Practicing Mindfulness Will Improve Your Data Analysis

Practicing Mindfulness Will Improve Your Data Analysis

Data analysis should be a catalyst for positive change in your business. Reaching goals requires effort, and effort without understanding is, at best, inefficient and often simply useless. Practicing mindfulness in data analysis is an effective counterbalance to paralysis by analysis.

According to Wikipedia, Mindfulness is the psychological process of bringing one’s attention to the internal and external experiences occurring in the present moment.

Mindfulness is the basic human ability to be fully present, aware of where we are and what we’re doing, and not overly reactive or overwhelmed by what’s going on around us. ~Mindful.org

I’m sure many of you are already seeing where mindfulness and analysis share some core principles. This is how I came to the realization…

A few years back, I lived in Honolulu, Hawaii. That’s where I first started practicing yoga as a counterbalance to my real passion – trail running. I’d been struggling with knee pain and my recovery between runs was longer than my fellow runners. I noticed the best runners were practicing yoga and so I decided to give it a shot. I started taking classes at a lovely studio in Kaka’ako called the Still and Moving Center.

The two teachers whose classes I loved the most were April and Tara. They both had very different styles but both encouraged mindfulness through their teaching.

Being Mindful Of Vanity Metrics In Data Analysis

April encouraged acknowledgement of internal negativity. Being aware of what, in the present, was holding one back was the first step to moving past it. I remember April honoring Ganesha as the remover of obstacles through her teaching.

April’s style of mindfulness inspires analysis that is critical, in a constructive way. It’s about overcoming, and not focused on failure. This outlook inspires growth through understanding what is easy and wrong, versus what is right and challenging. That means avoiding the vanity metrics that mask true opportunities for improvement and success. Mindfulness in analysis requires acknowledging the uncomfortable metrics that indicate barriers to achieving goals.

What Matters More Than Daily Active Usage

Snap CEO, Evan Spiegel discussed barriers and vanity metrics in a call with shareholders following their first report since their IPO. On the face of it, daily active usage (DAU) is the metric by which to measure a successful growth strategy. Certainly, obsession with this metric played a role in Facebook’s longevity and current success but Spiegel says:

…  I think while that’s the easy way to grow daily actives quickly, we don’t think that those sorts of techniques are very sustainable over the long term. And I think that can ultimately impact our relationship with the customer.

And so I can give an example where if we had just in the beginning encouraged Snapchatters to add all their friends in the contact book instead of just a few of them, they might feel really uncomfortable creating Snaps and adding them to their Story, because they wouldn’t know who was actually watching.

… so, I think the most important thing to understand is that really we think of this daily active user growth as a function or a derivative of the growth in creation.

Spiegel exhibits an awareness that the easily influenced metrics are not currently the right ones. Snapchat focuses first on metrics around creation as a way to drive growth whereas less mindful companies might focus on growth first, creation later.  

Had they decided to “growth hack” their daily engagement through interruption and push notifications what they’d likely see in this case is a growth in voyeurs and not creators. The metrics by which one measured success would drive growth that was not sustainable, leaving a Twitter-like result of few and fewer content creators with more and more eggs and an over-valued stock.

Instead, Spiegel and his team focus on the metrics that add the most value – content creation. Their metric is content creation and so they drive growth that I believe will result in longer lifetime engagement with the app.

Mindful Metrics Vs Vanity Metrics

Before Teacup Analytics, I was the Chief of Culture for Mad Mimi Email Marketing. We helped small businesses send simple, delightful newsletters. For us, the wrong metric was customer activity. It didn’t matter how often anyone logged in or how many emails were sent monthly though certainly that would have been easy to track and to influence.

Instead, after some mindful analysis, we realized that the best customers that stuck with us the longest had the loveliest newsletters and thus had the best response from their readers. That realization influenced our entire strategy around customer support and what we considered marketing. Mad Mimi was acquired by GoDaddy in 2014.

As per April’s teaching, mindful analysis with recognition of barriers and mistakes, lays a foundation for sustainable future success.

Related post: Measuring Micro-Moments With Google Analytics

Being Mindful Of Current Weakness And Current Opportunity

Tara’s teaching offered me a different perspective. Tara taught me to look on every weakness as an opportunity for improvement. If I said, “I’m not flexible” Tara would encourage me to rather consider that I’m becoming more flexible. This mindfulness results in a positivity that highlights opportunities for improvement rather than acknowledgement of lacking.

The distinction is vast. It’s the difference between seeing yourself as overweight versus seeing yourself as on the pathway to becoming healthy. One outlook encourages a state of failure. Tara’s mindfulness instead encourages a view of the present as a step in the right direction. It’s a mindfulness of where you are now as you strive to reach goals.

A Social Media Opportunity Discovered In Data Analysis

Here’s an example of Tara’s style of mindfulness applied to analysis. I was reviewing the web analytics of a non-profit who put a lot of time and effort into their social media strategy. While we were looking at their social media report in Teacup, we noticed that their strategy was failing in one significant metric – conversions. For this non-profit, it meant a lot of investment and not enough donations.

They were spending tons of time and effort in driving traffic to their site from social media but the strategy, as a fundraising idea, wasn’t working. Being mindful in our analysis, we framed the analysis goal as such: Where should we focus to improve our social media strategy?

Knowing that we’d find an opportunity here, we dove (or dived) into segmentation. Turned out that most of their social traffic was on mobile and the mobile traffic was not converting. This caused some dismay, considering they’d invested heavily in re-doing their website to be mobile optimized.

Reminding them of our mindful analysis goal, we looked for the easiest opportunities to improve their social media strategy and it turns out that the opportunity lied in their mobile design. They had two issues with social-mobile visitor experience. First, a pop up, asking for donations that looked great on desktop but was awful on mobile. This was the call-to-action that drove donations but it blocked most of the screen on mobile, instead.

Viewing this as an opportunity, they made a few small tweaks to their mobile site to cater for mobile visitors. Now it looks like this (with identifiers blurred out):

Mobile-optimized page for non-profits

And the results look like this:

Teacup Analytics Achievable Results

Quite impressive, right? That’s the result of mindful analysis.

Isn’t All Analysis Mindful?

We’d all certainly like to think that we’re mindful when analysing data but the truth is harsh – no, we’re usually not mindful in our analysis. Working in startups and in corporate, I’ve seen analysis take on the form of busywork that fills time in meetings and provide no understanding of why results are such, nor provide recommendations. So, no, not all analysis is mindful.

For data analysis to be mindful, the analysis must provide understanding not only of what value a metric has, but why it has that value and where the opportunity lies to improve on that value.

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