Machine learning as the term implies, is the process of making machine learn on its own and make the decisions the way human brain takes. The learning process includes collection of information, reasoning for conclusions and self-correction. These algorithms are not limited to any specific industry or nature of business or kind of a product/platform.

Have you ever wondered, how Flipkart knows your choice and recommends a list of products which becomes very difficult for you not to buy them? How does Uber Eats know exactly when the delivery boy going to meet you and shows estimated delivery time?

All these are epic examples of machine learning algorithms. Be it Google, Uber or Flipkart, the systems are trained in such a way that they analyse all data points and come up with the most relevant results/suggestions. The process is continuous evolving and producing day by day better results.

Few pointers which can help brands reach the unreachable [micro-optimization]

1. User’s Browsing Journey

The most effective way to know a user’s choice is to think the way they think. Machine learning algorithms can trace the user’s everchanging choices and connects the dots to make a pattern. The continuous process makes user see the content and ad of his/her choice and thus increase in upselling a product or a brand. It may sound like traditional user categorization technique but the moral difference here is, categorization is a discrete method and it does not follow a pattern what ML algorithms do. Some platforms have taken a step forward and tried to build advance categorization by introducing scoring logic to each category user falls under. But again, bucketing a user in many categories makes the data skewed and this in turn leads in less accurate results than ML produces.

2. Audience Cloning

Continuing point 1 wherein machine learning algorithms continuously track user behaviour and makes a patter out of it; the process is applicable to all users in the network of an ecosystem. Once the system has a substantial amount of data, it can create samples of users having majorly same choices, interests & browsing patterns. These patterns change continuously with the change in audience counts and choices.

The best example of this algorithm is – Netflix

You have watched Sacred Games, Riverdale, 13 Reasons why, and suddenly you get a notification saying “Top Pick For You: Little Things”. Now, if you notice, all for web series are NOT of same a genre – so, it is definitely not picked by tracing your past taste. Netflix has got a huge user base and sampling those made it possible. Its ML algorithms continuously sample these users of same taste and try to suggest the unmatched shows across users in that sample – hoping that having same taste among these users, may also like the suggestion made by Netflix’s ML algorithm

3. System Suggestions

Have you ever seen a system talking to you? Yes probably – Google, Alexa, Siri etc. What if your advertising platform suggests you how to optimize your ads? Yes, it is possible with Machine Learning algorithms. System slice and dice the big data and correlates content’s meta data like – keywords, urls etc. That’s why, you type a single keyword and system suggests multiple around it.

If you notice here, there can be thousands of keywords relevant to Virat Kohli but, system filters out the recent ones – This only possible when system learns the publisher content on continuous basis and this is the beauty of ML algorithms over traditional keyword suggestion techniques.

Another example would be – system automatically crunches the inventory and user behaviour data for last T hours and suggests you change targeting accordingly.

4. Analytics with suggestions

Sometimes it is very difficult to define KPIs for you campaigns and taking decisions out of it. For video & rich media ads it can be views, engagements, sentiments and share of voice whereas for Native or Emailer ads, it can be CTR, eCPAs etc. What if you are using a comprehensive system which provides all possible stats of all possible ad formats and dimensions around these? It will be a mess! A straight away solution over this would be to have separate systems/analytics dashboards for separate ad formats – but, this will only help you analyse data separately and join the dots manually

It is highly possible that, you reach your consumer through more than one channel & for that matter, having different systems to analyse those will never tell you the common user specific insights. Machine learning probabilistic algorithms can predict and identify common users from different channels and their responses towards your brand. Moreover, by using these insights, you can re-target your consumers through different channels; as a flip side of it, at some point in time consumers may experience it intrusive if they feel it irrelevant or disturbing. So, it is very important to continuously slice the user specific data and fine tune your campaign settings accordingly. ML algorithms can make it happen in a single dashboard with suggestions in it.

So, in this episode we have seen how ML algorithms can help in betterment of user’s ad experience. We will see how ad-servers can make use of ML to get maximum of it to grow the business and make a responsive platform in the final part of this post. Click Here to access the final part