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X or Data Exchange (transcribed as "X or Data Exchange"; spelling unclear in source)

by MikeLaunched 2014-01via Nathan Latka Podcast
See all API companies using enterprise direct sales →
Growthenterprise direct sales
Pricingusage-based
Built inAbout 12 months before revenue began
The Spark

Mike had spent years building data consortiums for large companies. He saw that the big credit card companies, wireless, cable, and satellite providers held large amounts of consumer data, but they did not like sharing it through the traditional sources. The big three bureaus and data brokers like LexisNexis offered little accountability, and the companies could not see where their data went. He wanted a system where companies could share data to stop fraud or manage credit risk while still holding and controlling that data themselves.

Building the First Version

The company was launched in January 2014. Mike funded it himself at first, saying "I put in $800,000 on my own money over the first year." That money came from selling his earlier analytics company, ID Analytics, to LifeLock. The product took about a year to build before revenue began. The first exchange was a small business risk exchange, built for large communications companies that screen business applications for internet, cable, satellite, and phone service. The architecture kept data behind each company's firewall, so requests were pinged inside the firewall and the data never left its owner.

Finding the First Customers

The company sold to a small group of very large clients rather than to many small ones. Mike said that working with the top six to eight financial services companies covered about 90% of America, and that the company was "very successful on dozens of companies." Revenue came from transactional fees of pennies per exchange, which varied with how much money the exchange saved clients and with loss rates. Mike said all current revenue was pay-as-you-go, with a subscription model planned for a new exchange focused on fraud information sharing among financial services companies.

What Worked (and What Didn't)

The company raised $6 million to date, including a $1.8 million convertible note and a $4.2 million Series A. It then extended its Series A terms to raise an additional $2 million from existing investors and a few new strategic angels. Mike chose this over a difficult Series B raise, saying raising a Series B in the current environment "is not the easiest thing to do." He also said the company had a long, slow ramp because of heavy technology investment and regulatory requirements, and that it had not yet reached cash flow positive.

Where They Are Now

At the time of the interview, the company had 10 full-time employees and one part-time, mostly in Austin with two field staff in Miami and North Carolina. Mike's target was to exit 2017 cash flow positive, which the host estimated would require over $1 million in 2017 revenue. He did not disclose revenue figures or customer counts.

Why It Worked
  • •Selling data exchange to a few very large companies means each contract covers a large share of the market, which makes a low customer count still produce meaningful revenue.
  • •Pay-as-you-go transaction fees tied to each exchange align the company's revenue with the value its clients receive, which lowers the barrier to adoption.
  • •The founder's prior exit and decades of consortium-building experience gave him credibility with large financial and telecom buyers who prize proven technologists.
  • •Keeping data behind each client's firewall addressed the privacy and security objections that made big institutions reluctant to share data, turning a compliance problem into a selling point.
How to Replicate
  • 1.Identify a painful data-sharing problem, such as fraud or credit risk, that large institutions already want to solve but cannot solve safely today.
  • 2.Design the product so each client keeps control of its data, for example by having requests run inside the client's firewall, and make that architecture the core pitch.
  • 3.Target a small number of very large enterprise customers first, and pitch them on coverage of a large share of their market rather than on user counts.
  • 4.Price on a per-transaction basis so the client pays only when the exchange returns data, and adjust pricing to the savings and loss reduction the exchange delivers.
  • 5.Plan for a long build and regulatory ramp by securing enough capital to fund heavy technology investment before revenue begins.

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