Insights

Understand Bias

Are you pitting winners against losers? See how selection bias can mislead you.

Controlled Experiments

Too much data? Learn how to use experiment design to create simple and elegant analytics.

Churn reduction

Why do churn reduction programs fail? See how data can make us pursue wrong targets for churn reduction.

See all insights

Find out more

Find out more about my background

Want to talk?

Latest Thoughts

Overview of uplift modeling in SAS

I found several good presentation on how to approach uplift modeling in SAS, and here is their overview. What is uplift modeling? Uplift modeling is a technique that allows us to determine which targets are more likely produce incremental response when exposed to marketing material. What do you need to create an uplift model? To create uplift model, we need to conduct an experiment. An experiment is a setup where we have targets from various groups, some of which have been exposed to our driver (aka marketing material). Sometimes a natural experiment will work, but in a classic case, we designate test and […]

Propensity to churn modeling does not help reduce churn

I hear a lot of buzz around advanced methods, like predictive analytics, machine learning, data science, etc. Everyone says that’s what you need if you want to make a difference in your business. For example, executives want to see predictive models that tell them who the most “at risk” customers are so they can be targeted for retention. However, when applied to a real life situation this approach often fails to deliver the results. This is how propensity to churn modeling is done. We run this model on our existing customers, and thus we know a lot about them, from their name and address, which […]

Why do uplift models fail?

Uplift (or incremental lift) modeling is generally harder to execute than response modeling. While response follows known customer traits (lifestage, transience, change in circumstances), uplift can be dependent on variables not commonly used in response modeling.After watching a few failed attempts at creating uplift models, I can identify the most common barrier in creating an valid uplift model: marketing programs that are extremely ineffective. Why would it matter? Let’s review in short what an uplift model is. The dependent variable of the uplift model is the difference in response between test (treatment) and control groups. The independent variables can be […]

See all thoughts

The is site was created by Tanya Zyabkina using instructions by NYC Tech Club. Free images by Unsplash.