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Data Science At Work

“Are rewarded customers good customers?”

Data Source Use Case: Credit/Debit Card and Bank Account Transaction Data
Hypothesis Category: Customer Loyalty

Key Data Source
Key Data Source
Credit/debit card and bank account transaction data
  • 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?
The impact that participating in a retail loyalty program has on spending
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What Did We Learn?
What Did We Learn?
Customers who join the Starbucks loyalty program tend to spend more—immediately and persistently

One of our large-cap portfolio management teams wanted to know what sort of benefit Starbucks itself was getting from its customer loyalty program.

Our data science team turned to credit and debit card transactions. After identifying loyalty-program members, it found that they spent around $88 on average each quarter versus the non-member spend of around $30. We were also able to show that average revenue per user jumped substantially at the point of conversion to loyalty-program membership and, for all but the very highest-spending customers, it remained high and even rose further over subsequent years.

Furthermore, the revenue growth associated with our sample of reward members closely tracked revenue growth subsequently reported by Starbucks. In our view, we had apparently found a robust new alternative data metric with which to forecast future quarterly performance.


Quarterly Average Revenue Per User



Quarterly Average Revenue Per User


Source: Second Measure, Neuberger Berman.


This example shows how alternative data can be used to tease out the spending patterns of distinct subsets of a customer base, and track the performance of business initiatives in close to real time.

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