The data we use to evaluate marketplace tractionData / AI / ML
At Version One, we love marketplaces and platforms. Over 50% of our portfolio companies fall into this category including our recent investments in Headout (a mobile-first marketplace for last-minute travel experiences) and VarageSale (a mobile-first community-driven Craigslist).
We’re often asked what we look for in a marketplace. While there are many factors to consider (Bill Gurley’s list is one of my favorites), the two most important to us are: 1) high fragmentation; and 2) regular frequency of use or purchase.
Keep in mind that these aren’t the only factors that lead to success. For instance, other VCs feel that a marketplace with less frequency of purchase can be offset by a high AOV. However, for us, these two points create the foundation of our thesis and we’re more likely to dive into a startup’s data if we’re aligned at this higher level.
What type of traction do you want to see in order for you to invest?
It’s a commonly asked question and my answer is always “it depends.” Every startup is different and we conduct our due diligence on a case-by-case basis. Yet with that said, there is a general set of questions we use to evaluate the dynamics of a marketplace and assess a startup’s product-market fit.
Using Headout as an example, here’s a sample of questions we may ask a potential marketplace startup. While some of the specific details may vary based on market, any founder can use this as a primer for building out their own metrics.
On buyers (i.e. travellers/tourists):
- How do you define engagement? What does your engagement pyramid or conversion funnel look like (i.e. % of users who download app, browse your app, purchase based on overall signups or relative to previous activity)?
- How do you identify your most engaged users? Have you been able to identify common traits of power users (i.e. perhaps a demographic breakdown)?
- What is the average time spent on the app on the whole, and on a per-activity basis?
- Do you have a weekly or monthly cohort analysis that you can share? What is your DAU and/or MAU?
- What is the average spend per user? Are users sensitive to price?
- How many users have purchased more than once? What is the time between first and second time of purchase?
- What is the average time between booking and experiencing the purchase?
On suppliers (i.e. vendors):
- What percentage of vendors’ total inventory is made available on your marketplace?
- How often do suppliers list new inventory?
- What percentage of listings are purchased?
- How long is something posted before it is purchase?
- What is the average price point? What is the most popular price for purchases?
- What is the average discount offered by vendors (if any)?
- What are the common characteristics of the most successful vendors?
- For scale: what percentage of vendors do you need to sign up (in a particular area) for critical mass or starting liquidity?
- To get a greater sense of product-market fit: what is the total number of downloads to date, number of users (suppliers and vendors), etc.?
- Are there any overlaps between suppliers and buyers? If so, what percent? (Note: this question won’t be applicable to every marketplace)
- What is your GMV? What is your take rate?
- What are your sources of traffic for users?
- What is your CAC by channel?
Note that wherever applicable, I ask for average values (i.e. spend, price, buyers, suppliers, time) and absolute numbers and distributions.
As you can imagine, there are many more questions to ask but these serve as a good conversation starter between Version One and the entrepreneurs we meet, helping us develop a stronger thesis faster.
Ultimately, we invest in smart founders – ones who are incredibly passionate, ambitious, and talented. But these founders are also data-driven, no matter how early the company is. We want to know that an entrepreneur has a good hold on all KPIs, i.e. the knobs and levers that he or she can turn and pull in order to engage users and scale the business. In fact, the founders we have been most impressed with have been able to present their data quickly and communicate insights clearly.