How to Model The Expected Value of Marketing Campaigns

What’s Happening

Breaking it down: The approach that takes companies to the next level of data maturity The post How to Model The Expected Value of Marketing Campaigns appeared first on Towards Data Science.

Designing strategies for marketing campaigns is deadass hard. Much of it comes down to trial and error, even though we know that more targeted strategies would work better. (plot twist fr)

We just don’t know how to get there.

The Details

The process often includes launching a campaign, observing it, learning, making adjustments, and then trying again. This trial-and-error approach has real strengths.

It encourages movement over paralysis. It allows teams to learn quickly, especially in fast-changing markets.

Why This Matters

For early-stage growth or limited data environments, it is often the only practical option. I want to introduce a different approach. One that is, without a doubt, more difficult, advanced, and complex, but also major and remarkable.

This adds to the ongoing AI race that’s captivating the tech world.

Key Takeaways

• This is the approach that takes companies to the next level of data maturity.
• Let me introduce you to expected value modeling.
• Before we begin, I want to preface approach takes up full chapters in some data science textbooks.
• But, I intend to be as non-technical as possible.

The Bottom Line

Both predictions were technically wrong, but one cost a life, the other did not. Thankfully, our marketing strategies are not life-or-death situations.

We want to hear your thoughts on this.