Pricing Analytics in an F&B Environment

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!

Next Speaking Engagement~

Short message tonight…

This week I will be at this event:

Looks to be a great event- very looking forward to it!

And, Chicago is a great place.  Last trip was a lot of fun. And Deep Dish Pizza.

-That Planning Guy

Upcoming Events

A few events to mention this week….
First, this Thursday, 5/12 – a LIVE WebX!
We’ll be talking about one of my favorite topics, PRICING! Hosted by Joe Skorupa from RIS News (@risnewsinsights), the panel includes the amazing Sahir Anand from EKN (@sahiranand, @EKNResearch) and Cheryl Sullivan from Revionics (@Revionics) who know a thing or two about pricing analytics!
Should be a great discussion!

Register here:


Then next week, Road Trip!  LIVE in Chicago! Very excited to participate in a Innovation Enterprise event! Incredible line up of speakers and some just-added compelling panels!

Who’s in?
-That Planning Guy


Order of operations

4+3×4=16. If you think it equals 28, please stop reading now and review order of operations in about 8th grade math. (if you think it equals 11, see an optometrist)

This week we continued our review of new planning systems, and internally had a conversation about what order of operations should look like in planning. 

If you change the forward forecast of sales what should be impacted? If sales go up,  inventory should follow inversely.  OR… Inventory should be stagnant,  and receipts increase equally to offset. 
What about MD?  Md% move,  or does % lock and md$ plan move?  And.. If MD $ changes than OH forward has to move.  
Endless loop?  How should this be done? 

The answer is yes to all.. Or no.   Depends on the item,  the goals,  the seasonality (in or pre-)
I want the system to match whats in my head. 

In Gen Merch (what we call our food,  bev,  HBA,  etc) the receipts have to move lockstep with sales. Todays receipts =tomorrow’s sales,  +/- fixture fill and safety stock/lead times. If sales jump 10% receipts must jump as well.

In Fast Fashion?  Cant  buy it back normally,  so I want to decrement the inventory to show future impact.  Ultimately the receipts have to compensate,  but there is more timing /availability to factor.
Jewelry,  high end,  watches?
Some of both. 

And in Branded goods?  Well,  hopefully replenishment already caught it,  bought it,  and trended orders as needed. (see Replenishment 101)

Just need to software companies to match whats in my head and we’ll get along just fine.

-That Planning Guy

Vacation, and the the Demi-God Maui

Back from vacation. It’s always hard to come back and get into the swing of things, but alas, what makes a vacation special is the time in between them I guess.

Taking a divergence from my usual Greek God commentary to add in a few Legends of Polynesia…

The legend of Maui is one of my favorites: Maui climbed Haleakala and lassoed the sun to make it move  slower and make the Hawaiian day last longer.  As one who enjoys spending a lot of time under the Hawaiian sun, the length of the day is delightful. Sunrise, sunset, and everything in between.
(Maui did several other things, such as creating the islands by hooking the ocean floor and he and his brothers pulling up the islands…)

Sometimes when we think there is not enough time in the day to accomplish all our tasks, we could use a Demi God like Maui to lasso the time clock and slow it down to give us more analytics time in the day. Perhaps we need a god/goddess of Big Data?  ‘Datalist’ should be the God of Big Data and Analysis? Open to other suggestions~~~
In the meantime, tomorrow is Monday morning, the birth of new data to review, and Datalist will be busy.

With much Aloha,
-That Planning Guy

Pricing analytics, revisited

I commented this week that if an item sells really well,  it is probably priced too low.   The reverse is also true.  There are no bad products,  only products priced wrong!
Is this really true? 

Part 1: if an item sells better than expected,  the perceived value is bigger than expected,  which should show in the pricing analytics.  Demand > expected demand is a pricing signal… Giving away profit. (assuming of course elasticity supports this,  and new retail X lower units is better than old lower retail X original units.) If not true, then the signal is wrong.
What about the reverse,  the part 2? If an item sells worse than expected,  can’t we just lower the price until demand is where it should be? Hmmm.   Unless it is not profitable,  in which case it was a bad product,  bad timing,  bad presentation even- and pricing is not the (only) issue. 
When you run out of pretty,  ugly sells?
But, how do you know what the right price is  to start?  What if your comparison items are wrong too? Comparing to competitors?  We KNOW we are smarter than those guys!

Perception.   Where is day 1,  and where will day 2 end? The route to success can not be short-cut.

Note: Athena was the Greek Goddess of wisdom,  justice, math… and war.   Is the connection that direct between wisdom and war? Perhaps the natural order, linear context,  direct path … Next,  let’s look at some SunTzu – Art of War. Always some fun things to discuss.
-That Planning Guy

15 minutes

I increased my workout time by 15 min a few weeks ago.   I accomplished this by waking up 15 minutes earlier.   No real magic there. Now I do a solid hour on the elliptical machine.  
I have noticed some interesting things that have happened,  and a trend.  The first 10-15 min I am just getting going.   Minor effort,  heart rate not yet really in the Zone.  (big fan of HR based training,  all about the data!)
The next 30 minutes are kind of a grind.  Heart rate better,  just flowing along.  HR good effective,  efficient workout.  
It’s the last 15 min that bring me to here. This is when I am fully warmed up,  all aches and pains better,  and I am in  ‘countdown to be done’ mode.   Can step on the gas.   These minutes are greater exertion,  and obviously greater effect. 
OK,  where you going with this  one,  Planning Guy?
Analytics follows the same pattern.   (you knew that was coming by now!)
First block of time is gathering data.   Pulling sources,  building queries,  filling tables.   Necessary,  but not anywhere near the end game. 
Stage 2: assembly.  What goes with what.   Where is this pointing?  What joins the data points?  How do we present to make sense? 
Final group : Insight.  When we reap the fruit of the labor.   We bring home the answer.   And we are JACKED about what we now know. 
So,  how do we get more time in phase 3 and less in phase 1? I can wake up earlier.  So how do we WAKE UP  earlier in this world of analytics?

-That Planning Guy