Fin's Plan for 2019 Fin Analytics
Fin Analytics

Fin's Plan for 2019

In the last few weeks we have been spending a lot of time making decisions about our goals for next year.

We are at an interesting juncture as a startup. In Q4 we had our strongest quarter ever in terms of user growth and usage on the Fin Assistant service. We were also able to break-even on the cost of providing service. At the same time, we took our first step commercializing some of the key technology we use behind the scenes to make Fin work, making it available to other operations teams.

Specifically, we released Fin Analytics, which is the tool we developed internally to coach our operations team members and provide rich analytics on operations work, which we believe is a key pillar of the future of work. Dozens of clients have expressed interest in major deployments.

After a lot of deliberation, we have come to the decision to double down on our Fin Analytics product and discontinue the Fin Assistant service in 2019.

As a small startup, it is difficult to do a single thing well and nearly impossible do two things well at the same time, so we have chosen to focus on Fin Analytics in 2019.

Obviously this is a big decision that has implications not only for our team, but also for customers who have been using our Assistant service. So, we wanted to share some context on our thinking and decision and a bit about what next year holds for the company.

Background: Fin Assistant

When we started the Fin Exploration Company a few years ago, our mission was to explore the future of human + machine ‘hybrid’ knowledge work. We were very very skeptical of the pure-AI visions being floated by many at the time, but were extremely bullish on how machine learning could be applied to improve knowledge work.

We asked ourselves: Can we figure out how to practically combine modern technology and human intelligence to make knowledge tasks more efficient and higher quality?

To explore this idea, we chose to build the Fin Assistant service.

We chose this course because we strongly believe that one of the best ways to learn is by “doing.” We particularly liked assistance work (scheduling, booking, buying, research, managing recurring tasks, etc.) as a starting point for a series of reasons, including: (1) it is open-ended, which makes it hard, but, as a result, the lessons are broadly generalizable (2) it requires very high levels of quality and timeliness to be trusted and useful, which matches to most knowledge work broadly (3) it is highly personalized - people want things done different ways, which again forces systems thinking vs. deep optimization, (4) it is a service that we wanted to exist.

A few years later, we are proud of the service and experience we have built. The service breaks even on the basis of the cost of operations work, and thousands of people rely on Fin as an executive and/or personal assistant. Last quarter, in particular, was our highest growth and heaviest usage quarter ever. Our tools have progressed dramatically; our operations team has professionalized and set itself up for scale, and our ability to measure and optimize progress of a black box service for performing arbitrary knowledge work tasks is light-years ahead of where we started.

Background: The Technology & Fin Analytics

In order to deliver the Fin Assistant service, we built (and re-built) a stack of technical systems that users never see, but ultimately make the product possible. The further we drove to develop these systems, the more we became convinced that these systems / systems like them will in fact change how all knowledge work is done on teams and have extremely deep impact on the world.

We iterated through different approaches to things like how to prioritize and route the right work to the right person at the right time.

We iterated through many approaches to managing human work itself: how you encode the steps in process for people, validate answers, customize / branch preferences, manage process updates and set up tasks for machine assistance from historical or customer context on similar tasks.

We iterated through how to manage knowledge about the ‘state’ of tasks and hand off context efficiently between people.

And, we also iterated through measurement and coaching technology to help our human teams improve.

What started as vague ideas or guesses as to what systems we needed and how to build them became pretty concrete systems and answers. In 2019, we plan to open-source a ton of what we have learned across almost all of these domains in a white paper.

But, the biggest insight of the whole technical journey has been about measurement, in particular, how measurement and data can improve feedback and coaching for people doing knowledge work. You can’t improve what you don’t measure, and we believe that we have become experts in a very unique approach to this problem.

As Sam wrote about in his annual letter in October, we have increasingly come to see measurement and coaching as the fundamental cornerstone to unlocking knowledge work for the future, and we are increasingly convinced that this is something we want to share broadly with the world.

After stumbling across this insight, in the last quarter of this year, we re-built our measurement and coaching tools so that other companies that have operations teams could use them to coach agents with more specific feedback and to find the biggest opportunities to optimize process.

Fin Analytics as a tool saves the history of work done by operations teams in screen-recorded video and an action stream of work. It automatically adds context and alerting around the content and allows operations agents to ‘mark up’ the video with questions and for review by managers, as well as issues and bugs for product and engineering teams. It helps teams dramatically improve and personalize coaching, and provides deep insights to teams in how to refine tools and process.

The response has been overwhelmingly positive. There are a huge number of teams, ranging from big traditional support services, to next generation human-in-the-loop technology services that understand and are excited about the impact of the tools we have built.

We are excited to double down on helping operations teams—and potentially millions of knowledge workers—dramatically improve their efficiency and quality with the right tools.

The Decision to Focus Deeply on Fin Analytics in 2019

So, as the year has come to a close, the question is, where should we focus Fin’s attention in 2019? Small startups really can’t manage multiple products at the same time.

Based on the strong initial interest from companies, we decided to focus on our measurement and coaching tool, Fin Analytics, and to discontinue the Fin Assistant service towards the end of January.

We believe that the Fin Analytics product has the ability to be insanely impactful for millions of knowledge workers. We also believe that getting the measurement and analytics platform right will open up the opportunity for us to play a major role in the future of knowledge work and help build the knowledge work cloud we see coming.

We want to be the platform that millions of knowledge workers use for getting the continuous feedback they need to do their jobs better and more efficiently with technology, and we think this is highly achievable.

Some might ask, why not leave the Fin Assistant service running even if you are focused on the Analytics product?

The answer is that we don’t think we can maintain high quality and continue to improve the Assistant service if we want to build out our measurement and coaching service, and it doesn’t make sense to have a product like Fin Assistant in the market unless we can fully dedicate ourselves to optimizing and growing it. Unlike pure software, you can’t simply leave a service like Fin Assistant in steady state, it requires constant investment, an investment we can’t simultaneously make while pursuing Fin Analytics.

This is a difficult call to make. We believe that on-demand assistance for professionals is an important part of the future, and we don’t take ending the service lightly. But as a startup that very much believes in the future of work, we believe that it is the way we can have the biggest impact, and build the most successful company. We will work hard to recommend other services to transition our customers to.

If you run an operations team and are interested in learning more about Fin Analytics, please let us know by emailing founders@finxpc.com.

Next Steps

We recognize that deciding to go all in on Fin Analytics has implications for the customers that have bet on us and been relying on our Fin Assistant service.

We have reached out to our users who know we will be continuing service for a while to help with continuity, and we will also be helping them transition to other on-demand assistant services (or to things like upwork) if they want. If you have an assistant service and you want to make an offer to our user base, please email founders@finxpc.com and let us know. We will list you as a resource for them.

Conclusion

There was a reason we incorporated as the ‘Fin Exploration Company.’ We knew that we were setting out to explore a space that was important, but opaque.

In the coming years we are very convinced that a series of technologies will come together to form a ‘knowledge work cloud,’ which will help people be far more efficient and do higher quality knowledge work across a whole set of industries. We think that this revolution will be every bit as important as the industrial revolution.

Working on the Fin Assistant service has led us to an exciting opportunity to build what we see as a critical part of that stack, and perhaps in time will put us in position to build other parts that we see as critical as well.

To everyone that has supported us getting to this place and making this move, thank you.

– Sam and Kortina