courses. Lots of vendors offer free webinars that can help you stay up to date with your skills. Or, consider taking a Coursera, Khan Academy or similar online course to learn something new.  

6. Experiment. Playing can be learning! If you hear of a new tool, social channel or technology, try getting your hands on it to see how it works.

What other tips do you have for keeping skills fresh? Share them in the comments!

Published on March 31, 2014 under Adobe, Conferences, Professional Development, Training

Adobe Summit Bound (2014)

It seems impossible to believe that twelve months has passed already. But here I am, Salt Lake City-bound for another Adobe Digital Marketing Summit.

For the past couple of years, I have been lucky enough to be invited to Adobe Summit as a “Summit Insider.” Being a Summit Insider gives me a chance to not only enjoy the education, networking and entertainment at Summit, but also an opportunity to share the experience with those who might not be able to make it. I’m super excited to be back, so thanks to the Adobe team for inviting me!

What am I looking forward to?

Like a kid in a candy store, I eagerly perused the Summit Agenda and have carefully selected breakout sessions on topics like predictive analytics, social analytics, data communication and storytelling, and building cross-department co-operation and a culture of analytics.

And even though I am the totally clueless person who never knows the bands, I’m definitely looking forward to the Summit Bash and musical acts Vampire Weekend and Walk The Moon. (Don’t worry, I created a Spotify playlist to brush up on my “new cool music” knowledge.)

Come say hi!

Are you planning on attending Summit? Come say hi! I’ll be there with my fellow Summit Insiders, Travis Wright, Toby Bloomberg and Elisabeth Osmeloski, as well as my partners at Web Analytics Demystified.

Keep up to date

Don’t forget to follow #AdobeSummit on Twitter via the official Twitter account (@AdobeSummit) and your Summit Insiders.

In town a little early?

Come check out Un-Summit on Monday afternoon. Un-Summit is a great chance to catch up with friends before the conference craziness kicks off, and hear from some great speakers.

Published on March 21, 2014 under Adobe, Conferences

It’s not about “Big Data”, it’s about the “RIGHT data”

Unless you’ve been living under a rock, you have heard (and perhaps grown tired) of the buzzword “big data.” But in attempts to chase the “next shiny thing”, companies may focus too much on “big data” rather than the “right data.”

True, “big data” is absolutely “a thing.” There are certainly companies successfully crunching massive volumes of data to reveal actionable consumer insight. But there are (many) more that are buried in data, and wondering why they are endlessly digging when others have struck gold.

Unfortunately, “big data” discussions often lead to:

  1. An assumption that more is better;

  1. A tendency for companies to try to skip the natural maturation of analytics in their organisation, in attempt to jump straight to “big data science.”

The value of data is in guiding business success, and that does not necessarily require massive volumes of data.

So when is big data of value?

  • When a company has pushed the limits of what they were doing with their existing data;

  • When they have the people, process, governance and infrastructure to collect and analyse volumes of data; and

  • When they have the resources and support to optimise based on that data.

But to succeed at a more foundational level, companies should focus first on whether they have:

  • A culture that fully integrates data and analytics into its planning and decision making;

  • The right data to guide their strategy and tactics. This includes:

    • Data that reveals whether initiatives have been successful, addressing the specific goals of the work;
    • Data that provides insight into progress, including early indicators of the need to “course correct”; and
    • Data that identifies new opportunities.
  • The resources and support to optimise based on findings from current data

While all businesses should be preparing for increased use and volume of data in the coming years, it is far easier to chase and hoard more and more and more data than it is to derive value from the data that already exists. However, the latter will drive far greater business value in the long term, and set up the right foundation to grow into using big data effectively.

Published on January 23, 2014 under "Big Data"

My presentation from the Digital Analytics Association San Francisco Symposium is now available on SlideShare:

What the ‘Quantified Self’ movement and really, really personal data means for marketing, analytics and privacy?

