The Role of Big Data & Data Science in Today’s Marketing World

The Role of Big Data & Data Science in Today’s Marketing World

Back in the day, Marketing was a top-down process that is creative-led. We call that “Mass Marketing”.
Where some brilliant minds get together and come up with slogans and phrases, which will push down
to the audience hoping that they will resonate on it. It’s creative, but basically it’s guesswork.

They had focus groups to try on the idea first. But that was not enough. People who may look similar
from outside often want and respond to completely different things.

Let’s look at a few great failures back in the Day.

In 1984, Pepsi market share was gradually increasing and if it continues it was going to overtake
Coca-Cola in a few years. So Coca-Cola conducted marketing research and identified it was the “Taste”
that reduced the popularity of Coke. So they developed a new formula which has more sugar than the
old Coke and Pepsi. And launched in 1985 while discontinuing the old Coke.

It was the costliest marketing mistake in history and received backlash from consumers and the media.

 So what went wrong?

Brands are more than lists of individual physical characteristics. And people’s brains respond to reassurance and conformism that associates with a brand. In the USA, Coca-Cola has a symbolic meaning and it is seen as a cultural icon by some consumers. Most of the customers prefer tradition and stability over novelty. Coca-Cola just focuses on one attribute and customers feel a sense of loss with the discontinuation of the old Coke.

Let’s look at another failed attempt of the rival to Coca-Cola. In 2017, the soda giant Pepsi released an ad featuring TV Star and Model Kendall Jenner.

It was a scene of a street protest where Kendall Jenner joined and tried to defuse the tension between protesters and police by handing a Pepsi to a police officer. This ad triggered a firestorm of anger and outrage.

What went wrong?

Because people felt it underestimated important topics like racism, police violence, and Black Lives Matter. People protest because they disagree with something or outraged, worried, and even scared. This ad makes it look like protesting is a hip thing young people do for fun. So it came out as incredibly insensitive.

This kind of failure can be catastrophic. When they realize things went wrong, they have already damaged the brand name, and probably their competitors will mock them and use this chance to increase their market share. Not to mention the huge money loss.

 So what’s the solution?

Marketing and consumer research has proved that it is not simply effective or feasible to influence everyone with the same message. Therefore, the targeting of smaller subgroups comes into practice, which brings the term Market Segmentation or Customer Segmentation. Market Segmentation is the technique that divides a broad target population into smaller groups or subsets with similar needs, interests, preferences, and characteristics. In addition to that, the individual needs to respond in a similar way to pitched marketing.

We are talking about 4 types of marketing segmentation.

Now let’s look at how Big Data and Data Science come into play.

Before that, we have to talk about Data. Right now, Data is the most valuable asset in the world. Which already surpasses Oil. Whenever we approve a website cookie to monitor our online behavior or allow a mobile app to access our personal information, it collects all our digital trails. Not only the platforms and applications we use, but also a lot of personal information about us. This allows them to track us online, tracking the time we spent on sites, the locations we have visited, Payments we have made and reviews we have left. You may have already figured out that Google is reading your emails and chats with your permission.

Did you ever wonder about how this data is being used?

With this collection of Geographic and Behavioral data with Psychographic profiling, this platform obtains highly detailed insights about their users. And also they will be able to accurately predict and influence its users. These platforms now know “who” are you and “What” you do. And with Psychographics, they can predict “Why” you do it. Psychographics is the new weapon of digital influencers.

Now you understand this Psychographic information can be used to effectively target consumers. If we take U.S. retail company targets, they use predictive analytics to predict customers’ current life situations. Using demographics and psychographic data with search queries and historical product purchase patterns, they can predict when a female customer is pregnant or when someone is going to marry. Likewise, this allows a company to predict customers’ life events and target specific products to the correct people.

Not only that. Psychographic data analysis can be used to influence attitudes and beliefs which can be used to sway the votes. Using demographics, geographic, and psychographic data, they can differentiate the voters into target groups. Then they will pass the tailored messages to improve the communication and sway their decisions. Different variations of the same message can be used to bring all the members in a single family to the same belief and motivation as the way political campaigns are needed. It is believed that Cambridge Analytics used this technique to support the 2015 Brexit campaign in the UK and the 2016 Presidential Election in the U.S.

This is all done by running machine learning algorithms on the Data that is extracted from you, where they are able to predict your personality and behavior to razor-sharp accuracy and adapt content and advertising to fit your persona.

Instead of Mass Communication like back then, today’s communication is becoming ever increasingly targeted. It’s been personalized for every single person which is highly effective and saves a lot of money by directly delivering the correct message only to the right audience. But there is a question mark with this growing technology.

“Are we being controlled? “

Sankha Jayasooriya

Lead QA Engineer


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