Working Paper: NBER ID: w27707
Authors: Scott R. Baker; Brian Baugh; Marco C. Sammon
Abstract: This paper demonstrates that it is possible to construct accurate pictures of firm revenue, growth, geographic dispersion, and customer base characteristics using an increasingly accessible class of consumer financial transaction data. We develop two new measures which characterize firms' customer bases: the rate of churn in a firm's customer base and a metric of the pairwise similarity between firms' customer bases. We show that these measures provide important insights into the behavior of both real firm decisions and firm asset prices. Rates of customer churn affect the level and volatility of firm-level investment, markups, and profits. Churn also affects how quickly firms respond to shocks in the value of their growth options (i.e. Tobin's~Q). Moreover, high churn firms tended to face steeper declines in consumer spending during the recent COVID-19 outbreak. Similarity between firms' customer bases highlights one under-explored type of predictability among stock returns -- we demonstrate that significant alpha can be generated using a trading strategy that exploits our index of customer base similarity across firms.
Keywords: customer churn; financial transaction data; firm performance; asset pricing; customer base similarity
JEL Codes: D22; E22; G32
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| customer churn (L96) | firm-level investment markups (D25) |
| customer churn (L96) | firm profits (L21) |
| customer churn (L96) | volatility of firm-level investment markups (D25) |
| customer churn (L96) | response to shocks in growth options (D25) |
| customer churn (L96) | decline in consumer spending during COVID-19 (D12) |
| customer base similarity (D26) | asset price movements (G19) |