Data building blocks: What Is Bayesian Inference?

Introducing Bayesian inference – what is it and why do we use it to power GrowthOS?

At Mutinex we talk a lot about artificial intelligence and AI powered analytics. But what are the building blocks of the AI we use to power our predictions? Where traditional market mix modelling is based on regression analysis to understand how media channels have performed in the past, GrowthOS leverages Bayesian inference to analyse historical data and predict how changes in investment will impact future results.

So what is Bayesian inference and how can it change the way you look at making strategic investment decisions?

Bayesian inference is a powerful tool that can be used to make predictions and decisions based on data. It is based on the Bayes theorem, which states that the probability of an event occurring given certain conditions is equal to the probability of those conditions given the event occurred.

This allows us to make more accurate predictions about future events and make better decisions based on past data. By using Bayesian inference, marketers can gain valuable insights into customer behavior and target their campaigns more effectively.

We use Bayesian modeling to power GrowthOS because it helps us to

  • Reduce the amount of data we need to gather from our customers (we know data sourcing can be painful! With the right training and expertise applied, Bayesian inference doesn’t require much data to surface powerful insights)
  • Model faster and in an online fashion (as new data comes in, we automatically update the model)
  • Define synergies in terms of relationships and capture non-linear behavior  (For example – The probability I make $100 on TV is conditional on how much I spent for Search)

How can Bayesian inference help you in your marketing efforts?

Bayesian inference can be used to identify trends in customer behavior and target customers more accurately. It can also be used to optimize marketing campaigns by testing different strategies and determining which ones are most effective.

Using Bayesian inference in your marketing efforts offers numerous benefits including increased accuracy in predicting customer behavior, improved targeting capabilities, better optimization of campaigns, improved understanding of customer feedback and more efficient decision-making processes overall. These benefits all lead to improved marketing results overall which will ultimately lead to increased sales and profits for your business.

What else uses Bayesian inference?

Bayesian inference has been successfully used by many companies for a variety of purposes.

For example, Netflix uses it to recommend movies and TV shows based on user preferences. Amazon uses it to personalize product recommendations for customers. And eBay uses it to optimize pricing for its products.

So, why is Bayesian is better?

GrowthOS leverages Bayesian inference to run several features which would not be possible to support with regression modeling because regression modeling cannot manage complex data sets with heavy interdependencies and non linear relationships.

For example, regression modeling may show that one channel is generating negative ROI whilst another highly correlated channel is generating a very high ROI. Bayesian modeling can establish complex relationships between data that helps to bring that correlation to the fore and suggest how changes in investment strategy might change outcomes.

Additionally, using Bayesian inference allows us to encode a significant amount of domain knowledge into the modeling process. When customer data is ingested into GrowthOS. This allows us to provide very detailed information about the level of confidence embedded in customer scenarios and suggest experiments that would help to clarify the best investment strategies moving forward.

Want to learn more? Talk to our team about a GrowthOS demo now.

Make better investment decisions. Faster.