Eye Tracking, ML and Web Analytics – Correlated Concepts? Absolutely … Not just a Laura-ism, but Confirmed by a Professor at Carnegie

Eye Tracking Studies Required Expensive Hardware in the Past

 

 

  Anyone who has read my blog (shameless self-plug: http://www.lauraedell.com) over the years will know, I am very passionate about drinking my own analytical cool-aid. Whether during my stints as a Programmer, BI Developer, BI Manager, Practice Lead / Consultant or Senior Data Scientist, I believe whole-heartedly in measuring my own success with advanced analytics.  Even my fantasy football success (more on that in a later post) …But you wouldn’t believe how often this type of measurement gets ignored. ​

Introduce Eye-Tracking Studies-Daunting little set of machines in that image above, I know…But this system has been a cornerstone in the measurement practices of advertisement efficacy for eons, and something I latched onto into my early 20’s, in fact, ad-nauseam. I was lucky enough to work for the now uber online travel company who shall go nameless (okay, here is a hint: remember a little ditty that ended with some hillbilly singing “dot commmm” & you will know to whom I refer). This company believed so wholeheartedly in the user experience that they allowed me, young ingénue of the workplace, to spend thousands on eye tracking studies against a series of balanced scorecards that I was developing for the senior leadership team. This is important because you can ASK someone whether a designed visualization is WHAT THEY WERE THINKING or WANTING, even if done iteratively with the intended target, yet 9x out of 10, they will nod ‘yes’ instead of being honest, employing conflict avoidance at its best. Note, this applies to most, but I can think of a few in my new role at MSFT who are probably reading this and shaking their head in disagreement at this very moment <Got ya, you know who you are, ya ol’ negative Nelly’s; but I digress…AND… now we’re back –>

Eye tracking studies are used to measure efficacy by tracking what content areas engage users’ brains vs. areas that fall flat, are lackluster, overdesigned &/or contribute to eye/brain fatigue. It measures this by “tracking” where & for how long your eyes dwell on a quadrant (aka visual / website content / widget on a dashboard) and by recording the path & movement of the eyes between different quadrants’ on a page. It’s amazing to watch these advanced, algorithmic-tuned systems, measure a digital, informational message, in real-time, as it’s relayed to the intended audience, all while generating the statistics necessary to either know you “done a good job, son” or go back to the drawing board if you want to achieve the ‘Atta boy’. “BRILLIANT, I say.”

What I also learned which seems a no-brainer now, but people tend to read from Left to Right & from top to bottom. <duh> So, when I see anything that doesn’t at LEAST follow those two simple principles, I just shake my head and tisk tisk tisk, wondering how these improperly designed <insert content here> will ever relay any sort of meaningful message, destined for the “oh that’s interesting to view once” sphere instead of raising to the levels of usefulness it was designed for. Come on now, how hard is it to remember to stick the most important info in that top left quadrant and the least important in the bottom right, especially when creating visualizations for use in the corporate workplace by senior execs. They have even less time & attention these days to focus on even the most relevant KPIs, those they need to monitor to run their business & will get asked to update the CEO on each QTR, with all those fun distractions that come with the latest vernacular du-jour taking up all their brain space: “give me MACHINE LEARNING or give me death; the upstart that replaced mobile/cloud/big data/business intelligence (you fill in the blank).

But for so long, it was me against the hard reality that no one knew what I was blabbing on about, nor would they give me carte blanche to re-run those studies ever again <silently humming, “Cry me a River”>, And lo and behold, my Laura-ism soapbox has now been vetted, in fact, quantified by a prestigious University professor from Carnegie, all possible because a little know hero named

 

Edmond Huey, now near and dear to my heart, grandfather of the heatmap, followed up his color-friendly block chart by building the first device capable of tracking eye movements while people were reading. This breakthrough initiated a revolution for scientists but it was intrusive and readers had to wear special lenses with a tiny opening and a pointer attached to it like the 1st image pictured above.

heatmapFast-forward 100 years, and combine all ingredients into the cauldron of innovation and technological advancement, sprinkle in my favorite algorithmic pals:  CNN and LSTM, and the result is that grandchild now known as heat mapping. It’s eye tracking analytics without all the cost, basically a measure of the same phenomena (at a fraction of the cost).

Cool history lesson, right?

So, for those non-believers, I say, use some of the web analytic trends of the future (aka Web Analytics 3.0). Be a future-thinker, forward mover, innovator of your data science sphere of influence, and I tell you, you will become so much more informed and able to offer more information to others based on…MEASUREMENT (Intelligent MEASUREMENT in a digital transformational age).

 

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