At the intersection of fitness, analytics and social media, a new trend of “self-quantification” is emerging. Devices and applications like Jawbone UP, Fitbit, Runkeeper, Foursquare and more make it possible for individuals to collect tremendous detail about their lives, creating a wealth of incredibly personal data. What does this intersection of “”big data”" and very small, very personal data teach us about the practice of analytics? And what cautions must marketers heed with respect to targeting and privacy in trying to seize upon this trend?

Thoughts/feedback? Share them in the comments!

Published on November 20, 2013 under Uncategorized

Better ways to measure content engagement than time metrics

I spent five years responsible for web analytics for a major ad-monetised content site, so I’m not immune to the unique challenges of measuring a “content consumption” website. Unlike an eCommerce site (where there is a more clear “conversion event”) content sites have to struggle with how to measure nebulous concepts like “engagement.” It can be tempting to just fall back on measures like “time on site”, but these metrics have significant drawbacks. This post outlines those, as well as proposing alternatives to better measure your content site.

So … what’s wrong with relying on time metrics?

1. Most business users don’t understand what they really mean

The majority of business users, and perhaps even newer analysts, may not understand the nuance of time calculations in the typical web analytics tool.

In short, time is calculated from subtracting two time stamps. For example:

Time on Page A = (Time Stamp of Page B) – (Time Stamp of Page A)

So time on page is calculated by subtracting what time you saw the next page from what time you saw the page in question. Time on site works similarly:

Time on Site = (Time Stamp of last call) – (Time Stamp of first call)

A call is often a page view, but could be any kind of call – an event, ecommerce transaction, etc.

Can you spot the issue here? What if a user doesn’t see a Page B, or only sends one call to your web analytics tool? In short: those users do not count in time calculations.

So why does that skew your data?

Let’s take a page, or website, with a 90% bounce rate. Time metrics are only based on 10% of traffic. Aka, time metrics are based on traffic that has already self-selected as “more interested”, by virtue of the fact that they didn’t bounce!

2. They are too heavily influenced by implementation and technical factors unrelated to user behaviour

The way your web analytics solution is implemented can have a significant impact on time metrics.

Consider these two implementations and sets of behaviour:

  • I arrive on a website and click to expand a menu. This click is not tracked as event. I then leave.
  • I arrive on a website and click to expand a menu. This click is tracked as an event. I then leave.

In the first example, I only sent one call to analytics. I therefore count as a “bounce”, and my time on the website does not count in “Time on Site”. In the second example, I have two calls to analytics, one for the page view and one for the event. I no longer count as a bounce, and my time on the website counts as “Time on Site.” My behaviour is the same, but the website’s time metrics are different.

You have to truly understand your implementation, and the impact of changes made to it, before you can use time metrics.

However, it’s not even just your site’s implementation that can affect time metrics. Tabbed browsing – default behaviour for most browsers these days – can skew time, since a user who keeps a tab open will keep “ticking” until the session times out in 30 mins.

Even the time of day your customers choose to browse can also impact time on site, as many web analytics tools end visits automatically at midnight. This isn’t a problem for all demographics, but perhaps the TechCrunches and the Mashables of the world see a bigger impact due to “night owls”!

3. They are misleading

It’s easy to erroneously determine ‘good’ and ‘bad’ based on time on site. However, I may spend a lot of time on a website because I’m really interested in the content, but I can also spend a lot of time on a website because the navigation is terrible and I can’t find what I need. There is nothing about a time metric that tells you if the time spent was successful, yet companies too often consider “more time” to indicate a successful visit. Consider a support site: a short time spent on site, where the user immediately got the help they needed and left, is an incredibly successful visit, but this wouldn’t be reflected by relying on time measures.

So what should you use instead?

Rather than relying on “passive” measures to understand engagement with your website, consider how you can measure engagement via “active” measures: aka, measuring the user’s actions instead of time passing.

