6 min.
Why is AI essential to your future as a marketer?
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Why is AI essential to your future as a marketer?

Business Strategy Data Science & AI Marketing Artificial Intelligence Marketing

The Big Data revolution in marketing makes artificial intelligence an essential tool for marketers to stay competitive. But what is artificial intelligence (AI) marketing and how can we benefit from it?



In order to understand how and why artificial intelligence is an indispensable marketing tool, it is essential to first define what marketing AI means. Currently, it is clear that a host of definitions about it are circulating on the web, which can create confusion as to the true meaning of the term.

In fact, an ideal definition of AI Marketing  should cover all branches of activity related to this field, rather than being limited to certain specific branches. It should also illustrate the contribution of artificial intelligence in the world of marketing, by demonstrating that the second element is empowered by the first. 

In his book,  Artificial Intelligence Marketing and Predicting Consumer Choice , Dr. Steven  Struhl defines artificial intelligence as " anything a machine does to respond to its environment and maximize its chances of success ." In the context of marketing, he explains how specialists “  [U] ntimately aim to identify the complex links that we fail to detect and that allow us to make better decisions. »  

The definition of AI Marketing presented above seems to be one of the most complete and understandable to explain this new hybrid field. For marketers, the last part of the definition on the ability to identify undetectable data patterns is exactly where the contribution of marketing AI can bring significant business value.

In general, the world of marketing, and more particularly that of digital marketing, collects a considerable amount of  Big Data  that must be managed and transformed. The four key things to consider in  Big Data  are: 

  1. Volume 
  2. Speed 
  3. The variety 
  4. Veracity

Ultimately, the role of data science and  machine learning  is to analyze all the data collected in order to draw reliable conclusions to optimize the commercial value of a company. This gain translates into use cases such as sending personalized emails to subscribers, the use of automated conversations ( chatbots ) on social networks or optimal targeting of customers in the context of programmatic advertising. It can also translate into consumer research to identify their real needs. Ultimately, Marketing AI  aims to improve the entire marketing lifecycle, from thought to action.



As Peter Drucker famously puts it, “The important and difficult work is never knowing how to find the right answer; it’s knowing how to find the right question.” However, your ability to find and ask the right business question depends above all on your ability to know what you don't know. In short, the more you know about what you don't know, the more you will be able to discover what can really increase your marketing performance. Obviously, this may seem easier said than done, especially when the only tool at your disposal is an Excel table or, at best, a set of SQL (Structured Query Language) queries in order to exploit your different data base.

These tools proved sufficient in the  “pre-GAFAM” world  ( Google, Apple, Facebook, Amazon and Microsoft), when most of the data collected ended up in Oracle databases or Excel reports. However, today's marketers are now bombarded with a long list of analytical data or data from Ad/MarTech platforms, each with its own set of complexity and challenges in terms of integration. 

For example, although most marketing analytics teams might not dare to admit it, much of the data collected through Google and Adobe Analytics ultimately gets tossed to the wind. The reason is simple: considering the ocean of dimensions and metric data gathered to be analyzed, an individual can't help but bet on a small part of them only. Otherwise, faced with an avalanche of columns of data, the human brain is not empowered to absorb and analyze such a large amount of information to draw valid conclusions. 


Your analytics team may choose to focus on dimensions and metrics they already recognize and know, a phenomenon commonly referred to as "descriptive analytics" or "know what you already know", or spit out all the data collected in an Excel table similar to a nice big extra large pizza all dressed and filled with rainbows. While the second method is known as Data Puke , the first often leads to biased, erroneous, and often worthless interpretations and conclusions of data. 

That said, no need to blame your BI ( business intelligence ) or analytics teams if this happens. In fact, digital marketing is based on Big Data that humans are simply not able to fully analyze. Even if you have invested significant sums of money for the implementation of platforms able to collect ALL the available data, the fact of thinking that your team will be able to gather them and make a detailed analysis via an Excel table is a bit like asking a mouse to eat an elephant…  However,  this does not mean that humans are now useless in this process. On the contrary, when most of the work is completed by an algorithm, humans then turn out to be particularly good at analyzing the data and extracting  relevant insights  . In short, if you provide your BI teams with the right data science and artificial intelligence tools, your company can definitely benefit from the best of both worlds and develop both a successful analytical program and culture for your organization.



For some of you, what I am describing may still, no doubt, seem rather utopian. Concretely, you could already be completely satisfied with the current state of things, that is to say a marketing analysis system exclusively based on Excel or SQL. After all, if this one works until now, why should it be repaired? You might certainly doubt the need to integrate marketing AI into the black box models you already use from various MarTech solutions. Besides, using AI is definitely too complex and expensive, isn't it? 

Assuming this is your current position, as we intend to demonstrate in future articles, you may be surprised to discover how AI marketing is easier than you think. Afterwards, dare to pause to reflect on the following questions and imagine a scenario where your biggest competitors have already begun to integrate AI into their marketing practice and culture.

  1. Have we reached the full potential of our marketing data? Are we at 80%, 50%, 20% of their potential? Do we even have the faintest idea? 
  2. How much revenue does our marketing data really represent or hide?
  3. Do we believe that we will be able to remain competitive in the long term using only Excel reports and SQL queries?
  4. Can a good marketing strategy compensate for a bad reading or an incomplete reading of the data?
  5. If everyone is now using the same AI solutions offered on a turnkey basis on platforms such as Facebook Ads and HubSpot or through programmatic ads, how is AI still a competitive advantage for anyone?

Before answering these questions, I invite you to look at the statistics related to the consequences of wasted marketing investments annually due to poor data exploitation or incomplete data:

Incomplete data

At present, the statistics presented above unfortunately apply to most companies and their marketing department. On the one hand, this means that you still have time to bury your head in the sand and contemplate what I call “ the big data marketing problem ” while standing still. On the other hand, time is running out and it can be particularly interesting for a company to know how to perceive this problem as a great opportunity to be part of the precursors in the field. This eventuality involves the rapid integration of AI Marketing  within your marketing and business practices, a concept better known as augmented analytics .