(Time to Read: 6 mins)
The relatively-new world of data science remains a mystery to most marketers and brand managers.
Because of that lack of understanding, or at least of a thorough one, most of those in the marketing field still don't understand exactly why data science events for marketing is great.
Did you know that according to a 2012 survey done by the Fournaise Marketing Group, 80% of CEOs don't trust marketers one bit, and that 65% of them believe that marketers live in la-la land?
Why do you think that is?
It is because marketers often lose the point from their existence, which is the generation of profit, and instead turn their departments into cost-creating machines. That, in turn, stems from their dependence on their own intuition backed by data-gathering methods that have become ancient, to say the least.
In the end, when push comes to shove, executives don't care the least bit about all the marketing jargon that is passed around during meetings.
All they want to know is how much they can make from every dollar invested in a marketing plan.
Data science has come in recent years, however, to change that perception of marketers by enabling them to make well-informed choices and decisions throughout every phase of their entire marketing process.
In order for marketers to actually adopt what data science has to offer them, though, they need to understand their need for it, come to fathom what it's all about, and become familiar with its different usages. Data science events are effective in many ways with regards to marketing, but these three main effects are what we will concern ourselves with here.
Together we will walk through each of them and understand how data science events can help marketing to marvelous extents.
Bridging the gap between data scientists and marketers
Most people who work in creative fields, marketing included, have this misconception about data science as something completely remote from what they are, on both a personal and a practical level.
Not to be offensive to the data scientists, but when most people hear “data scientist” they immediately imagine a madcap geek who has 1s and 0s in their breakfast cereal.
While that might be accurate for some of them, to be honest, that's a quite narrow view of data science.
My point is that people who consider themselves creative, most of all marketers, often distance themselves from the science and math people.
They think that such fields are way beyond their mental capabilities and their practical needs.
What most marketers don't understand is that their whole job actually involves data. Without information about your competitors, targeted segments, available marketing channels, etc., there is really nothing that a marketer can do.
All that data scientists do differently is that they actually approach the matter scientifically.
They basically do it the right way.
In times of uncertainty like the ones, we are living today, what you “think” no longer matters. It's what you know that executives care about.
With industries going down the flush and new industries emerging with wholly new mindsets and mechanisms, every step a business executive takes needs to be thoroughly studied and well-informed. In such circumstances, data science is no longer a complementary field for marketing that would serve well if used but can also be done without.
Both fields are bound by a holy matrimony, and data science events are the ceremonies in which marketers and data scientists get to tie the knot.
The most important thing in this regard is to know that data scientists are not all one and the same.
Like marketers themselves, they don't all just fit any role.
To realize which type of data scientist your brand needs, you need to first understand their different roles and specialties, and ultimately search among them for your catch. In that sense, data science events are not only the wedding ceremonies but also the meetups.
Setting the Rules
Deciding the nature of the relationship between data science and marketing
I'm sure that many people would think this is an absurd point since the relationship is supposedly very obvious.
The data scientists bring in the information that the marketers use to make profits for the company.
What I mean, though, is that there are certain “philosophical” questions, if you may, that need to be settled.
For example, in late 2016, Michael Smith, a professor in information systems and marketing at Carnegie Mellon University, gave a TedX talk at Harvard College where he raised the question about whether big data is killing creativity.
That's not a question posed by someone who is critical of data science.
On the contrary, Professor Smith is the Co-Director of the Initiative for Digital Entertainment Analytics (IDEA) at CMU, which “conducts research into timely public policy and managerial questions raised by the emergence of digital distribution channels for entertainment content,” as described on the initiative's webpage.
Nevertheless, the relationship between data science and the creative fields making use of it, seeing as how this era is the first in which the world of raw scientific methods have intersected with that of creativity.
Traditionally, both worlds were considered completely disparate.
In his talk, Professor Smith commented on the House of Cards experience as a window on the modern entertainment production process.
He compared the traditional creative process as adopted in Hollywood, and the new form of creative production that took off from Silicon Valley with Netflix, epitomizing the coupling of the technical with the creative.
In the House of Cards case that he chose, he pointed out that while a traditional broadcasting network would have figured out that Kevin Spacey was generally a popular actor and David Fincher a rockstar director, what Netflix was capable of was learning who constituted the audience to whom they would produce House of Cards.
Targeting today is no longer done with a “collective” approach, but is rather done on the individual level.
That being said, creativity in the age of data science is customized for a certain specified audience, as opposed to the traditional way by which the creative product is made and thrown out there to find out which audience would choose it, with only a very general idea in the minds of its producers about the “desired” audience.