Some examples of “active” measures on a content site:

  • Content page views per visit. A lot of my concerns about regarding time measures also apply to “page views per visit” as a measure. (Did I consume lots of page views because I’m interested, or because I couldn’t find what I was looking for?) For a better “page views per visit” measure of engagement, track content page views, and calculate consumption of those per visit. This would therefore exclude navigational and more “administrative” pages and reflect actual content consumption. You can also track what percentage of your traffic actually sees a true content page, vs. just navigational pages.
  • Ad revenue per visit. While this is less a measure of “engagement”, businesses do like to get paid, so this is definitely an important measure for most content sites! It can often be difficult to measure via your analytics tool, since you need to not only take in to account the page views, but what kind of ad the user saw, whether the space was sold or not and what the CPM was. However, it’s okay to use informed estimates. For example:Click-through rate to other articles. A lot of websites will include links to “related articles” or “you also might be interested in….” Track clicks to these links and measure click rate. This will tell you that users not only read an article, but were interested enough to click to read another.
    • I saw 2 financial articles during my visit. We sell financial article pages at an average $10CPM and have an estimated 80% sell through rate. My visit is therefore worth 2/1000*$10*80% = 1.6 cents. This can be a much more helpful measure than “page views per visit” since not all page views are created equal. Having insight in to content consumed and its value can help drive decisions like what to promote or share.
  • Number of shares or share rate. If sharing is considered important to your business, clearly highlight this call to action, and measure whether users share content, and what they share. Sharing is a much stronger indicator of engagement than simply viewing. (You won’t be able to track all shares, for example, copy-and-pasting URLs won’t be tracked, but tracking shares will still give you valuable information about content sharing trends.)
  • Download rate. For example, downloading PDFs.
  • Poll participation rate or other engaging activities.
  • Video Play rate. Even better, track completion rate and drop-off points.
  • Sign up and/or Follow on social.
  • Account creation and sign in.

If you’re already doing a lot of the above, consider taking it a step further and calculating visit scores. For example, you may decide that each view of a content article is 1 point, a share is 5 points, a video start is 2 points and a video complete is 3 points. This allows you to calculate a total visit score, and analyse your traffic by “high” vs “low” scoring visitors. What sources bring high scoring visitors to the site? What content topics do they view more? This is more helpful than “1:32min time on site”!

By using these active measures of user behaviour, you will get  better insight than through passive measures like time, which will enable better content optimisation and monetisation.

Is there anything else you would add to the list? What key measures do you use to understand content consumption and behaviour?

Published on September 16, 2013 under Analysis, Best Practices, Content

Data Privacy: It’s not an all or nothing!

Recently I have been exploring the world of “self quantification”, using tools like Jawbone UP, Runkeeper, Withings and more to measure, well, myself. Living in a tech-y city like Boston, I’ve also had a chance to attend Quantified Self Meet Ups and discuss these topics with others.

In a recent post, I discussed the implications of a movement like self quantification on marketing and privacy. However, it’s easy for such conversations to to stay fairly simply, without necessarily addressing the fact that privacy is not an all or nothing: there are levels of privacy and individual permissions.

Let’s take self quantification as an example. On an on-going basis, the self quantification tools I use track:

  • My every day movement (steps taken, as well as specific exercise activities)
  • Additional details about running (distance, pace, elevation and more)
  • Calorie intake and calorie burn
  • Heart rate, both during exercise (via my Polar heart rate monitor or Tom Tom running watch) and standing resting heart rate (via my Withings scale)
  • Weight, BMI and body fat
  • Sleep (including duration and quality)

That’s a ton of data to create about myself every day!

Now think about the possible recipients of that data:

  • Myself (private data)
  • My social network (for example, my Jawbone UP “team” can see the majority of my data and comment or like activities, or I can share my running stats with my Facebook friends)
  • Medical professionals like my primary care physician
  • Researchers
  • Corporations trying to market to me

It’s so easy to treat “privacy” as an all or nothing: I am either willing to share my data or I am not. However, consumers demand greater control over their privacy precisely because there are different things we’re willing to share with different groups, and even within a group, specific people or companies we’re willing to share with.

