How the marketing-data science relationship has changed – for good

0
100

By Kirill Eremenko


As you’ve probably seen, (for the past couple of years now), data science is here and it’s here to stay, it is taking half the world by storm – and there is really no going around it.

A couple of years ago, we could’ve covered up our eyes and pretended as if this was another fad that would pass by and probably be gone before we knew it. Or even worse: marketers could have pretended that marketing and data science were two separate fields, each working in their own area and if they somehow interacted with one another, it was a good to have, not a must.

Nowadays, it seems as if this relationship has almost entirely flip-flopped. What used to seem as if data science trailed behind marketing, now looks like data science is behaving as the guideline towards which marketing should go. Now, it would be difficult to contradict such an assumption.

The reason is simple: data science allows marketers to target and understand their audiences like never before. With the power and critical amount of data being produced every day (to put things into perspective, we are creating approximately 28,000 gigabytes of data – every second!), there is very little that is unknown from the consumer that the average data scientist can’t analyse, organise, extract, modify and most importantly: understand.

If you add into the equation that the internet of things is practically upon us, the sheer connectivity of devices will probably dazzle and confuse the everyday consumer – hundreds of appliances and everyday objects that are constantly used are feeding, updating and recording data 24/7.

Think of what a person with the right tools to manipulate and understand this information would be capable of doing- nothing short of personalizing ads and products for every single individual out there.

Theoretically, with the right “aim” from a data scientist, a marketer could in theory not “misfire” any (or fire less) of his or her shots, saving them headaches, financial resources and campaigns that could’ve been better targeted to a better audience. Any marketer out there will understand the importance of this, it almost seems too good to be true: a sure-fire way to aim at your target audience, understand them and probably mean an even higher conversion? – jackpot!

It is obvious that the relevance of data scientists has increased over the years, and there is a good amount of marketing teams that are aiming this way. But how is the relationship the other way around? Are data scientists effectively (and actively) looking at what they can gain from marketers? Data scientists know that they are the “new kids on the block”, but this doesn’t mean that they should underestimate what they could learn from other professionals around.

This post has no meaning of creating a division between these two fields of study. As a matter of fact, it aims to do the very opposite. Both fields could actually benefit from the other’s strengths since there are many characteristics from one another that could make you stand out in the other field.

As we’ve made a point before, Marketers can really benefit from the information provided by data scientists, but there are other aspects as well, from the profession itself. Data Scientists (generally) bring a great deal of analysis and evaluation of a given subject, as well as a holistic view of a problem. Data is often messy, so sometimes it is necessary to “clean it up” in order to be able to view what is really going on.

Data scientists on the other hand, could greatly benefit from the communication and presentation skills as well as the natural intuition of marketers. Data science is not over once the analysis is completed, there is a crucial aspect that is often overlooked: the presentation stage of a data science project. What use would it be to realize incredible insights from a tremendously huge amount of gathered data, if it can’t be correctly communicated and most importantly: understood. If a data scientist can’t correctly transmit their message, then half of the work that was done is practically invalid. Data scientists should aspire to be considered as the “bridge” between data and the business world, in order to transmit the analysis and insights they gather into actionable strategies for the overall business.

Instead of creating a division, we aim to call for a “new” version of professionals, some (such as digital marketers’ Ryan Deiss) mention that there has to be a paradigm shift between the traditional way of understanding the marketing side of a business and transition to a more appropriate one: the Growth Team.

In short: the Growth team involves every aspect of a company that could be somewhat related to the expansion of the business in any way or form. In order to achieve this, there should be a change in terms of the traditional ways in which the teams were set up before, a division between “Sales” and “Marketing” teams no longer seems beneficial for the company, and neither does a separation between Data or Analytics from the “business” side of things. Any marketing team would be lucky enough to make itself stronger with the accurate aim of a data scientist.

Modern times call for modern measures and a new player of the “growth team”. It is important that we emphasize and enable a symbiotic relationship between these fields, both data scientists and marketers have a lot to learn from one another, and the direct impact and results will be seen all over any given business.

Kirill Eremenko, pictured above, is the founder of the data science academy SuperDataScience and the author of new book Confident Data Skills, published by Kogan Page