Quitting
“A quitter never wins and a winner never quits.” — Napoleon Hill
A good click-bait headline can go a long way, and so we shall not quit using them from time to time. On to the topic at hand…
Napoleon Hill had a nice, empowering quote there about quitters and winners, and the internets are lousy with similar quotes and memes intended to encourage those facing difficult or even impossible odds. If one is concerned with uplifting the spirit, preserving the soul, or perhaps simply saving face, quitting seems like a Bad Thing and so we are told over and over with all manner of quotations true or otherwise.
There is a small problem with that notion as far as the Mobile Vendor is concerned.
Initial Assumptions
These assumptions have come up and will continue to come up as we discuss our view of the current mobile vending marketplace (in Nevada and perhaps beyond) and our strategy to improve it, but for the purpose of discussion quitting, let us assume at least this things:
- Mobile Vendors can and should go to locations where they may conduct business with a profitable outcome. This is how a business stays in business, after all.
- If Mobile Vendors are paying additional fees or revenue shares to an event host or organizer to participate in an event, the decision as to whether to participate (initially and ongoing) should be made with some performance data, rather than by the seat of one’s pants.
- If the same Mobile Vendor revisits a recurring event (whether for “free” or as part of a recurring event hosted or organized in exchange for fees), there is at least one point during the recurrence where the Mobile Vendor may consider Quitting, and it is selection of the single point of actual quitting that is of interest.
This seems reasonable, but in practice there tends to be a need to “build up a spot” so that perhaps if there is a bit more social media outreach or some other magic, there will ultimately be success in the form of attendance, enjoyment, and profit. Giving up early is unappealing, particularly as any given vendor has invested more with each additional vending opportunity to accomplish this location development.
As with many things in life and business, there’s a point at which one should and must limit losses, cut costs, or generally move on. The question is, when is this point reached? How to know?
There’s a Math for That
Vsauce2 (and its sibling channels and their creators) is a great resource, this is a solid video with the benefit of brevity. Hopefully you took the time to watch it.
For the purposes of this introductory discussion, we’re looking at a completely contrived summer series of events that occur on Fridays in June until August end in 2019. The chart in Example 1 above shows the ticket counts for 4 Vendors and the average of those 4. By “ticket count” we mean the number of transactions, where each ticket may have one or more items, the dollar amounts will vary, the profit margin will vary, and of course these will all vary between each Vendor. The average value is among the participants at each event, of the four Vendors sharing their data. This means that most of the data includes the average of 3 of the Vendors (not participating is not 0 tickets).
The data are fabricated and are subject to bias, since these are used to illustrate the application of a stopping method, but over time and with more real-world data, whether these methods are applicable will be shown with practical results, rather than theoretical or anecdotal results.
That said, with 12 events in our result set, dividing by e gives us about 4.4, so that after the 4th event, we should be in a position to make a decision about quitting. Is it four total events, or four events of participation? Good question.
Strategy
Our goal is to determine, on behalf of one of our Vendors, whether continued participation is a good idea, or if it’s time to change the schedule. If we follow the prescribed method and collect data on the first 4 events for each Vendor, we can decide what the “worst” event result was for each, and from event 5 forward, make any result that falls below that “worst” result the final participation. In other words, given lowest ticket count reached in the first 4 events, if the ticket count at any of the following 8 falls below the lowest value, that is the final event. That is the time to quit.
For the first 37% of our events, the lowest ticket counts for Vendors 1 and 4, both participating in all events, were both 10 tickets. This means that the next time that a ticket count falls below that cutoff of 10 tickets, the Vendor in question should quit and move on to other opportunities.
In this contrived example, Vendor 1 should stop at 8 tickets on August 2, and Vendor 4 should stop at 8 tickets on July 26. At the end of the events on those dates, the respective Vendor should inform the organizer that they are moving on, and quit this one. That is what the strategy says.
But…
As you know from watching that quick video, there is actually a better chance that these choices are “wrong,” or not optimal, than there is that these were the correct decisions. As it turns out, when we have hindsight in our favor and we can examine all of the fake data here, that while Vendor 1 really didn’t catch on at this event, Vendor 4 had a few solid outings after the quit date. What of that?
Let us not forget also, that Vendors 2 and 3 should also decide whether to continue to participate on alternating weeks, or quit as some time. Since they are not participating each week, how do we count their events?
Vendors 2 and 3 are each attending 6 events on alternating weeks, so their 37% point is near the end. If they each only consider their own ticket counts, then Vendor 2 quits at the end of the August 9 event while Vendor 3 goes the entire distance. We’re jumping ahead a bit, but what if each of our Vendors in this example can look at all of the ticket counts to decide, whether they are there or not?
What about that Average line?
The trouble with an average value is, it hides information. It is a type of filter that prevents the casual observer from knowing some important detail, and in this example there already wasn’t enough detail. Why show it, then?
Let’s actually consider more of the missing details first:
- What are these Vendors actually selling?
- Are all of the Vendors selling similar products (eg food, fresh produce, clothing, etc)?
- Are prices at all similar across Vendors?