For example, I may be willing to share my data with my doctor, but not with corporations. Or I may be willing to share my data with Zappos and Nike, but not with other corporations. I may be willing to share my running routes with close friends but not my entire social network. I may be willing to share my data with researchers, but only if anonymised. I may be willing to share my activity and sleep data with my social network, but not my weight. (C’mon, I won’t even share that with the DMV!)

This isn’t a struggle just for self quantification data, but rather, a challenge the entire digital ecosystem is facing. The difficulty in dealing with privacy in our rapidly changing digital world is that we don’t just need to allow for a share/do not share model, but specific controls that address the nuance of privacy permissions. And the real challenge is managing to do so in a user-friendly way!

What should we do? While a comprehensive system to manage all digital privacy may be a ways off (if ever), companies can get ahead by at least allowing for customisation of privacy settings for their own interactions with consumers. For example, allowing users to opt out of certain kinds of emails, not just “subscribe or unsubscribe”, or providing feedback that which targeted display ads are unwelcome, or irrelevant. (And after you’ve built those customisation options, ask your dad or your grandma to use them to gauge complexity!)

Want to hear more? I have submitted to speak about these issues and more at SXSW next year. Voting closes EOD Sun 9/8, so if you’re interested in learning more, please vote for my session! http://bit.ly/mkiss-sxsw

Published on September 6, 2013 under Privacy, Self-Quantification

ACCELERATE your analysis skills in Columbus OH!

It’s no secret that ours is a new and rapidly evolving industry. Skills are often acquired on-the-job, and training is critical to building a successful analytics practice and career.

That’s why I’m so excited about ACCELERATE in Columbus, OH. Even before I joined Demystified, ACCELERATE was my favourite event of the year. As my prodigious use of Twitter would suggest, I have been accused of having a short (140-character!) attention span, and ACCELERATE is the perfect format for delivering rapid-fire insights without even a split second to get bored. On top of that, ACCELERATE has hosted some fantastic speakers, many who don’t typically speak at analytics conferences, giving us a fresh perspective.

This year however, ACCELERATE raises the bar, with two days of training preceding the event. With specific trainings on testing & optimisation, social analytics, analysis practice and career development, Adobe SiteCatalyst, Discover, ReportBuilder and Advanced Google Analytics, there’s a training to help you grow, no matter your level.

I’m personally pretty excited to get a chance to discuss analysis and analytics career development. Here’s a little sneak peak of what you can expect to hear about in my analysis practice training:

  • A guide to using analytics for performance measurement, whether it be on-going performance or for a specific initiative
  • A guide to ad-hoc analysis for hypothesis testing
  • Communication tips and tricks
  • Best practices for communicating analytics results, including:
    • Tailoring to different learning styles
    • Tips for data visualisation
  • What a career in analytics can look like, and how to choose your path
  • How to successfully recruit for analytics
  • How to grow and retain your analysts

And shhhhh: Don’t tell Eric, but I snuck you all a discount. Use the code blog-michele (or just click through that link) for 10% off ACCELERATE trainings and the event itself.

For more information, check out webanalyticsdemystified.com/accelerate/.  Or, just go ahead and sign up now. You know you want to.

Published on August 28, 2013 under ACCELERATE, Conferences, Training

Self-Quantification: Implications for marketing & privacy

At the intersection of fitness, analytics and social media, a new trend of “self-quantification” is emerging. Devices and Applications like Jawbone UPFitbitNike Fuel BandRunkeeper 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.

In my previous post, I discussed what this kind of tracking could teach us about the practice of analytics. Now, I’d like to consider what it means for marketing, targeting and the privacy debate.

Implications for marketing, personalisation & privacy

I have argued for some time that for consumers to become comfortable with this new data-centric world, they need to see the benefits of data use.

There are two sides to this:

1. Where a business is using consumers’ data, they need to provide the consumer a benefit in exchange. A great way is to actually share that data back to the consumer.