While the intuitive conclusion that many people might arrive at would be that such tight targeting would be a constraint to creativity, Professor Smith had a different opinion based on a quite valid point.
He believed that because Netflix knew its audience better than traditional production companies would have, its writers had more leeway in terms of incorporating material that traditionally might be deemed too controversial and risky.
To demonstrate his point, he referred to the very first scene in House of Cards where Frank Underwood performs a mercy killing on his neighbor's dog after it was hit by a car, giving us a taste of the mentality by which the character operates.
If you are of the House of Cards fan, you would admire the ruthlessness and pragmatism exhibited in this scene along with the monologue that followed it.
They both show the very essence of the character that actually gave the show its popularity among its fans. If you don't fall into the group that would qualify as House of Cards fans, you would despise Netflix for showing it and Kevin Spacey for acting it.
Showing a dog being mercilessly strangled is not something that traditional entertainment producers would usually experiment seeing as how touchy the idea is.
Because they don't know who exactly their audience is, showing something like that can spark rage against the movie, immediately deeming it a failure.
Thanks to the magic of data science, Netflix had enough information to know that its audience would accept it, and so it went and did it. Having this as the first scene was in itself a data-gathering experiment.
Through that experiment, Netflix got to know who proceeded after seeing it.
Those would be assigned a certain audience group. Those who shut down the show immediately upon seeing it would thus be grouped differently and recommending lighter content later on.
That was the basis on which Professor Smith concluded that data science actually enhanced the freedom of creativity.
Although that case study was in the entertainment industry, the same basic premise applies to content marketing. Data science events facilitate the opening of such discussions and thus the delineating of the relationship between both fields.
Familiarizing marketers with their new tools
One thing we know for sure is that, apart from the tech specialists and data scientists, people from non-tech fields know little to nothing about the immense depths of the world of data science.
The big data wave caught the world off guard, and those who were not prepared beforehand found themselves overwhelmed by a plethora of new tools and mechanisms that they don't know the slightest thing about.
Instead of focus groups and on-the-ground customer surveys, marketers found themselves in front of a world of data mining, neural networks and a bunch of other jargon that on the surface appears enigmatic and impossible for them to grasp.
Even e-mails questionnaires that in relative terms were recently a breakthrough, became part of the Digital Stone Age (if such a thing exists), while offline methods have become too archaic to even consider.
The only thing that a marketer can do offline is play that Google Chrome dinosaur game until they're back online where their actual work is.
The transition of marketers from the era of traditional market research to the data science era cannot be smoothened without such events.
A prime example of this familiarization process came at the IBM Amplify 2016 event, which aimed at introducing the various aspects and uses of the Watson Marketing platform.
A demo was made for each of the marketing tools provided by Watson, IBM's AI quantum leap that was introduced in 2010 to serve as a natural language question-answering system.
In the event, Melanie Butcher, who is the head of IBM's User Experience (UX) Design Studio, ran a conversation with Watson where she played the role of a marketer for an outdoor sporting goods company. The purpose of the conversation was to construct a social ads campaign aimed at encouraging people to go to work by bike.
M: Watson, I'd like to target a new audience for our paid social ad interaction.
W: Ok, how can I help?
M: I'd like to start by understanding who my existing buyers are for a similar category. Watson, show me how many customers have purchased from the road-bike category in the last three years.
W: 18,681 customers have made a purchase. Here are the details: —-
The conversation then continues between Melanie and Watson the Computer in smooth English as they define their audience, create a lookalike audience group, and find ways to target their audience.
That's done based on Watson's suggestions, all of which were in turn based on the data gathered, analyzed, and presented by Watson himself. When Watson found out that one of the images in the content they put together for the campaign was not performing well, it pointed it out and suggested replacements for it.
This presentation demonstrated to the marketers in attendance how a marketing team can collaborate with a machine like Watson in the creation of a full-on ad campaign all based on extensive and concise data.
“I really need you to engage with us. Share your thoughts and perspectives,”
then followed Kareem Yusuf, General Manager of Watson IoT.
“Where do you see cognitive technologies working for you? How do you wish to interact with them? How does it fit into your tools and way of working?“
Data science events play a key role in posing and answering such questions, which otherwise would remain unanswered and perhaps even unthought of by the marketers under their own steam.
It is for these three reasons that every company wishing to take the right approaches with regards to marketing its brand has to engage in and even organize data science conferences.
You need these events in order to understand that massive field better precisely where it concerns your business and to get to find the right data scientists to fit your purposes. And then, get ready for the harvest.