Businesses derive value from their customer base. This makes it critical to be
able to forecast the value of a customer to the business. At the same time,
modeling customer behavior is not a straightforward measurement -- the data
generating process is subtle, data is characteristically sparse & stochastic,
and heterogeneity abounds.
We'll look at business analysis from a customer-centric mindset, where analysis of growth and retention is at the core. We'll dive into the model of Fader & Hardie, which provides a principled, probabilistic model to forecast customer lifetime value (CLV) from a stream of purchases.
We'll motivate multilevel models and show how they can account for customer
heterogeneity in purchase behavior. We'll analyze predictive fits using lifetimes, an open-source implementation of several useful Bayesian CLV models. We'll look across businesses and briefly cover insights that come from looking at large-scale datasets of customer purchase behavior.
CLV estimates have a multitude of business uses, across:
* forecasting of cashflow, profitability and demand
* investment & valuation
* customer base segmentation
* allocation of marketing spend, or
* monitoring the health of the business
Data scientists and business analysts should leave this talk with another tool in their predictive modeling toolkit. More importantly, they should have clarity on how customer lifetime value is defined, how it can be reasoned about, and how forward-looking estimates can be fit from purchase data.
Brian is Director of Data Science at Second Measure (YC S15), which analyzes billions of anonymized purchases to answer real-time questions about U.S. consumer behavior. Data scientists at Second Measure deal with large, messy, real-world data, make it clean and representative, compute novel metrics and drive key business decisions for clients.
Prior to Second Measure, Brian worked in risk, forecasting and dynamic pricing
for large-scale home purchase&resale at Opendoor. He's been in data-intensive
fields for 10 years across quantitative finance, machine learning for home
valuation models and large scale data pipelines. He's the office Bayesian, and
cares a lot about data visualization and empowering good business decisions.