Some notable examples:

  • Recommendations: “People who looked at Product X also looked at Product Y”, as seen on sites like Amazon.com.
  • Valuation and forecasts: Websites like True Car, Kelley Blue Book and Zillow crunch data from thousands of transactions and provide back to consumers, to help them understand how the pricing they are looking at compares to the broader market.
  • Credit scores: Companies like Credit Karma offer a wealth of data back to consumers to understand their credit and help them make better financial decisions.
  • Ratings and Reviews: Companies like CNet inform customers via their editorial reviews, and a wealth of sites like Amazon and Newegg provide user ratings to help inform buying decisions.

2. Outside of business data, consumers’ own collection and use of data helps increase the public understanding of data. The more comfortable individuals get with data in general, the easier it is to explain data use by organisations. The coming generations will be as fluent with data as millennials today are fluent with social media and technology.

This type of data is a huge opportunity for marketers. Consider the potential for brands like Nike or Asics to deliver truly right-time marketing: “Congratulations on running 350 miles in the last quarter! Did you know that running shoes should be replaced every 300-400 miles? Use this coupon code for 10% off a new pair.” Or McDonalds to use food intake data to tell them that 1) The consumer hasn’t yet eaten lunch (and it’s 30mins past their usual lunch time), 2) The consumer has been following a healthy diet and 3) The consumer is on the road driving past a McDonalds, and promote new healthy items from their menu. These are amazing examples of truly personalised marketing to deliver the right offer at the right time to the right person.

However, it is also using incredibly personal data and raises even newer privacy concerns than simple online ad targeting. Even if a marketer could do all of that today, the truth is, it would probably be construed as “creepy” or, worse, a disturbing invasion of privacy. After all, we’re not even comfortable sharing our weight with the DMV. Can you imagine if you triggered a Weight Watchers ad in response to your latest Withings weigh-in?!

So how must marketers tread with respect to this “self-quantification” data and privacy?

1. We need to provide value. This might sound obvious – of course marketers need to provide value. However, I would argue that when consumers are trusting us with what amounts to every detail of their lives, we must deliver something that is of more value to the consumer than it is to us. This all comes down to the idea of marketing as a service: it should be so helpful you’d pay for it.

2. There has to be consent. This technology is too new, and there are too many concerns about abuse, for this to be anything but opt-in. (The idea of health insurance companies rejecting consumers based on lifestyle is a typical argument used here.) If marketers provide for (and respect) opt-in and -out, and truly deliver the right messaging, they’ll earn the right to broaden their reach.

3. It requires crystal-clear transparency. Personalisation and targeting today is already considered “creepy.” Use of this incredibly personal data requires absolutely transparency to the end user. For example, when being shown an offer, consumers should know exactly what they did to trigger it, and be able to give feedback on the targeted message.

This already exists in small forms. For example, the UP interface already gives you daily “Did you know?”s with fun facts about your data vs the UP community. Users can like or flag tips, to give feedback on whether they are helpful. There has to be this kind of functionality, or users only option to targeting will be to revoke access via privacy settings.

4. We need to speak English. No legalese privacy policies and no burying what we’re really doing on page 47 of a document we know no one will ever read. Consumers will be outraged that we didn’t tell them the truth about what you were doing, and we’ll never regain that trust.

5. We have to get it right. And by that, I mean, right by the consumer’s perspective. There will be no second chances with this kind of data. That requires careful planning and really mapping out what data we need, how we’ll get consent, how we’ll explain what we’re doing and ensuring the technology works flawlessly. Part of the planning process has to be dotting every i and crossing every t and truly vetting a plan for this data use. If marketers screw this up, we will never get that permission again.

This includes getting actual consumer feedback. A small beta release with significant qualitative feedback can help us discover whether what we’re doing is helpful or creepy.

6. Don’t get greedy. If marketers properly plan this out, we should be 100% clear on exactly what data we need, and not get greedy and over collect. Collecting information we don’t need will hurt opt-in. This may involve, for example, clearly explaining to consumers what data we collect for their use, and what we use for targeting.

