Wednesday night I had the opportunity to have dinner with a group of folks who were well above my weight class. Jason Calcanis (LAUNCH, Maholo, general rabble rousing) was in town meeting with Dharmesh (Hubspot, OnStartups, angel investor) and I was able to hustle my way into a spot at the table that included a number of other Boston heavy hitters.
The table conversation swayed from catching up on the Boston tech scene to old war stories of trials and tribulations on the warpath of business, but one of the more fascinating topics concerned using tech stature and advice for paying it forward. Both Dharmesh and Jason have given their time to countless entrepreneurs, and in some instances have even auctioned off lunches and meetings for charitable organizations. As the conversation progressed, the group around the table evolved into discussing what any group of highly competitive individuals would:Who could wrangle up more cash for their time?
Rather than leaving the question to the imagination, I decided to use our software to get some real data to determine whose time (between Jason and Dharmesh) is worth more. If you just want the results, skip to the end, but we’ll briefly go through the methodology.
The Scope of the Study and Our Methodology
At Price Intelligently, we build value based pricing technology to reveal true customer willingness to pay and price sensitivity. Without getting too deep into the pitch and the boring bits, essentially we allow you to stop guessing on your pricing and have real data from your customers about what features and benefits are most important to them, as well as what price you should truly be charging to maximize revenue.
For this study, we targeted entrepreneurs who were pre-series B in the Boston tech space as the target customer persona, who had knowledge of Dharmesh and Jason, and were interested in seeking their advice.
We positioned “the product” as a 60 minute meeting with Dharmesh or Jason that included a meal at a 4 star or above restaurant, the option of bringing an additional co-founder, and where all the proceeds went to a charity of Dharmesh or Jason’s choice.
In terms of methodology our software collects data directly from customers and then crunches those numbers through our algorithm to reveal the output you’ll see below. It’s based on some fancy science, but for more on the process and validity, check out our pricing is a process blog post. We also dug into the relative value of reasons why these entrepreneurs would want to meet with Dharmesh or Jason, which hinges upon the basic premise of forcing the respondent to make choices between options.
The big reveal: Who’s worth more in Boston?
After crunching the numbers, the data revealed that Dharmesh’s time was worth between $2,521 and $3,336, while Jason’s was worth between $1,781 and $2,430. The Indifference Price Point (IPP) for each was $2,983 and $2,244 respectively. For context, the IPP is also known as the “median price point”, where half of respondents believe the product is expensive and half believe it is inexpensive. For this reason and given the fact that the sample sizes for each study were smaller (n = 40 or below), comparing IPPs is much more valuable than comparing the optimal price ranges.
Here’s Jason’s output:
A big thing to notice here is that the price sensitivity graph (the brown one below) is relatively flat, which makes sense, because in a variable good like time with a rock star entrepreneur, you’re definitely going to have a lot of variability, unlike a commodity like gold where the profile of the graph will be much more defined.
The same goes for Dharmesh’s output:
Although Dharmesh’s output isn’t as flat as Jason’s, it does have a wide bottom, which means that there is greater predictability for a higher price point within the range, as opposed to a lower one.
For both heavy hitters, if they were to sell their time in an auction model they could definitely exceed the limits of their range, because as you notice in the price sensitivity graph, some respondents were willing to pay much more than the top end of the optimal price band. With a singular, fungible product like time, you don’t need to sell to the masses, you just have to find one person.
Why do they want to meet with Jason or Dharmesh
Knowing a price point is great, but as we mentioned before, getting into the purchase intent or even what “features” of a particular product is especially important, which brings us to our “feature” utility product. Both Jason and Dharmesh’s output aligned very closely to one another, so in the nature of space, we combined the data to give you a concise look:
As you can see, clearly these two know a thing or two about giving away cash, as that was the number one area, followed by marketing. On the other end, thankfully these folks knew what they wanted out of the pricey meeting, because few found just general networking to be worthwhile.
Keep in mind these results are targeted
Keep in mind that these results are for Boston area entrepreneurs. Dharmesh is one of the head honchos around here, which definitely had an impact on the results. As I mentioned before, the sample size is targeted, but not huge, and for a “product” like their time, especially in a charitable setting other “customer personas” may pony up much more cash.
In all, these results were pretty cool to see, especially because there’s clearly some cash being left on the table by any area groups looking to raise money for charitable purposes or just to give back to the community. As such, someone should take up the banner and get some dinners, luncheons, or workshops going with this model: TUGG, Intelligent.ly, Dart...I’m looking at you.