- How long have each of the Vendors been in business (ie do they have regular customers and followers, or are they new to the marketplace?)
- How have prices been effected (increased) at this event series and is that having an impact on sales compared with other locations (and how do regular customers react)?
Given the ticket counts for our fictitious Vendors at our fictitious event, not knowing these specific details, how useful is this information at all?
Getting back to the Average line shown on the chart, does it have any value? To each individual Vendor, probably not, because an average value filters out the details and with all of those other Vendor specifics left out of our discussion, it’s not so useful for any of our Vendors here to consider. There is an event organizer, though, and that is a different story.
Using the same measurements, the organizer could have quit this event at the end of business the 5th or 7th week (July 12 or July 26), and would have made a “correct” decision according to our method, for the most part. It isn’t that easy to cancel an event under these circumstances, but it does happen, with all of the associated and cascading effects.
Is any of this actually useful, if there is so much left out, and if we are indeed only likely to have made the “correct” decision 1/3 of the time?
If you decided to keep reading, let’s consider some of the additional factors missing in this example scenario so far:
- How much does each Vendor spend to participate in this event (all costs, from being in business to wages on that day to vending fees to inventory risk… everything).
- Given the costs, how do ticket counts actually impact outcome? If one ticket yields profit for Vendor 1 and Vendor 4 makes a profit after about 12 tickets, each is in a very different situation.
- Were other event options rejected in favor of this event? Consideration of the Opportunity Cost, especially given that Mobile Vendors can often move to more receptive geographical areas if any are identified, cannot be left out of any serious decision process here.
- What about other Vendors? If this event had 100 Vendors participating, or only the three each week shown with data here, we have not considered at all what the event outcome really is and how these indicators place within that picture. We lack perfect information.
Why perform this exercise at all?
It has been said, “The first step in solving any problem is recognizing there is one.” This exercise has, with any luck, shown that there is a basis on which to make this decision. Should a Vendor continue to “through good money after bad,” or “double down,” or “cut their losses,” and at which point does it seem like taking that path is actually Winning, despite what Mr. Hill might think.
Because Mobile Vendors may enter and leave localities, participating in events here and there, drawing fans and followers from near and far and engaging new customers, paying fees, obtaining licenses and permits… knowing that there is a system to analyze, and that there are tools and methods available to do so, is an important starting point. Rather than flying by the seat of one’s pants, it is actually possible to make some initial decisions based on some simple data.
That leaves us with an important notion brought up in the previously-published Hypotheses article in this series:
The Example 1 chart is interesting because anyone can see the data for our four Vendors, and we would know how many Vendors participated in each event each week in total, and we probably know whether there were other events competing for attention, what the weather was doing to thwart attendance, and whether there were other extenuating circumstance. We can infer a great deal of information about individual Vendor success potential (not assurance, but potential) by considering “the big picture” from multiple perspectives.
Since each Vendor has their own success benchmarks to consider, the intent is to provide useful information and tools and methods to use against that information, on which to base business decisions. By providing ticket counts (and item counts if possible), we can start the computation with a common set of measurement data that leaves out individual pricing and margins, but still indicates customer and sales volume, which also indicate event interest and market segmentation (“is my product relatively popular with this crowd?”) and we now have a mathematically sound dividing line between measurement and decision.
This being Nevada, and given this topic, another YouTube video seems apropos:
As he says, Blackjack win potential depends at any given time on what has happened previously (the hands each player has played, with hits and stands and the state of the deck(s), along with players entering and leaving the game), and by using a method frowned upon by everyone in the casino business, it is possible to make informed decisions about the win potential even though there is missing information. Here again, Winning sometimes means Quitting…
If we consider this business decision as falling between selecting the largest number, choosing a parking spot, hitting or standing or leaving a blackjack game completely, and finding someone to spend the rest of your life with in marriage, we can see that it is merely a tool and not The One True Way. It is a target at which to aim, and it is one based on a sound methodology, but it is not a guarantee. Few things are.
Let’s end with two more quick examples based this time on random ticket counts for our four Vendors, where each participates in all scheduled events (unless they quit):
In Example 2, Vendor 3 would quit (or consider quitting) after the July 19 event, while Vendor 4 would consider it after the August 23 event. Vendors 1 and 2 seem to be getting a consistent amount of business through the series. For extra credit, what does the Average line tell the event organizer? Hint, does it help to show how Vendors and organizers to work together to select a lineup that works for everybody? Worthy of note, the event on July 5 was not so great, and while this is random data, how would you consider this data given what you would know at that time about July 4?
Example 3 is an exercise left for the reader, with a reminder that consideration of known data against individual selection of Key Performance Indicators can begin after the 37% mark, but it need not, and in the case of mobile business decisions, should not end there. Add another method to the toolbox, and make sure you’ve got room for a few more…
These are the musings of an engineer with a minor academic concentration in economics and a few years experience working with mobile vendors and observing mobile markets. This is (or perhaps continues to be) the beginning of a discussion and that will extend in a few directions and for months and perhaps years, and corresponding explorations and corrections from which we hope to learn a great deal.
