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    Machine Learning; 7 Algorithms In Digital Advertising Platform [Part 2/2]

    1024 632 Apurv Lungade

    Click Here to read part 1 of this blog

    Few points which can help Ad-Network do more business with same setup [macro-optimization]

    Dear marketers and brands, please ignore whatever you read in this section 🙂 Helping brands reach & interact with their consumers is the most important thing; similarly, it is equally important for an ad-server to make money out of it & meet the ROI. There must be a sweet spot between these two goals – and when an ad-server system achieves this, it is a win-win situation for both – marketers/brands and the ad-server agency/network.

    5. Dynamic Floor Price

    80-20 rule says, 80 percent of revenue is generated from 20% of the clients (brands). In an ad-network ecosystem, the rule is a bit different – 90% of the revenue is generated from 10% of the SSPs. It is a game of demand and supply where ad-server system is the referee. When all demand players land the battlefield, they all want to hunt THAT user (most relevant & likely to interact with the brand) & ad unit. This is the opportunity for a referee to change the rules of the game and make it expensive to yield maximum.

    Yeah, it is very cool thing to have an any RTB based ad-serving system; but there are two BIG challenges over here:

    1. It is not merely the site content that makes brands attract and bid higher, but it is majorly the quality and relevancy of the user that plays the vital role. So, defining a set of discrete rules to raise or lower down the floor price won’t help here. Again, Machine learning algorithms which continuously track demands at user and ad unit level would only help tackle the problem.
    2. Second challenge here is – if your ad-server keeps raising floor price then at one point in time, no/very few bidders will bid for the ad slot and most of your inventory will get unsold. And once your system gets a “Expensive” label, it becomes difficult to retain and gain more demand. So, the system should be intelligent enough to know if demand is consistent and the floor price is just below the optimal point beyond which if it is increased, they are not going to be satisfied with.

    6. Know Your Inventory Treasure

    What if your ad-server is not RTB based and still want to take benefit of variable pricing? Well, there are ways.


    ML algorithms can keep a watch on inventory parameters like:

    • Site/Brand Popularity – based on trending/viral content being published
    • Monthly Traffic
    • Alexa Rank
    • Content Quality – based continuous sentiment analysis, reader engagements & sessions durations
    • Audience Quality – it is completely based on user’s response towards ads being served to it – it is measured in KPIs like ads visibility, user interaction with ads, clicks, leads and brand engagement
    • Inventory Type – it can be anything – a social media platform, SEM, websites, mobile app, push notifications etc. but the behaviour of each one of these is different – ML tracks the changes in the behaviour

    So, the ultimate mantra here is to make marketers spend more on inventory in demand.

    7. Platform Secrete Survey

    It is ad-servers responsibility to make brands happy with quality performance of ads, best picked inventory and reach the unreachable audience. Similarly, the usability and experience of platform plays an important role to make brands happy and helps retaining them for product lifetime. So, the goal here is to understand user’s behaviour on the platform and mould the platform accordingly.

    Make FAQs interactive as if a human is interacting with the user. This requires a very popular machine learning algorithm – Natural Language Processing. Again, a heavy piece of data required here to make system precise and accurate while answering the user’s questions.

    Another example would be to track user’s interactions through click events, time taken to complete a process – say setting up a campaign. Using this data, system can make inferences as in – which features are most favourite, which are very rarely being used, which processes are time consuming and which quick ones. All the inferences made by the system if it consolidates and conveys to the project/product manager, they can work on the pain areas and work towards the betterment of user experience.

    Machine Learning; 7 Algorithms In Digital Advertising Platform [Part 1/2]

    945 427 Apurv Lungade

    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