Data Providers. How to maximize revenue. Part 1
Scott Hall
Data Monetization, Strategy, Sales and Product expert. Experienced Entrepreneur/Advisor.
By most accounts, there are already over 3,000 providers selling into the alternative data financial market, with some estimates indicating that 50 or more are coming online each month. At Neudata 's New York data summit on December 7th, well over 125 providers streamed in to meet data buyers. Ian Webster at Neudata mentioned that over 8,000 meetings occurred this year between data sellers and buyers on their platform. Even with a conservative close rate, this implies $100’s million in data purchases in 2024.
However, the reality at the data seller level tells a different story. It appears that only 5-10% of data sellers are capturing the majority of revenue, adding millions in new revenue, while the remaining 90% are experiencing low revenue levels. The question arises: Why does this canyon in success exist?
In this post, and in subsequent ones, I'd like to suggest topics to explain why and prompt others to contribute. First, as an engineer at heart, I appreciate formulas. Here's my take:
Product Strength Index * Sales Capacity * Frictionless Process * TAM = Revenue
This formula says the keys to revenue growth are: 1) the strength of your product, 2) your sales capacity, 3) the smoothness of your sales and client service process, and 4) your total available market (TAM).
Let's delve into the Product Strength Index (PSI) for Financial usage. This area in the formula appears to have a wide range. Some products from data providers have low PSI levels, possibly even below the Minimum Viable Product threshold needed to gain traction in the financial market. Factors like enhanced tickerization, format improvements, robust data history and limited data gap, consistent collection methods, and streamlined delivery may help explain this variability.? High PSI levels are evident in products like credit card data or well-prepared consumer insight data, and these companies generate considerable revenue.
To see what PSI might tell us, we conducted a modeling analysis of data sellers, creating a grid that incorporates a PSI score based on 12 metrics. Additionally, we made an informed estimate of revenue by gathering information from various sources, including LinkedIn and CrunchBase.?
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The modeling results support a logical trend: firms that achieve a higher score, in this case above 10.75 experience significant revenue, while those scoring below this threshold face limited revenue. This pattern resembles a canyon wall, highlighting a critical point of transition in revenue generation within the data seller landscape.
It's a logical premise that well-prepared products gain traction, a principle recognized across all industries. However, I sense that several data sellers may have entered the Financial alternative data market, bypassing some essential steps they took in their core market, to build a strong relevant product. Perhaps the allure of a call from a hedge fund makes the process seem simpler, but selling to 10, 20, or 50+ hedge funds requires a strategic product approach.
I frequently inquire with data sellers about the expenditure involved in building their core business revenue. Many mention investments in the millions. While building a Financial product may incur lower costs since the data factory is largely established, it still demands a significant investment, encompassing internal and external resources and tools. LinkedIn provides valuable insights in this regard. Examining data sellers with substantial revenue in the alternative data space reveals numerous individuals with a Financial focus, spanning product management, engineering, and sales.
Viewed differently, sales averages in the industry, sustained for many years, suggest starting at a cost level of 40% or higher. Once momentum and efficiency are established, this can decrease to 25% a common subscription metric in the SaaS world. Applied here, if aiming for $1 million in data revenue over the next 12 months, investing up to $400,000 appears logical.? ? However that level may not be possible, but with some careful spending and with new technology platform partners like AltHub , data sellers can do a lot today at a lower cost level.? Also, there are new data financing options like Gulp Data to help.
Stay tuned for more insights on this and other components in the next post. If you'd like to explore your firm's PSI score, please reach out to me, [email protected] or https://althub.com/contact-us/
Managing Director | Chief Revenue Officer
10 个月This may be my first time being quoted at the top of a report, Scott Hall, I'm flattered! I like your analysis. With a couple of thoughts / challenges: - The idea of Product Strength really boils down to 'does it make a fund some money'. Clearly factors like history, tickerisation have some input on this (for quants) - do you also use scarcity? This question 'does it make a fund some money' is empirical though, depending on intangible qualities of the data and many factors on the buyer's side (what they have already, capabilities). Well worth a go though! - My first foray in the industry was of being a one-man band who knew nothing about finance selling an exhaust product of UK transaction data. We managed to get to c. 30 clients that way, so I wouldn't agree that you need a million dollar commitment to enter this market. Or at least, I should have been asking for more money if that's true! - Quick plug for Neudata - any data owner can get their data profiled and listed on Neudata *for free*, in a matter of hours, and see how the market reacts. #leanstartups for #altdata if you will. Over 1,000 data scientists and data sourcers use our platform, providing a very useful signal of 'is there a market here for us'.