Data Creates Normalization, Not Differentiation…Unless

Storytime —

Charlie first heard the statement, “Data Creates Differentiation” and he decided to make this the mantra of his life. Everything Charlie did, was data-led.

Charlie carefully analyzed what everyone around them was doing, what they were wearing, and how they spoke. Now he had a choice, to either do what’s the statistical normal or go away far from the statistical normal. Now if Charlie chooses to go with the mass, they’re automatically disqualified as different, and if they go against the mass then they’re an outcast.

When Charlie traveled, they found this data-led life creates a lot of problems because data from India around spicy is very different from an American subset of the data; and previous data told Charlie that they enjoyed spicy food in America.

Fast forward through Charlie’s life, and after studying the most common/least common degree, Charlie struggled to get a job, either because there was a lot of competition or because there weren’t a lot of jobs. Charlie then decided to start a business and once again used data to make decisions. Within a week, Charlie became the CEO of a company called ThisIsSex — The First Metaverse Brothel.

Blindly Following Data: A Reality Check For Brands

Now let’s look at this in the context of a brand. Brands usually do what data tells them to. In a previous blog titled, ‘Time Economies V/S Attention Economies & Their Role in Digital Marketing”, I’d mentioned how conversations in time & attention economies are measured with the same variables. “In a video advertising campaign, you buy someone’s time, not their attention. Conversely, in marketing newsletter campaigns, you buy someone’s attention, not their time. The point here is different outreach tactics, although part of the same ecosystem, don’t follow the same economics.”

In this blog, inspired by a conversation on Mentza, let’s take a more mathematical approach toward data. Most, if not all, normal distribution graphs generally take the form of a bell curve. The curve always peaks at average. Following this blindly, is the quickest, and most sureshot way to build the most average brand. This is why all food brands are “healthy, hygienic, natural, fresh” or some subset of it. (Also, I don’t understand how factory brands make that claim. Nothing that comes out of a factory is natural or fresh. Trust me, I used to be a chef and I’ve seen food factories.)

Now, this problem is further compounded by our love for data sets. Many of us go to our tools (GWI, Think with Google, Facebook Insights to Go, AppAnnie, LinkedIn Insights, etc) and spend hours creating as many data sets as possible using the simple concept of AND and OR. Essentially the same set theory we were introduced to in like 8th grade. Am I saying these tools don’t help? No. Not at all. They help, as does listening and audience data. But on its own, this data is nowhere near the kind of data necessary to create differentiation that will truly take a brand from 1–100.

Today, we all know which colors to use, when to post, what kind of hooks to use, where the call-to-action should be placed, and yes, these are brilliant use cases of data. In each of them, data is used for optimization, not differentiation. And the modern age is a horrible time to be average, especially for brands.

So What Kind of Data Creates Differentiation

I firmly believe, practice does not make you perfect. Perfect practice makes you perfect. Similarly, data does not make you different. Different data makes you different.

During the conversation, Jafar Baig, a behavioral & data scientist added (and I’m paraphrasing spoken word into written), “There is a misconception that big data and data can solve everything. In many cases, one doesn’t know the context behind the data. When you mine a million data points and build graphs, it is very easy to simplify and oversimplify, and not read into the nuances of the data. At the end of the day, it's easy to make a decision based on a good-looking graph.”

Anurag Vaish, founder of Mentza, during the conversation (again paraphrasing spoken word into written) said, “I’ve seen people who have absolutely no regard for data, and people who have too much regard for data. The truth is people have to figure out how to work with data. One thing data does not do, is lie. Data also requires interpretation. I’ve worked a lot my heuristic of choice is “triangulate”. Always look for the second data that verifies the first data. If that happens, you can trust it.”

Anurag also mentioned, “Data does create differentiation. For eg. if a person has hot beverages 1000 times, out of which 999 is coffee and 1 is tea, then looking at 1000 data points may not make sense. I look at it as two data points. Because it’s easier to differentiate 1 from two, not 1 from a thousand. Essentially, data does its job, it’s our ability to contextualize it that truly makes a difference.”

To listen to the full conversation, (and honestly, these excerpts do not do justice to the conversation) click this link.

Building Data Banks with Primary Data

Old school marketing talks about going into the field, talking to customers, distributors, and other stakeholders. Somewhere in the modern era, we’ve forgotten a bit of this, and our reliance on proprietary, bought, and shared data has increased. But the core truths of marketing, branding, and making connections are out there, not necessarily in the data. These connections help create primary data. Data that is unique to you. Data that no one else has.

It is this different data that is going to help you differentiate and stand out. Using secondary data to create differentiation is going to point towards starting a bowling alley business as close as possible to a pre-existing bowling alley business because that’s where people go for bowling.

I remember reading/hearing somewhere, “doing anything is crazy, documenting it is science”, and this needs to be the approach towards data gathering too. Depending on what category and kind of brand and business you are, the primary data that is relevant to you is largely going to change, and since data and algorithms require constants, there’s absolutely no framework to create primary data.

That said, using pre-existing customers, focus group interactions, and other tactics still go a long way in creating primary data. Increasing their frequency increases the amount of primary data created.

That’s all folks. If you’ve got the time, I’d highly recommend listening to the entire 25-minute Mentza conversation here:
https://portfolio.mentza.com/portfolio/sharjeelyunus/circles/16658

Hey folks, my name is Sharjeel Yunus, and I’m a brand & communications strategy consultant. I’m a jack of all, master of some — I offer more than any specialist of one :)

The writer in me is equal parts reader. The editor, a strict optimizer. I improvise, think on my feet, don’t waste time with inaction. But I don’t rush in either. I am calm, composed, and genuine. I like thinking out-of-the-box. Really far out of it. Yet, always on the same plane.

I trained as a chef where I acquired consistent attention to detail, an ability to juggle multiple projects, extreme time management, and process setting skills. I’ve led teams in media offices and on football fields. I’m a veteran gamer, a ‘PlayStation trophy’ hunter, a consequence-conscious decision-maker. Games, communities, and crafts have shaped me a lot. Cue quick poem outro:

I take pride in what I do.
Artists sign their name on art.
I’m a creator in an age new,
I do offer services a-la-carte.

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