Contextual Bandit Algorithm - A optimize way for customer engagement

Personalization is the key aspect of user engagement which provides a more scalable and accurate way to achieve a unique experience for individual users. If you want to enable the personalization of user experience around your website or an app, contextual bandit Reinforcement Learning with Vowpal Wabbit can help you.

Vowpal Wabbit provides a fast, flexible, and open interactive machine learning solution for reinforcement learning, supervised learning, and other machine learning paradigms.

“Bandit” in “multi-armed bandits” which is a one-armed bandit a simple slot machine wherein you insert a coin into the machine, pull a lever, and get an immediate reward. But why is it called a bandit? It turns out all casinos configure these slot machines in such a way that all gamblers end up losing money. The task is to identify which lever to pull to get the maximum reward after a given set of trials.

This problem has an ample amount of real-world applications where there is a need for optimization, some clinical trials, financial portfolio design, and many more. You can think of this as advanced A/B testing.

Now we would be going to simulate a content personalization scenario with Vowpal Wabbit using contextual bandits to make choices between actions in a given context. The goal is to maximize user engagement quantified by measuring the expected reward - Increasing the revenue

In contextual bandit algorithm, we have 4 types of components :

1) Context/environment

2) Action

3)Probability of choosing the action

4) Reward/Cost you get by opting for an action

For example: - 

Let us define a context for an agent which takes some actions/decision for a given context. The goal would be to maximize the reward i.e increasing the revenue.

Let's define a context where we have the following data about onboarded and active customer in our database:-

1) Customer data ( details about a customer)

2) Clickstream data ( Capturing each move/event of a customer)

Now let's define actions that would be - recommending the best offer, campaign, and time of triggering for the above pull of customers.

The reward will be whether they will perform customer will perform transaction or not

So here:-

The context is information about the user: where they come from, previously visited pages of the site, device information, geolocation, etc. An action is a choice of which combination of options(campaign, offers, time) to display. An outcome is whether the user will perform its first transaction or not. A reward is binary: 0 if there is no transaction, 1 if there is a transaction.

With contextual bandit, a learning algorithm can test out different actions and automatically learn which one has the most rewarding outcome for a given situation. It’s a powerful, generalizable approach for solving key business needs in industries from healthcare to finance and almost everything in between.

While many businesses may want to use bandits, applying them to your data can be challenging, especially without a dedicated ML team. It requires model building, feature engineering, and creating a pipeline to conduct this approach.