I just have to say: wow! How much money are they spending on this survey, and how completely useless is it. A couple of pages in, it comes to a page asking me to choose three things from a list of things that this company does better than any other provider.
I balk at that. I’m a user, and a customer, and I don’t think that company does even one of these things better than any other provider. Literally. If I didn’t like them, I wouldn’t use them. But they are a compromise between competing features. So what happens:
I tell them the truth. I check none of the boxes. I write it into the “Other (please specify)” area on the form.
And the survey stops. Dead. Nope, you, user, can’t continue your survey until you tell us the three things we do better than any other provider. So I’m gone, out of the survey, writing this blog post, far more amused than annoyed.
Seriously, though, somebody charged with marketing came up with the bright idea of a survey, spending company resources, that pushes people for empty meaningless results. Or is this a hidden weapon in marketing-management politics, maybe, that the survey pushers wanted to prove how good they are with faked results proving they’re good?
But they don’t have my results included. At this point, like they say on the TV show, “I’m out.”
Palo Alto Software’s marketing team prepared this infographic from information provided by 10,000 users of its LivePlan web application for business planning. So that’s not a random list of small business owners, but it is a list of people who have wanting to plan a new business, or grow an existing business, in common. So that, to me at least, makes this information pretty interesting.
I enjoyed this thoroughly and I’ve been meaning to post about since Scott Shane first posted Entrepreneurs’ Job Creation: Expectations Versus Reality on Small Business Trends last March. His chart, shown here below, compares what entrepreneurs said were their hiring expectations to the actual hiring:
You can read the details on Scott’s post. I don’t really care about the research specifics. I think it’s wonderfully eloquent as is, a picture worth at least 1,000 words.
Once again, research based on asking people what they are going to do is inherently flawed because most people don’t know and most people say what they feel good hearing themselves say, not what they really think.
Entrepreneurs are optimistic about hiring.
Entrepreneurs are most optimistic about hiring when asked by a pollster how many people they are going to hire.
If you could invest in the difference between what entrepreneurs say will happen and what actually happens, you’d be very rich.
This is a lot of fun. My conclusion, however, is that it’s only a lot of fun. Not great information.
The top three wants for both sexes are the same, just in different order.
The top 10 lists for the sexes overlap by 70%.
80% of the items on both lists are food.
What? No sex? No health? No love? No longevity? No owning your own business?
I don’t know about you, but I’m not posting what I really want onto Twitter. I really don’t have that much need for sharing. Ice cream, maybe; a car, maybe; coffee, cookies, and all that, maybe. As long as it’s pretty much trivial, or it dresses me up in my business persona.
I really like the idea of mining large amounts of data to get past what people say about themselves and into what people actually do. Sales information and actual purchases, for example, tell me thousands of times more than what people say they want or plan to purchase.
But in this case, we’re talking about social media. And that’s publishing.
Question: are you putting real personal information into your social media feeds?
Can we talk about beauty in numbers? Amidst all the nervousness about forecasting new products, does it not make sense, and add elegance too, to talk about the classic s-curve we see in nature? So many natural phenomena show an increase along a natural S-curve and bell curve like the two shown here on the right. Which, by the way, was done with Excel. And I’m going to explain it here and give you the formulas, so you can use them in your own sales forecasting. If you like this sort of thing.
What we see there is a natural projection of a good product — like cell phones or television or computers — spreading through a population of 50,000 people. It could be a new product technology that everybody ends up wanting, or, simply, a good idea. It doesn’t apply to a lot of new product forecasts without humans evaluating the results and tempering them with common sense. But then few mathematical models do.
The spreadsheet model that drew that line used these assumptions:
The total population of potential users is 50,000
At the start, 10 people are users
Each month a new person starts using for every three people already using.
If you like math buzzwords, then you’ll like knowing that the math behind this natural curve is called a diffusion model, which has been used in science in applications like epidemiology (it’s basically the plot of the recent hit film contagion). It’s also closely related to the classic idea adaptation model, which produces a classic bell curve that we often see divided into areas for the opinion leaders, early adapters, and so forth.
