How An Ad /Content Targeting Engine Is Built

Soumya Kapoor
Aug 26, 2019 · 8 min read
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We often come across ads/stories when we access Facebook, Instagram or other web apps/games like Candy Crush etc.

Ever wondered why and how this relevant content reaches us?

If we try to devise a user story as a Product Manager, we’ll surely be able to come up with something in terms of who to target, how to target, when to target and where to target, with what kinds of ad.

So, skipping this step for now, we’ll try to look at what an Advertising Engine’s high-level architecture would look like.

In order to arrive to our vision, we need to start with a set of mandatory activities for laying out the foundation of our product. The foundation shall include the following list of capabilities:

1. Message Frequency

How often should one message/email users?

Once the system is automated, there can be a growing urge among marketers to start sending messages, emails or notifications to the users very frequently. This may result in frustration for the target users. So, there is a need for a feature to learn the optimal message frequency and repetition for each user to increase the metrics like CTR (Click Through Rate), CvR (Conversion Ratio), etc. The recommendation space / duration should be limited in a way that it doesn’t overpower the user’s experience with the application in question.

2. Channel Selection

Which channel(s) best help connect with the user

It is very important for a marketing strategy to be exactly where the users are. With growing innovations in the digital world, users have a large number of available channels to interact with a product (phones and desktops with different operating systems). So, in order to increase the reach and sales, we need to run multichannel campaigns. However, just reaching out to a large number of users is not enough. We need to ensure not only that they receive the messages, but also take the expected action. Hence, we need to have an intelligent product that selects the right channel(s) to reach a particular user based on their past browsing behavior on different devices (given that we have this data already).

3. User Segmentation

Targeting the user by defining and categorizing behavior

User Segmentation refers to grouping of users based on certain properties. The properties may be gender, age, geographical location, interests, buying behavior, channels used, economy, polarity etc. Segmentation is useful for targeting users with relevant offers to upscale sales. But, with the growing size of user data and preferences, it can be very demanding for a human to segment users manually. We know that there are various clustering algorithms which can be used to cluster users in different groups, according to their properties and thus, completely eliminate the human intervention required for this task.

4. Personalization

Playing with product or offer description, positioning, actions, preferences, etc.

Nothing beats a user satisfaction strategy like personalization. It’s very important to interact with users on a personal level in the form of personalized offers, personalized messages or product recommendations based on their previous buying behavior or browsing history. Contextually relevant marketing adds value to a user’s buying experience and can greatly influence their future buying behavior. There are many machine learning algorithms that can take user’s historical features and give personalized recommendations to them. Also, customized offers on birthdays or anniversaries successfully gratify users. The system can also gather purchase points for the user and offer higher discount on products.

5. Natural Language Processing

Sentiment analysis improves personalization

These are some advanced AI techniques which can be very beneficial in marketing strategies. NLP techniques like sentiment analysis can be used to gather the sentiments of the users with respect to various products. Sentiment refers to the emotions of users like sadness, joy, anger, etc. It can be gathered using the user reviews of a product online or from social media posts of users regarding a product. This can provide relevant insights of users regarding a product which can greatly influence future marketing strategies. Since, there are numerous reviews and posts from users. It won’t be feasible to manually extract sentiments out of them when this can be automatically done using AI.

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6. User Ranking

Ranking helps make the user experience more intimate

Not all users add the same value to a business. Some are more profitable to a business than others, and hence, require a different level of service. Such users can be identified using a ranking system, which ranks users based on their buying behavior, potential for upselling, etc. Some of the characteristics that can be used to build such an AI system could be revenue, loyalty, service requirements, etc. Once, there is a ranking system in place, all future marketing strategies can be directed successfully.

7. Content Generation

Generate customized content to scale

With the growing number of users buying online, it has become very important to scale up the marketing strategies. AI can not only help in targeting the right users, but also can help in creating personalized content for them. Creating content using AI techniques would become the true form of personalization and would connect with users on a larger scale, thus, improving the connection with the users without manual intervention.

8. Feedback

User feedback to access the performance of the system

No system is complete without a closed feedback loop. Feedback allows us to access the performance of the system and alter them, if necessary. AI techniques can be used to constantly catalogue various user feedback in the form of tagging the posts, reviews, etc. These could also help generate new tags and classify feedback using existing tags.

9. Failure Predication and Recovery

Predict if the system can fail

Every system fails at some point of time. So, it’s very useful to have a fall-back mechanism to which the main system can switch in case of a failure. Also, AI can be used to have a system which can predict if the system is going to fail soon by analyzing the features which led to a failure in the past. Such a mechanism can help in taking measures to prevent a failure in future.

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DATABASE SCHEMA

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HIGH LEVEL SOLUTION

1. User Segmentation

2. Personalization

3. Message Frequency

4. Channel Selection

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5. User Ranking

6. Feedback

7. Failure Prediction and Recovery

8. Natural Language Processing

9. Content Generation

Cactus Tech Blog

Welcome to the Cactus Tech community!

Soumya Kapoor

Written by

Cactus Tech Blog

Welcome to the Cactus Tech community! We’re shaping the future of scholarly and medical communications with innovative solutions and cutting-edge technology. Like what we do? You can join us too! https://tech.cactusglobal.io/

Soumya Kapoor

Written by

Cactus Tech Blog

Welcome to the Cactus Tech community! We’re shaping the future of scholarly and medical communications with innovative solutions and cutting-edge technology. Like what we do? You can join us too! https://tech.cactusglobal.io/

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