Protégé

Ran into one of my favorite people this morning whom I hadn’t seen in several years.  Was absolutely wonderful seeing her,  and made me think about the protégé relationship.

Having an amazing protégé must be like having a Padawan,  but more analytics,  less light sabers. (for a good explanation,  see wookiepedia…seriously.  Yeah I couldn’t make that up if I tried)

When your protégé passes you on the ladder to success,  it is such a proud moment. 

I hope she gives back as well. 
 
I have only had a few protégé’s since I graduated from student to teacher,  so to speak,  and I hope they all experience the same levels of success.

Analytics drives business,  but relationships bring it home.

-That Planning Guy

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?

grop3

 

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?

grp33.png

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:
https://theinnovationenterprise.com/summits/marketing-analytics-innovation-chicago-2016

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

Stock Outages

Good technology +great talent =NO stock outages
Bad tech + great talent = outages
Good tech + low talent = outages
Bad tech + low talent = disaster.

Simple math. 
If you have stock outages,  which scenario are you in? 2,3 or heading to 4? Stock outages are a last-generation problem.  Should be eradicated like polio by this point. 

Analytics drives business. ’nuff said.

-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: http://risnews.edgl.com/web-event/5-Critical-Components-of-Advanced-Pricing-Strategy105100?referaltype=revionics

 

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!
https://theinnovationenterprise.com/summits/marketing-analytics-innovation-chicago-2016

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