If you haven't tried Bayesian Analysis to attack common business problems, I highly encourage you to explore it.

Companies' data science curve often starts with linear regression before a giant leap to ML within weeks or months (ensemble models, even some deep learning).

In pragmatic terms, the power of Bayesian modeling comes from being able to assign probability intervals to predictions.

Suppose you're creating a demand model (predicting unit sales using price, seasonality, competitor actions, and a host of other variables). You want to use the outputs of this demand model to understand how price changes influence unit sales - and of course, revenues and gross profits.

Our traditional models suggest that a -20% price investment would increase unit sales by +35% and revenues by +7%. It's a sound decision: let's invest in price to grow our Unit Share and Revenues (let's ignore that we're losing Gross Profits for now).

In contrast, suppose that our Bayesian model tells us that:

• There's a 20% probability our units would decline by -20% to -40%

• 20% probability of declining between -20% and 0

• 30% probability of increasing by up to 15%

• and a 30% probability of increasing +15-50%

Now we can play with these estimates and understand our pricing investment's risk and the upside. If there is a:

• 50% probability of losing -$3MM to 8MM in Revenues

• 40% probability of gaining +$1MM to 3MM

• and a 10% probability of earning $3MM-5MM, we will rethink our decision.

Think back to elementary business or stats classes and the concept of Expected Value - or, simply, the probability-weighted outcome.

It's a compelling but heavily underutilized concept in the business world - except perhaps Business Development / M&A modeling. (Yes, many Finance types still use Monte Carlo simulations - the basis of Bayesian Modeling - in Excel to quantify the risks and upside of business acquisitions or divestitures).

We often hand predictions to our business stakeholders as absolute truths without quantifying risk and reward.

Bayesian modeling, while more complex and time-consuming than popular modeling approaches, gives us the ability to quantify both the risk and upside.

Especially if you are doing Data Science for Pricing, Marketing and Sales problems, I highly encourage you to dive deeper to understand how you can apply Bayesian techniques to your critical use cases:

• Should we invest $10MM in this new ad campaign?

• Should we deploy $20MM in promotions/price investments next quarter?

• Should we reorganize the sales territories in XYZ way?

• Should we introduce these three new products next year?