{"id":9217,"date":"2021-11-30T07:00:18","date_gmt":"2021-11-30T07:00:18","guid":{"rendered":"http:\/\/TheNextWeb=1374403"},"modified":"2021-11-30T07:00:18","modified_gmt":"2021-11-30T07:00:18","slug":"how-my-startup-completely-transformed-its-revenue-model","status":"publish","type":"post","link":"https:\/\/www.londonchiropracter.com\/?p=9217","title":{"rendered":"How my startup completely transformed its revenue model"},"content":{"rendered":"\n
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Building and scaling a startup<\/a> inevitably involves revisiting and changing some aspects of your approach. But one of the most daunting pivots to make is your own revenue model.<\/p>\n

If you\u2019re considering having to change the way you generate your income, then you\u2019ve probably identified that your revenue model is a barrier to your startup\u2019s further growth.<\/p>\n

However<\/em>, at the same time, you\u2019ll likely be dependent on your current revenue model to generate the income you need to keep the lights on and pay your team.<\/p>\n

In this way, changing your startup\u2019s revenue model while keeping up with your own growth targets is analogous to trying to swap out an airplane\u2019s engines mid-flight. So, how can this be accomplished?<\/p>\n

Well, here\u2019s a story of doing just that at my startup, Seldon \u2014 where, in just over a year, we shifted from 71% of our revenue coming from consulting to 88% of our income coming from selling a product.<\/p>\n

Why we changed our revenue model<\/h2>\n

In 2014, I founded Seldon with a small team of engineers. We\u2019ve had a simple aim over the past seven years: to accelerate the adoption of machine learning (ML) to solve some of the world\u2019s most challenging problems. To that end, we specialize in providing DevOps teams and organizations with the tools to deploy ML models at scale.<\/p>\n

To achieve this, our team has created a transformational open-source<\/a> project, Seldon Core, that has formed the basis of thousands of organizations\u2019 ML tech stacks. Seldon provides teams with the means to deploy ML models on whatever cloud infrastructure suits them.<\/p>\n

This open-source model has enabled the technology to be verified by an open community of experts and constantly improved upon by thousands of users. Since we launched the project in 2018 it has attracted over five million installs, a testament to the strength of the technology.<\/p>\n

Despite being free to use, Seldon Core served as the main funnel for our income by providing consulting services and support on top of the platform for large organizations, making up 71% of our revenue in the year ending March 2020.<\/p>\n

If we wanted to achieve our ambitious goals for growth, however, we needed a source of revenue that would prove much more amenable to scaling up.<\/p>\n

Along with having to convince organizations of the value of paid support, focusing our revenue model around support meant more of our current resources would have to go into maintaining existing relationships rather than building new products and services that could bring more customers over to us.<\/p>\n

Pivoting to product<\/h2>\n

Rather, we decided that we should instead move towards an \u201copen core\u201d revenue model, which saw us continue to offer Seldon Core and other open-source projects whilst also offering a more extensive paid option that integrated and built on our open source offerings.<\/p>\n

Therefore, we began to develop Seldon Deploy \u2014 a fully integrated enterprise solution that brings together Seldon Core\u2019s technology along with additional tools to help customers understand the behavior of their models and monitor the performance of their ML infrastructure.<\/p>\n

Following a couple of years of building and working closely with some of our early adopter customers on MVPs, Seldon Deploy finally hit v1.0 in February of this year. A major focus for our team was converting our existing customers and users from the open-source community into Seldon Deploy subscribers.<\/p>\n

Deploy was designed to provide additional value to users above and beyond that of Core, offering additional tooling that provides a comprehensive ML deployment solution.<\/p>\n

To help ensure teams could make the most out of Deploy, we built up teams of account executives, solutions engineers, ML engineers, and customer success managers to maintain relationships with our clients and liaise with product and tech teams to ensure any queries or issues are quickly resolved.<\/p>\n

In practice, this meant that moving to Deploy was a pleasant upgrade for our existing customers and, in turn, helped us slowly shift our revenue model.<\/p>\n

The results of Seldon Deploy<\/h2>\n

The results have been stark \u2014 our revenue has continued to grow, but its composition has already markedly changed since March 2020. By the end of Q2 2021, our new solution had surged to make up the overwhelming majority of our income: 88% of our revenue came from product licenses.<\/p>\n

How did we achieve this? Our takeaways are:<\/p>\n