IIn many places, we mentioned on Machine Learning, Artificial Intelligence, and relative data sciences that are newly gained influence with the fourth industrial revolution. Using those tools to discover new patterns and/or predicting what is going to happen with certain inputs is now invasive enough find its place among various sections of any sector. Repetitive actions, time-consuming works, and workflows that need different tasks from different points are now easily described while almost all possible outcomes are being predicted. Thus, ML helps you with all those abovementioned processes in almost every aspect or section of the job.
Marketers using AI is not a new thing; in fact, preprograms designed by marketers using basic conditions such as “if” and “and”, where automated emails are working by a little intervention were widespread enough to increase marketing productivity of the campaigns. However, being limited automated actions only does not solve the efficiency problems arose. That is, even though using Artificial Intelligence on marketing campaigns to automate simple actions gives a lot of extra time, it is limited to human intervention; thus, it is prone to human-caused errors. Therefore, as for the other sections of the operations, in marketing too the gap between human experience and machine learning can be closed.
The tools of marketing using machine learning algorithms are limitless as in deep learning for other operations too. However, in marketing descriptive and predictive nature of the ML becomes the core part; thus, cluster analysis of customers so that guess work can be cut from the predictive part of the marketing strategy as well as the segmentation of the customer groups can be provided with ML factors, and descriptive and predictive analysis of customer behavior may be done through ML based algorithms to reveal the future path of the customer behavior; thus, offering right thing at the right time can be done, all and all, this is what marketing concept tries to achieve.
As time passes, the examples of the ML based strategies in retail and marketing widens. The future tools are now being considered as close as ML integration to any system. Until recently, even ML was used as simple predicting of what must be shown to the customer regarding their past behaviors. However, now things are a little bit out of hand since ML algorithms are now thinking outside of the box, unlike many people. That is, keeping aside the recommendation engines for Amazon, Netflix, Spotify and such, now the IoT, AI, and ML based cloud systems are collecting your permitted data to predict whether you want a new fishing rods or not regarding your past search on fishing boats. What is more that engines now predicting whether you are pregnant or not by your past purchases or searches .
Therefore, various marketing strategies showed that ML based marketing is becoming widespread as well as successful. The improvement in the performance and the effectiveness can be observed with little bit of simple inspection; however, the deeper analysis showed that ML based strategies are increasing productivity since it points out where you should look at. Also, they are improving your forecast accuracy thus encouraging better risk management since descriptive nature of it helps you to understand what is going on in the market whereas predictive nature of it encourages you to foresee possible outcomes.
So far, the relation between a few operations and the ML was clarified. It must be adequate to understand that the ML, which is more beautiful and intelligent offspring of AI, is the core element of the Industry 4.0. Therefore, using it almost in every possible situation would generously maximize input/output ratios. The marketing aspect is mere one of the examples where the steps were taken timid in the past; but, are much wider now.