In the olden days
In pre-modern times, businesses, to boost their sales, used to dispatch their salesmen to areas where “they thought” the probability of finding a customer was high. There was always the question of “they thought”. The salesmen were not backed up by data and information as to which house, or which street should be a high priority. The salesman, having undergone no filtration and sorting out of potential buyers, set out, going from one house to another, in hopes of selling his product. This way the salesman had to encounter many such people who had no interest in the product, some who had little interest and others who had interest but could not afford to buy owing to their financial strains.
Had the salesman known before time that some of his targeted people had zero interest in his product, he would have never reached out to those people, thus, saving time and energy. He could have utilised this time and energy later on, in going to an altogether different place to sell his product. But, unfortunately, in pre-modern times, there wasn’t any platform that could provide them with such accurate and precise information.
Arrival of the Internet
With the arrival of the internet and most importantly, the social media, big chunks of data made its way to company servers. People gave, and are still giving the social media companies important information, also called their digital footprints. As more and more people started using social media, bigger amount of data appeared. This data was a game-changer.
Experts began to realize that this data could be used to achieve heights of marketing that were unimaginable hitherto.
This data on users, would tell lots about them. It would tell a company what product is the user interested in, which brand does he prefer to wear, what time is he most probable to buy products, from obvious details to the very minutest online behaviour of the user. When you know what a user is searching on google, you can easily guess what product he might be interested in or looking for in a certain time frame, and this information, in turn would help you guess which ad would he most likely fall prey to.
This data on users is what companies nowadays are looking for to enhance impact of their marketing advertisement campaigns. With the transition from traditional salesman-styled marketing to modern marketing approach, the role of salesmen has been reduced to zero, and its task has been taken over by social media and internet companies like Facebook, twitter, etc. which propose their data of users to the companies and allow them to display ads on their platforms.
These ads that companies display, is not displayed randomly to any one on Facebook (for instance), it is rather displayed to only those who are more likely to be affected by the ad. This likeliness, again, is something that data of users can help determine, whether a person is a football lover or cricket lover depends upon his Facebook usage data.
Let’s understand through an example
As an example, let us imagine that 100 people are to be approached by a company for advertisement of a product. On the one hand we suppose the company uses the conventional method I-e dispatching salesmen to 100 random people in a town. On the other hand, the company undertakes a modern way and uses Facebook to display advertisement to 100 people.
In the conventional method, we don’t know how many of the 100 people are likely to be interested in our product because we don’t have any data on them except their location. Therefore, there is an element of risk involved in this method. It might happen that all of the 100 people would be interested in the product, equally probable is the possibility that none of them shows interest in the product (and in this case, the expense of this advertisement campaign overweighs the output)
Conversely, the modern approach reduces the element of risk to a bare minimum. Because Facebook has enormous amount of data and insights on every user, it would make sure that all the hundred out of hundred people selected for ads are those who are genuinely interested in the said product. And UP goes the chances of them ending up buying the product.
This way the modern way of advertising, whose backbone is ‘data’, would naturally be the choice of the company. And nowadays, as companies are crossing Billion-dollar net worth figures, their marketing budgets have also plunged up to millions of dollars. That is why, with so much money at stake, no company would want to go with a model that is shrouded with risks.
Role of Data science
In all what has been discussed until now, one thing seems to be missing and that is: Where does Data Science fit into all this?
Well, the most important role in the transition from traditional advertisement to modern advertisement is the role of Data scientists. How do the companies know that such and such product would entice so and so person? How do they group people into different categories with only their behavioral data available to them? This is all a blessing of data science. Data science, with the application of programming and statistics manipulate and polish the raw data into such a form which is makes decision making very easy and almost risk free.
Example of a restaurant
For example, let us suppose there is a fast-food restaurant. How would a data scientist, or, data science in general be of help to this restaurant? A data scientist would first check on company’s sales records and find out those places where these restaurants sales are higher than other places. He would visualise the data I-e make different graphs and charts that would show that in some specific places the restaurants business is growing while in others its plummeting. This data would compel the restaurant owners to reconsider their marketing approach and take steps to also enhance their sales in those areas where it is plummeting.
The restaurant owners may also derive insights from the successful areas by observing the factors which enhanced their business in the successful areas. This is a very ordinary and basic example of what a data scientist can do to the growth of a company. Data science have evolved to greater heights. Companies now hire data scientists to address very serious and decisive questions of their business. The fact that data of users is made usable today for marketing purposes, is made possible by data scientists.
Example of a tech giant – Facebook
Facebook is another good example to discuss the role of data scientists in businesses. The fact that it is very easy for businesses to find out potential customers using Facebook advertisements is due to data scientists. It is the data scientist who, behind the curtains, is writing codes and algorithms to display the right audience for any business. As explained above, Facebook has enormous amount of data on every particular user therefore they have to hire data scientists to handle the data and polish it.
Data gathered from Facebook users is in the form which cannot help in decision making of any sort. Data scientists have to apply techniques of probability, statistics on the user’s data to reach a conclusion as to where does the user stand in terms of his interests and behaviors. Or based on his watch-time, likes, shares, in which category do we group him? With those who like football? Or cricket? Or buying sports shoes? As mentioned earlier, these details varying from general attributes like a user’s location to the most subtle details, is the backbone of today’s capitalistic world, and the contribution of data science to this capitalistic world is indispensable. It is data science that does the lion’s share of work in this new age of marketing.
Conclusion
Data Science uses applications of fields like AI and programming to analyse data and shape it to a form which makes decision making on it easy. With good analysis of data, companies can take better decisions for future and take such steps that are utile for its survival in the highly competing market. Data scientist is the future-teller of the modern world.
In 2011, Harvard business school called data science the sexiest job of 21st century. Afterwards, an increase was witnessed in the amount of people opting for data science as majors, but overall, as compared to other fields, the figure remains low and companies till now feel there is a shortage of data science experts.