Propensity Modelling
Susan Ikegwu, ACIM
Snr Growth Product Marketing | Leveraging Data for Strategic Growth | GTM | Lifecycle Marketing | Growth @Google
Decoding the Crystal Ball of Marketing Experiments
Let's be honest, marketing is a bit of a gamble. You throw out a campaign, cross your fingers, and hope it hits the right people. But what if you could peek into the future and see who's most likely to bite? That's where propensity modelling comes in.
Think of it like a crystal ball for your marketing experiments. Instead of blindly targeting everyone, propensity modelling uses data to predict who's most likely to do what you want them to – buy, subscribe, click, you name it.
Understanding Propensity Modelling
At its heart, propensity modelling is all about looking at past behaviour to predict future actions. It crunches data on customer attributes, past purchases, browsing habits, and more to figure out who's got a high chance of taking a specific action. The models leverage historical data and various attributes associated with customers to generate these predictions.
Okay, But How Does This Actually Help My Marketing?
Glad you asked! Here are a few ways propensity modelling can supercharge your marketing efforts:
Sounds Great, But What's the Catch?
Like any tool, propensity modelling has its quirks. It relies on good data, so make sure yours is clean and up-to-date. And remember, it's a prediction, not a guarantee. But when used wisely, it can be a game-changer for your marketing.
In a Nutshell...
Propensity modelling takes the guesswork out of marketing. It's like having a secret weapon that helps you understand your customers better, target your campaigns smarter, and get more bang for your marketing buck. So, why not give it a try? You might be surprised at what you discover.
Product Marketing | Product Management | SaaS (B2B & B2C) | Marketing Automation | Driving Business & Product Growth Through Data-Driven Strategies & Cross-Functional Collaboration
4 个月Interesting read, I absolutely agree, Predictive analysis in this era of too much data can be life saver! Thanks Susan!