7. Give users complete control. This will include control over what, of their data, is shared with the company, what is shared with other users, what is shared anonymously, what is used for targeting. There has to be an exhaustive (but user friendly) level of control to truly show respect for informed and control opt-in. This includes the ability to give feedback on the actual marketing. Without the ability to continually tell a business what’s creepy and not, we end up in a binary system of either “consenting” or “not”, rather than an on-going conversation between consumer and business about what is acceptable.

Think about the user reaction every time Facebook changes their privacy policy or controls. People feel incredible ownership over Facebook (it’s “their” social world!) even though logically we know Facebook is a business and does what suits their goals. The tools of the future are even more personal: we’re talking about tracking every minute of sleep, or tracking or precise location. This data is the quantification of who we are.

With opportunity comes responsibility

This technology is an amazing opportunity for marketers and consumers, if done well. However, marketers historically have a habit of “do first, ask permission later.” To be successful, we need to embark on this with consumers’ interests and concerns put first, or we’ll blow it before we even truly begin.

Published on July 17, 2013 under Privacy, Self-Quantification

What Self-Quantification Teaches Us About Digital Analytics

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.

These aren’t the only areas that technology is creeping in to. You can buy smart phone controls for your home alarm system, or your heating/cooling system. “Smart” fridges are no longer a crazy futuristic concept. Technology is creeping in to every aspect of our lives. This can be wonderful for consumers, and a huge opportunity for marketers, but it has to be done right.

In this series of blog posts, I will explore what this proliferation of tools and data looks like, how it relates to analytics, and what it means for marketing, targeting and the privacy debate.

What Self-Quantification Teaches Us About Digital Analytics 

Since April, myself and a surprising number of the digital analytics community have been exploring devices like Jawbone UP and Fitbit. Together with apps and tools like Runkeeper, Withings, My Fitness Pal, Foursquare and IFTTT, I have created a data set that tracks all my movements (including, often, the precise location and route), every workout, every bite and sip I’ve consumed, every minute of sleep, my mood and energy levels, and every venue I’ve visited.

Amidst the explosion of “big data”, this is a curious combination of “big data” (due to the sheer volume created from multiple users tracking these granular details) and “small data” (incredibly detailed, personal data tracking every nuance of our lives.)

Why would one go to all this trouble? Well, “self-quantifiers” are looking to do with their own “small data” exactly what we propose should be done with “big data”: be better informed, and use data to make more educated decisions. Over the past few months, I have found that my personal data use reveals surprisingly applicable learnings for analytics.

Learning 1: Like all data and analytics, this effort is only worthwhile and the data is only valuable if you use it to make better decisions. 

Example: My original reason for trying Jawbone UP was for insight into my sleep patterns. Despite getting a reasonable amount of sleep, I struggled to wake up in the morning. A few weeks of UP sleep data told me that my current wakeup time was set right in the middle of a typical “deep sleep” phase. Moving my wakeup time one hour earlier, meant waking in a lighter phase of sleep and made getting up significantly easier. This sleep data wasn’t just “fun to know” – I literally used it to make decisions, with positive results.

Learning 2: Numbers without context are useless.

Using UP, I track my daily movements, using a proxy of “steps.” Every UP user sets a daily “step goal” (by default, 10,000 steps.) Without a goal, 8,978 would just be a number. With a goal, it means something (I am under my goal) and gives me an action to take (move more.)

Learning 3: Good decisions don’t always require perfect data

Steps is used as a proxy for all movement. It’s not a perfect system. After all, it struggles to measure activities like cycling, and doesn’t take into account things like heart rate. (Note though that these devices do typically give you a way to manually input activities like cycling, to take into account a broader spectrum of activity.)

However, while imperfect, this data certainly gives you insight: Have I moved more today than yesterday? How am I tracking towards my goal? Am I slowly increasing how active I am? Did I beat last week? Good decisions don’t always involve perfect data. Sometimes, good directional data and trends provide enough insight to allow you to confidently use the data.

