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Data Science At Work

“How do customers like the new-look McDonald’s?”


Data Source Use Case: Credit/Debit Card and Bank Account Transaction Data
Hypothesis Category: New Business Initiative

Data 
Key Data Source
Key Data Source
Credit/debit card and bank account transaction data
Star 
Features
Features
  • 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)
Search 
What Were We Looking For?
What Were We Looking For?
The customer’s take on McDonald’s pilot “Experience of the Future” outlets
Light Bulb 
What Did We Learn?
What Did We Learn?
Traffic increased at the new outlets and repeat visits went up, giving us added confidence regarding the full rollout

When McDonald’s announced that it would spend $6 billion on remodeling its restaurants, as investors we naturally wanted to know whether this substantial expenditure would add to the top line or tarnish the brand in the eyes of customers.

Our data science team was able to isolate the credit and debit card transactions made in the 839 “Experience of the Future” restaurants that were remodeled in the second half of 2017.

We found slightly slower growth in average spend in the new-look stores, with their new mix of self-service functionality and table service. They more than made up for it, however, with substantially increased growth in traffic. Whereas the number of repeat visits had been declining in the old-style restaurants, we saw it growing for the new model. Given the company’s plans to roll the new model out for most of its U.S. restaurants by 2020, these findings gave our analyst more confidence in its potential to report earnings upside over the next two years, against a tough background for the sector.

 

Average monthly growth, McDonald’s “Experience of the Future” restaurants


chart 

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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