Investors don’t look at pitch decks for very long
techcrunch.com
techcrunch.com
e.g. The slide with the correlation scatter plots - who devised the model to draw those trend lines? That's just awful statistics.
e.g.2. The slide that judges the important pages in the deck based on the length of time they're looked at (important? why? why not 'complex' or 'difficult to grok'?)
e.g.3. Observed slide order actually says 'team is never in the middle', but then averages the team to be in the middle of the deck.
Hmmm. What an awful piece of data analysis on potentially interesting data.
Two is an even number.
The average of two even numbers is an even number.
Therefore, one is an even number.
In which universe is that true?
Since the tool appears to be ggplot2, the line (generated via geom_smooth() ) is likely LOESS: http://en.wikipedia.org/wiki/Local_regression
However, they deliberately removed the confidence interval provided, which is misleading.
- Capture the investors' attention -- they'll have to remember your company among dozens
- Make them curious to hear more -- you want them to reach out and ask for a meeting
None of these requires a big number of slides, or for that matter, time to go through the pitch. Most entrepreneur do a simple mistake and try to put everything that's relevant to their project in the pitch.
So assuming a VC will spend ~4 minutes on your deck would be a bad idea. They'll probably either toss it out more quickly than that or take a decent amount of time to go through it properly.
Investors spend more time on financials slides because they're information dense (there's literally just more to read), because they often receive less design consideration than the "softer" slides (slap a table on there because it looks like something an accountant would make), and because they're calculating runway and evaluating business-savvy — not because they necessarily "matter more," whatever that even means.
When it comes to markets, people, sales, etc, people are more used to going with their "domain knowledge" or "gut instinct", which takes only a few seconds.
I would agree that if that's what they're doing that on that slide, then it still does "matter more".
The data presented is a sample, the "true" distribution might be different from what is shown, and if you start 'cleaning" the data based on subjective judgements, you might never discover the true range.
Note that if the sample is large enough and selected uniformly, the need for the analysis on the entire population is unnecessary.
In this case, it does't apply, though.
The sample isn't particularly large, and we don't know the extent of possible selection biases. For example one obvious bias is that the sample is restricted to data sent through Docsend, thus likely does not represent a uniform sampling of all pitches.
In any case, there is no prior reason to exclude outliers; any large enough representative sampling of a Gaussian distribution would include "outliers".
I really don't have more than basic education in statistics, but to me it looks like a slight but constant upward trend until 100 investors contacted and after that we simply don't have enough data. If you think about what the diagram talks about getting a constant upward line or even more likely a square root shaped line is very likely. Getting a bell curve is unlikely, just from thinking about the topic. What should happen that contacting more than X investors would result in less meetings?
In the second diagram removing the two dots at $4.5m and the two dots above 200 investors met leaves us with 196 data points that show a fairly equal spread and that the amount of investors contacted might not influence that much how much money you get in the end. That's also good because it's expected to find that company evaluation depends on company value (customers, market, team, etc) and not on how many investors you contact.
You make the data more conclusive by removing data points that have a big impact on your results without the backup of other data points. That's what handling outliers is.
I would agree that cutting-off curve fitting at 100-150 investors would look prettier; the thing to do in that case would be either to not attempt to fit a curve and just show the data, or alternately to fit only the earlier part (so the red line stops half-way across the x-axis), but show all the data.
The only reason for doing a curve fit on this kind of data is to show that the data fit some prior model, showing that the model is likely correct, or to show where some statistical value lies.
If it's private, how were they able to study them?
Before going to it, she said "You'll even have a private bathroom".
When I see the apt, it turns out she had a suite bathroom, and I had the other one: the guest bathroom. To this day she still claims it was my private bathroom, since she never got into it.
TLDR; private for whom? :)
Any chance you can share your infographic? I'd like to see how it wasy laid out.