That’s my second chart here, and it is done from exactly the same numbers as the first. The first is total users per month, and the second is new users per month. This one was made famous by sociologists studying the pattern of adaption of a new farming technique, and has been used a lot for predicting technology adaption.
This is one way to answer that frequently asked question “how do I forecast a new product when I have no history.”
In the spreadsheet model below, you can see the key formula for calculating new users from existing users. In cell D7 there you have the formula:
To read that formula you’d guess — correctly — that the users range is in row 7, contagion rate is dell B3, and population is cell B2. I enclose that basic formula inside a rounding function that rounds results to whole people, rather than portions of people. That turns my formula into:
… which is what you see in the illustration above.
As you can probably guess, the users calculation in row 6 of the spreadsheet adds the beginning of the month users total to the new users to calculate the beginning-of-month total for the next month. So the 23 users in cell E6 is the sum of the 17 users in cell D6 plus the 6 new users in cell D7. For that formula I use a MIN function to make sure I don’t accidentally project more users than total population. The actual formula for cell E6 is:
So what’s the use of all this spreadsheet detail? First, because it gives a forecast some meaningful assumptions you can explain to an interested party, substituting a natural phenomenon for SWAG (scientific wild-assed guess). Second, because sometimes it works. One of the most accurate forecasts I ever did was a projection of personal computer usage in Latin America in the 1980s. I assumed four economic strata, so four different population groups, each of which had different contagion rates and total populations. They were basically the urban wealthy, the middle class, and the rest of the population. I did a five-year forecast that my clients checked five years later, and I had predicted the total, on a five-year time frame, to within five percent of what actually happened. That was quite a coup, and it still makes me happy many years later.
Final note: I’m enjoying all the possible contradiction between this post and my last post, make my analysis intuitive please. I’m not offering any excuses for it either. Except, maybe, the beauty of a natural curve.
Are you thinking type or feeling? Analytical or intuitive?
There are studies, there are tests, there’s a whole body of work on personality types dividing people into types. Most of us have heard of this, but if you haven’t, and you’re curious, you could find out more with this google search. It’s about the work of Carl Jung, the Myers Briggs tests, etc. There are more factors than just this one division, but this division — thinking vs. feeling, analytical vs. intuitive — is interesting to me.
I’ve had a career focused on business planning, metrics, market research, and business analysis. That started for me with a couple of years at business school, during which I discovered that I love numbers, and patterns, and programming. So I’d expect to be classified as a heavy thinking and analytical type.
But no, in fact, I’m not. It turns out that over the last 15 years I’ve done the testing four times and I come out fairly heavy on the intuitive side.
What’s up with that? I asked myself that the other day. Of course I ruled out all the possibilities related to something wrong with me, or defective in any way; I like me.
I ended up thinking that this might be the ideal: take an intuitive person and teach analysis and numbers, and you’ll get somebody who does the analysis, likes it, uses it, but doesn’t really believe it just because it’s analysis. There’s constant tempering with common sense and skepticism. Show the charts, give me the analysis, and then I’ll digest and come back with an educated guess.
Balance is better. Teach intuitive people analysis and teach the analytical people how to take long deep breaths and let the damn numbers percolate for a while in their subconscious. Best of both worlds.
Are you a store owner in the U.S.? Do you know one? I want your opinion on a few things.
I’m working up some data on small stores and payment plans and local loyalty programs for some friends of mine. As part of that, I’ve worked up a quick and easy survey — only 10 questions, no names, no email addresses, no sensitive data, just your opinion — that I’d like you to take.
A simple scan of some of the cheaper-looking websites that some of the franchise brokerage outfits include as part of their, “Start-Up Packages,” are very telling. Newer franchise brokers and, “Consultants,” regularly copy and paste paragraphs of gunk that cite franchise success statistics from 20-30 years ago. (That have since been been disproven.)
Well done Joel. And of course it’s not just franchising. This is happening all the time, everywhere. Statistics are so easy to push out as if they proved something, when in fact they don’t.
It’s been a long time since I’ve seen this much eloquence in both words and pictures. It’s a great illustration of how a good picture is worth a thousand words, and then again, maybe also why it isn’t always.