How Machine Learning will revolutionize Marketing and Advertising
By — Sudip Bhandari
One of the major subjects concerning the future of technology and other various fields associated with it today is: “How is Machine learning and AI going to affect X?”. There are worries about its regulation and usage and in the same time there is overwhelming optimism about the good things that AI and ML are and will be capable of accomplishing.Artificial Intelligence itself is a broad Umbrella terms encompassing various fields of study like Machine learning, Data Science, Statistics and so on. Sure, Machine learning is hyped up now by industries and academia alike and there are skeptics considering the fact that big companies like Google, Amazon, Facebook, Microsoft are looking for new ways to incentivize on this opportunity. The entry barrier to machine learning and Artificial Intelligence has been decreasing and we are witnessing the incorporation of Machine learning into various fields. As the web continues to proliferate and the sheer volume of data outgrows the capacity of our computing machines, it’s various clever machine learning and optimization techniques that are going to be of any help. We have a limited computing resource (computers, programmers included) and the problem space is unlimited and is bound to increase non-stop which calls for a new paradigm of problem solving and engineering. We are getting to witness that machine learning is that new paradigm. Out of numerous areas which could leverage the power of machine learning like finance, healthcare and medicines, education I am going to focus on marketing and Advertising which have a very huge influence on our lifestyles in this digital age.
Media and Marketing:
Marketing and Advertising have been flourishing since the advent of digital media at a rapid rate. First it must have been the Radios, Televisions and the newspapers where advertisers could reach out to millions of people instantly. After the world wide web was built on top of the Internet a whole new door of digital media lay open. Web became the place to advertise. Big giants like Google and Facebook’s primary source of revenue is advertising. Web didn’t limit itself to pages. Streaming services for both videos, audios and now Virtual Reality (VR) and Augmented Reality (AR) sprang open. Web started becoming more and more personal to different users and this is where we are witnessing the power of machine learning.
Relevance At Scale:
When advertising and marketing something in a giant pool such as the world wide web one of the problems that has baffled many marketing and advertising gurus has been how to maintain relevance at scale. Producers want to reach out to maximum number of audiences with their products who might actually be interested. As a general individual spends a majority of time in the internet doing transactions, visiting blogs and news articles, engaging in social media, buying stuffs online, interacting with bots there are innumerable ways s/he is sending her signals. These signals when captured efficiently, pruned and processed can give us a very coherent and detailed digital persona of the individual. Making maximum use of these signals we can create a machine learning model and use it to target consumers with products they might be interested in. In fact we can see that this is already happening.
As we browse the web our digital identity is almost always tracked gathering more signals about what we like. Marketing and advertising platforms can use this data and in real time they can advertise the suitable products to us. In fact it’s not surprising that you search for some commodity to buy in Amazon and when you log into facebook you can already see the advertisements showing up in timeline.
One huge limiting factor in conventional marketing and advertising is Market Analysis. Still majority of the companies have ‘Sales and Marketing’ guys doing laborious manual work of market analysis, working over excel sheets, chasing targets. This process is very inefficient and error prone. There is a huge cost involved which is always at stake of the effectiveness of the outcome of whole operation. Us human beings can be trained in a particular domain and get really good at it but the problem is domain keeps changing almost every single day. There are way too many signals out there and to decide which one to pursue and which not to is a very big decision and involves a lot of risk behind it. With such a huge dataset of very high variance (too many features, we can’t afford to lose some and can’t come to conclusion with many features and few observations) it’s only sub disciplines of machine learning like Deep learning that can come to rescue. The whole realm of market sizing and analysis can be automated using machine learning techniques.
Easy Infrastructure, Easy Entry:
One of the major advancements in the machine learning in the recent years apart from emerging new techniques and ideas has been the infrastructural development. It has become relatively easier if someone wants to foray into machine learning and leverage it in their product solution architecture. We have ready to train and deploy cloud infrastructures like Google’s Cloud ML Engine. We have various well tested frameworks and libraries open sourced if we wish to build our own ML solutions and tinker under the hood. Google’s Tensorflow and Python Scikit library are the perfect example. Even for mobile devices we have lighter versions of these frameworks like tensorflow lite which work by utilizing the power of cloud infrastructure. As we advance further in this digital age, its becoming more and more likely that even a basic understanding of Machine learning and it’s applications can give an individual a prophetic power and a vision that’s more likely to yield fruitful results.
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About Author | Sudip Bhandari
I have been working as a software engineer for about two years now. I have finished my undergraduate studies in BE Computer Science. Currently pursuing Udacity’s Machine Learning Nanodegree. Apart from programming I am interested in music. I want to be a ML researcher and build ML solutions to various problems.