Learning 4: Not all tools are created equal, and it’s important to use the right tool for the job

On top of Jawbone UP, I also heavily use Runkeeper, as well as a Polar heart rate monitor. While UP is great for monitoring my daily activity (walking to the store, taking the stairs instead of the escalator), Runkeeper gives me deeper insight into my running progress. (Is my pace increasing? How many miles did I clock this week? What was my strongest mile?) UP and Runkeeper are different but complementary tools, and each has a purpose. Which data set I use depends on the question at hand.

Learning 5: Integration is key

One of things I enjoy the most about UP is the ability to integrate other solutions. For example, Runkeeper pushes information about my runs to UP, including distance, pace, calorie burn and a map of the route. I have Foursquare integrated via IFTTT (If This Then That) to automatically push gym check-ins to UP. Others have their Withings scale or food journals integrated.

Depending on the question at hand, UP or Runkeeper might have the data I need to answer it. However, there’s huge value for me in having everything integrated into the UP interface, so I can view a summary of all my data in one place. One quick glance at my UP dashboard tells me whether I should rest and watch some TV, or go for an evening walk.

Learning 6: Data isn’t useful in a vacuum

The Jawbone UP data feed is not just about spitting numbers at you. They use customisable visualisations to help you discover patterns and trends in your own data.

For example, is there a correlation between how much deep sleep you get and how many steps you take? Does your active time align with time taken to fall asleep?

While your activity data, or sleep data, or food journal alone can give you great insight, taking a step back, and viewing detailed data as a part of a greater whole, is critical to informed analysis.

The bigger picture

In the end, data analysis is data analysis, no matter the subject of the data. However, where this “self-quantification” trend really shakes things up is in the implications for marketing. In my next post, I will examine what the proliferation of personal data means for targeting, personalisation and the privacy debate.

Jawbone UP Sleep Analytics  Jawbone UP Runkeeper Integration  Jawbone UP Trend Chart  Runkeeper

Published on July 16, 2013 under Privacy, Self-Quantification

Digital Analytics “Down Under” – Key Takeaways from eMetrics Sydney

Though it might be eight thousand miles away from the continental United States, my takeaways from eMetrics Sydney reveal that Australia faces the same challenges as digital analytics in the United States, and has some similarly fantastic speakers with great advice and stories to share.

Like the United States (and everywhere, really) there is a definite skills shortage for analysts in Australia – and a market willing to compensate! The Institute of Analytics Professionals of Australia‘s annual survey revealed that while the median income in Australia is $57,000, the median income for analytics professionals is $110,000. What’s more, there’s such a shortage that (from my conversations) there’s a definite opportunity for foreigners to find great roles within Australian companies. (So if you’ve been interested in a new life experience, this seems like a great time to try Australia!)

There was no shortage of great advice at eMetrics Sydney. Here were a few of my favourite takeaways:

“The stone age was marked by man’s clever use of crude tools. The information age is marked by man’s crude use of clever tools.”

The value of analytics is to allow you to quantify what would otherwise merely be anecdotes. (Chris Thornton, RAMS.) This kind of knowledge and understanding of the customer living outside Sales is actually a fairly recent development. After all, historically Sales were the ones with direct contact with the customer, and the ones who could bring back stories of what was happening “out there.” Now, analysts are able to not only identify but also quantify the magnitude of problems and opportunities.

Curiosity may have killed the cat, but it made for awesome analysts and marketers. Chris Thornton from RAMS declared it a “sign of a highly functioning marketing team”: when marketers get curious about data, it can become addictive and contagious, leading to great things within the organisation. After all, hiring analysts is not about the tools they know how to use: creativity is key.

