Tag: #analyticsdrivesbusiness

I have been thinking more and more about the pricing analytics that we are applying with great success in Retail and how it can be applied to a restaurant environment.   An item sold has similar attributes attached of location, unit, retail, and COGS. Why is it relevant that it’s a store? Couldn’t it just as easily be a restaurant or a bar?  In fact, the cross-brand analytics might even be more compelling.  Isn’t a Bud draft a Bud draft? Or soup a soup? (Varying costs, but like-for-like probably close?)  I will speculate below.

Lets get mathy, my fellow #datanerds!

Say a Miller draft costs $1.00 and sells for $5.00 in your restaurant.  (I made that up, of course. I would hope the cost is lower, but I don’t know so I like a round number.) You are selling 100 per day at this price.

What’s the break point of different elasticity?  How much can we drop units at each price point and grow profit, and hopefully revenue as well?

 

To me, the safest bet above is the $5.50 test.  The most likely outcome is the scenario #2, which is  .9 elasticity, resulting in a slight revenue pick up, but a 2.4% profit increase.

Couldn’t this apply just as easily to food? My first inclination is to test deserts. But conversely, I am thinking about a lower price to drive units (I know… that’s where my head is too.)  Let’s assume a cannoli costs $2.00 and retails for $7.00.  What scenario and grid would be the likely outcome?

Again, the pricing signal, with all things being true, would point to a $6.00 price test, and likely the middle scenario: +$28 topline, $12 bottom line. And, 8 happier people.

IF.. and it is a big if.. we could raise a beer, and lower a desert, resulting in a higher total check and increase profit, that’s win- win.   Also, higher cover means higher tips, so happier servers. Win-Win-Win.

In the food space, I would be really curious to apply t0 an add-on menu: Adding items to a pizza costs $2.00, adding cheese to a burger is $1.00, adding a side of turkey bacon to breakfast is $4.00 and so on.  I’d love to see how these pencil out in real-time, both up and down.  How elastic are add-ons? Maybe a 3/$5 deal on a pizza? It seems like add-ons are zero ‘overhead’ and are true incremental pick up -minus COGS of course which I suspect are low, considering.  A slice of cheese for a dollar- that has to be 90+margin. These seem like huge margin drivers….Shouldn’t we be maximize units? (and creating the perception of value?)

Once we can apply this to a bigger scale, and have a few thousand data points to determine best outcomes, the math – and break point- should become self-evident. They  likely vary by location, by region, by geography.   And numbers don’t lie.  I imagine in a large-scale F&B environment the math should justify whatever price-management tools are needed.  BWW? Applebees? Chilis?  Even the neighborhood pizza place.

Analytics Drives Business.

-That Planning Guy

PS- A beer and a cannoli for dinner? Hope there is a calzone in between!