Seemingly Random Events

I have been thinking a lot recently about everyday events that occur- and how they all link together. Or do they? Does a person or an event change the future?

Does big data explain – and predict- randomness? Can we??? Is it the job of data science to shape the future? 

A car pulls out in traffic, and cuts off another car, causing 2nd car to slam on his brakes.  The car behind him hits his brakes to avoid collision, and behind him a little less so (time and distance spaced to make it less ‘crisis’) But a 4th car was not watching, and swerved into oncoming lane and hit an oncoming car.  Which random event caused what?

In chess there are exactly 20 opening moves, and turn 2 equally 20.  So at the start of player 1, turn 2, one of 400 scenarios has happened.  19 more possible moves (or reactions at this point) and 19 for Player 2 means at the end of 2 full turns, there are almost 145,000 possible board layouts.  Seemingly random? Or action-reaction/cause and effect perhaps?  And more importantly, Is there a difference?

As Scott Adams postulated, are we all just moist robots? Is free will an illusion?

When I meet a new person, if I choose to just dismiss and say “Hey, how are ya” and walk off, versus having a conversation, engaging with that person, how does that affect future outcomes? Was that the person who will have an impact later in your life? Or would it have been?

Social Media makes these engagements far more common. If you accept that LinkedIn contact, is that a person who will give your company a huge game-changing order down the road? Or will you hire them, and they develop a new software/process/widget that forever changes the industry?

What if Jobs hadn’t met Wos? Or if Paul Allen’s parents had moved out of town, and he never met Bill Gates? (or even more random, if Allen hadn’t read about the Altair 8800 Microcomputer in Popular Electronics and suggested they could program a basic interpreter for it? He read Sports Illustrated that day instead. No Microsoft? No software revolution? )

Malcolm Gladwell tried to explain some of these are right-place/right-time in the book ‘Outliers’- but I don’t necessarily agree that they are explained away.

We hear about a butterfly flapping its wings in Africa and causing a hurricane. I think there a million random occurrences per day. Or are there none, and they’re all pre-determined, like the 145K moves in the first 2 turns at chess.

If I had not decided to change jobs in 1999 to a completely different field, where would I have ended up? And the people I met along the path have had some affect as well, like the steering currents of wind and water on the hurricane. 

In Summer of 94, I owned a pool service company in New England.  For the winter, a buddy suggested I work with him at a part time job.  If I wasn’t in that job in 1995, I wouldn’t have moved West.  So I wouldn’t have started new role in 99.  Or this new position in 2006. Or this website in 2016.  So a buddy hooked me up with a simple part-time job for the winter, and set off a chain? Or was it all unrelated, and just one of the 145,000 outcomes in the first 2 moves.

Moist robots. 

“You can choose a ready guide in some celestial voice
If you choose not to decide, you still have made a choice
You can choose from phantom fears and kindness that can kill
I will choose a path that’s clear, I will choose freewill” – Rush


New year 

The calendar calls Jan 1 New Years day,  but I think that’s arbitrary.   Wasn’t that  determined by a Roman Emperor in about the 3rd century?  (Gregory?  Julias?Augustus? Sorry,  I follow the Greek history more!) School years are Aug to June… So why would  Planning Guy years be locked into a traditional calendar? In planning we plan seasons anyway.  Spring/Summer in my world is Feb to about Sept.  Planning is flexible within a rigid environment.  

In my house we plan years by our annual pilgrimage to Maui.   As I am currently sitting at the airport, yesterday ended the 2016 year  to me, and today starts the New Year. 

2016 was a good year.  No major health issues,  no catastrophic events in my life.   Launching this website,  several great speaking events,  made many new amazing friends and  met hundreds  of interesting people. Learned an incredible amounts of skills and lessons. 

2017 will be a year of change and transitions.   I dont know exactly what changes will occur but there will be many.   Some will be small,  some big.   Planning means evolving.  

To the friends I made in 2016, to the old friends I reconnected with,  to the ones who continue to be in ny life,  I appreciate my time with you,  and the effect you have. 

Time to board,  and start the new year.  

Aloha,  and Mahalo.

I am… That Planning Guy 

Amazing Event

The Big Data and Analytics for Retail event was really fun.  I love when you get a room full of very smart people talking about what they do,  area of expertise,  etc.

I always find these events not just educational,  but a general good time.   Seeing what others are doing well and struggling with validates my own opinions,  as well as adds perspective.
Always seem to make some new friends and contacts as well! Being in Chicago is also a great time.

Now back to the real topic: Analytics Drives Business.

-That Planning Guy


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?



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