Skip to content
Select Your Location
View available investments and insights in your market
Europe and the Middle East
Asia Pacific
Data Science At Work

“Why did we doubt that investors would pick up Lyft and Uber?”

Data Source Use Case: Credit/Debit Card and Bank Account Transaction Data
Hypothesis Category: Pre-IPO

Key Data Source
Key Data Source
Credit/debit card and bank accounts
  • Transactions include date, amount and description
  • Bank accounts include deposits, such as paychecks
  • Detailed geographic information (to the individual store level)
  • Detailed demographic information (age, gender, income)
  • Online vs. in-store sales (in most cases)
What Were We Looking For?
What Were We Looking For?
Patterns in driver loyalty and new customer growth
Light Bulb 
What Did We Learn?
What Did We Learn?
An increasing number of drivers were working for more than one company, and fewer new customers were subscribing.

The market leaders in ride-hailing apps both took their stock public in the first half of this year, and both similarly suffered inauspicious first-day trading. Our analysts were not surprised by this because our data science team, using two types of alternative data, had confirmed their existing doubts about potential rising costs and potential declining sales growth.

On the costs side, from direct deposit information, we were able to identify Lyft and Uber drivers from our sample of bank account data. Through 2017 and 2018 that data showed a rising percentage of drivers working for both companies. That implied declining driver loyalty and increasing driver churn even as the absolute number of drivers was rising.

On the sales side, from credit and debit card transactions, we found clear evidence of rapidly declining new customer growth for all three of the leading ride-hailing firms, which suggests that future revenue growth will be ever more reliant on increasing use by existing customers.


Driver Preference – Lyft vs. Uber



New Customer Rate: Rideshare Rivals


chart 2 

Source: Second Measure, Neuberger Berman.


These findings were not decisive for all portfolio managers: some decided not to invest; others saw the findings as a counterpoint to a strongly held investment thesis. All would agree, however, that these cases show how alternative data can provide a unique window into the true performance of pre-IPO companies. This case also shows how there is more to bank account data than consumer spending insights (payments out of accounts). Payments into accounts (e.g., paychecks) are key to explore labor mix and labor costs for individual companies, sectors and the economy as a whole.

White Paper
The Data Science Revolution
Download White Paper
More Big Data Case Studies
Competitive Threat
"Are Amazon's new batteries eating into Energizer's market?"
Customer Mix
"Are men really going to buy clothes from Lululemon?"
Customer Loyalty
"Are rewarded customers good customers?"
New Business Initiative
"How do customers like the new-look McDonald's?"
"Is Comcast a good place to work?"
Big Data
The Data Science Revolution
How the new richness and accessibility of data, and advances in data science, are enhancing both quantitative and traditional fundamental investment research—and sparking a revolution in active management.
Learn More