Common sources of analytics failure. While, sadly, these are not new, Steve Bennett (News Corp) discussed common sources of analytics failure, including:

  • Measuring too many things
  • Measuring the unimportant
  • Not measuring the important
  • Measurement is not mapped to what drives the business
  • Asking the wrong business questions
  • Delivering flawed insights
  • Not acting on the insight

It’s all about action. The value in data analytics is in the decision an executive makes based on the insight, not the data itself. And while analysts often labour over data quality and trying to perfect data capture, keep in mind that if you wait for your data to be 100% accurate, you will never do anything. You need your data to be reliable, but that may not actually require 100% accuracy. An interesting piece of (very honest) advice from Steve Bennett from News Corp was to never ask for budget for data quality. (It will never be understood, appreciated or prioritised as important by those removed from it!) Rather, incorporate that work into other, bigger projects, that are easier for business stakeholders to understand the value of, where they can see tangible results. Bennett noted that you don’t need a $100 million dollar datawarehouse to see value from analytics: Do what you can with the data, resources and tools you have, and you will see value!

“Analytics stems from a need to do more with less. After all, if you had unlimited resources, you would not need to optimise your efforts!” -Jim Sterne

Data is like diamonds. One of my favourite sessions, and definitely my favourite analogy, was Rod Bryan from Deloitte, who likened data to diamonds, for a number of interesting reasons:

  • Data, like diamonds, is typically not valuable in its raw form. Rather, it requires modeling and engineering to create something of value. The value of data comes from the interpretation, insight and communication, just as diamonds require specialised cutting to reveal their value.
  • Data, like diamonds, are hard to get value out of.
  • Data, like diamonds, is (over?)hyped. Diamonds are, after all, incredibly common. Data too is everywhere. So it’s not having data, but what you do with it that matters.

Importance of communication. Good communication is absolutely critical for a successful analytics program. After all, finding insights in data isn’t what matters: it’s being able to communicate them - and creating a process for doing so again and again. A person can have the greatest insight in the world, but if they can’t share it with other people, it doesn’t matter. For example, think about complex statistical models and algorithms. While they may be good predictors, business users won’t buy in to something they don’t understand. Black box or very complicated models are less likely to be successful than something the your stakeholders can understand.

“There is no such thing as information overload, just bad design.” -Edward Tufte

Data visualisation. Data visualisation is an excellent example of the importance of communication! Data visualisation is not itself about insight, but rather, about communicating insight. -Paul Hodge. Hodge’s session on data visualisation was fantastic not only for the content presented live, but for the enhanced content available via his live tweetstream! I definitely recommend checking out some of the additional resources.

Advice for growing analysts. Communication skills are likewise important for the growth of your career. Rod Bryan from Deloitte noted that the best analysts often make the worst leaders because they lead without understanding how people use information. In fact, being viewed as “analytical” may not be a good thing for your career, if it means you are perceived as not business-minded. (Gautam Bose, National Australia Bank.)

Rather than technical or tool skills, Steve Bennett from News Corp advised analysts to work on business, communication and political skills to succeed. Jim Sterne noted that while analysts often consider themselves independent arbitrators, the best thing an analyst can do is have an opinion. Your value comes from your opinion, coupled with the data and analysis to back it up!

“Do not use statistics as a drunk man uses lamp posts – for support rather than illumination.” -Jim Sterne 

Organisational challenges. One of the challenges of working in analytics, and especially working on analytics projects with IT teams, is that analytics is inherently different from the typical IT process. IT projects typically require a definitive outcome, while analytics is about exploration.

This is a real struggle in analytics: Rigidity is the killer of good analytics, but analytics without discipline is a mess. Creating a culture that encourages “playing” with data is a big organisational challenge, but it’s also critical to success. Businesses easily understand “reporting.” What they often fail to understand is the opportunity of analytics.

“Some people make decisions like a bladder.” (Only make quick decisions when you have to!) - Steve Bennett, News Corp

Analytics is not a cure-all. There are some things that analytics doesn’t apply to! It is not a cure for all of society’s ills. There can be a danger of users drinking too much “kool-aid” and ignoring common sense. Analytics can’t fully replace the insights of a competent decision maker’s personal knowledge & experience. (-Steve Bennett, News Corp)

Conclusion. I’m admittedly a little biased (given I was born in Australia) but if you’ve never been, I highly recommend checking out not only eMetrics Sydney, but also Australia generally! It was a great experience and an opportunity to hear from some new voices in analytics.

Published on July 2, 2013 under Conferences


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