This post has nothing to do with numbers but everything to do with data.
Good analysts don’t just know how to analyze data given to them but how to interpret the context of that data and how to design meaningful ways to get good data.
Here in Northeast Ohio, the sandwich chain Subway has been offering $6 footlongs on any sandwich. The sandwiches normally range in price from about $5.50-$8.00.
Why? I asked. Loss leader? Drive traffic in slow winter months?
Then it hit me. Maybe Subway is running this promotion to collect the valuable asset of all: data.
Ever heard of the ceteris paribus assumption? It roughly translates from Latin as “with all other things being held equal.”
Good analysts find causal relations in their data. And to do that we need to design studies using ceteris paribus.
When we control for all other variables, what changes when we tweak the one variable?
Maybe this is Subway’s idea. Nothing else has changed. No new menu items, no changes to the size of the sub, no changes to combo prices, etc. They are holding all other things equal while setting all subs to the same price.
Why? Maybe this gives them data on, regardless of price, what customers prefer. With price not an issue, what sandwiches are popular?
Will the simplified price structure work better for Subway? I’ve noticed how flat the pricing structure is at Jimmy John’s, a comparable to Subway. There are $5 subs and $6.50 subs. No smaller portions, no one-price-per-item menu. Chipotle has a similar flat structure, and increasingly so does McDonald’s.
This is just speculation. There may be other reasons for Subway to run this promotion. Whatever it is, this promotion will give the chain very valuable data, and good analysts will look at assumptions, design, and potential courses of action.
Food for thought!
What do you think, analysts? Any other ways Subway could use this data? What is their experimental design here?