Understanding the disruptive forces behind Simon Data’s Series D

“Your algorithm is cute but it’s just not powerful enough to account for real-world problems with complex, non-linear decision criteria.”

I had just delivered a conference talk for a paper that had won best paper award at the prestigious International Conference on Machine Learning. It was my big breakthrough, and it ultimately paved the way for me to finish my PhD. Over the next several years, the paper received many thousands of citations and was regarded as a seminal piece of work.

Yet despite all of this, detractors were out there with competing methods and alternate approaches. These words came from the NYU professor Yann Lecun – a quirky yet dogmatic researcher who focused exclusively on neural networks. This was in 2007, an era when these methods were still mostly science fiction, and I passed Yann’s criticisms off as just that.

Fast forward 5 years later – his science fiction turned into reality with breakthroughs around graphics processing units (GPUs). Google Brain demonstrated that Yann’s “quirky” neural networks could be trained to solve some incredibly hard problems (albeit identifying cat videos on YouTube). So-called “deep learning” was born, and Yann went on to be Meta’s Chief AI Scientist, and won the prestigious Turing Award in 2018. 

Disruption is all about timing. Yann was technically correct with his words in 2007, but the underlying technology requirements (advancements in parallel computing with GPUS) as well as many of the key applications (large scale UGC video) weren’t there. When these two pieces came together, his core thesis and research did as well – opening up a new class of technologies that today do everything from monitor the internet to automatically drive your car.

Fast forward to today, and Simon Data’s Series D again represents a unique moment where we’re seeing a convergence of trends. 

  • Cloud Data Infrastructure has reached an inflection point where many brands have invested deeply in data teams and centralized data infrastructure with platforms including Snowflake, Bigquery, and Databricks. Yet access to these platforms outside of basic reporting is still incredibly difficult – and unlocking end business value with so-called “data applications” is in its infancy.
  • Gen AI & Large Language Models (LLMs) have taken the world by storm. These methods represent a generational shift in the potential that AI can have to understand customer data and to translate this into both improved automation and optimization of customer outcomes. In 2007, state of the art neural networks had 1 million parameters. In 2012, Google Brain was developed with over 100 million parameters. And today, the latest version of GPT-4 has 1.76 trillion parameters.
  • Today’s Macro & D2C Disruption has caused brands of all scales to rethink their strategies. COVID brought everyone digital, and today’s “back to normal” trends have omni-channel online + offline strategies as table stakes. The broader macro has put a new lens around growth priorities – shifting priorities in 2021 from acquiring new customers to focusing on LTV and customer value over the past 2 years.

In a similar fashion that Yann told me over a decade ago that my research was fundamentally limited, Simon Data has been preaching our vision and forward looking approach for the past 8 years. Today, we’re entering a perfect storm where the differentiated value of our platform aligns across a massive set of shifts in data, AI, and marketing.

So what does the future look like for us? 

Simon will fully understand your data in a way that allows marketing to work independently and fast.

Data access and data workflows is the foundational problem affecting all marketing teams today. Access is still very much gated through IT and data teams – and technology limitations are at odds with much of the process changes that enterprises are pushing toward as they seek to get more out of their first party cloud data investments. 

This starts with the right data architectures that permit secure and real-time data access and ends with AI-enabled data access that works by clicking a few buttons or telling Simon in a few words what you want to do. “Include a row of recommended products that you’d wear in rainy weather.”

Simon will both identify & take action on opportunities leveraging deep, AI powered insights

along with robust capabilities that work across the entire lifecycle. We’ve spent the past 8 years building a robust application to target customers in acquisition, first purchase, and core retention contexts across owned, paid, and offline channels. 

Our next-generation fully connected architecture builds on this – and the infrastructure we’ve developed across access to high quality data, semantic data understanding, and prescriptive use cases is the perfect set of conditions on which to build our next-generation of AI capabilities. This will include capabilities such as auto segment creation, customer lifecycle predictions, and 1-to-1 content personalization.

Our ability to execute these goals is unlocked by the foundational work we’ve put into our platform to date. Today, our infrastructure is uniquely built to fully leverage state of the art in-cloud data. And our application provides robust end-to-end capabilities to drive a full set of segmented, personalized, 1-1 data-driven actions. It’s somewhat of a “just add water” moment in time to bring everything together.

The last few days since we announced our financing have been a whirlwind of press interviews along with customers and partners reaching out in congratulations. As a values-driven organization, we focus as much on “the how” as we do “the what” – and I was particularly impressed by TechCrunch reporter Ron Miller’s eagerness to understand our values and how they relate to our DEI initiatives. 

One aspect that I wasn’t able to cover with Ron was how our values relate not just to how we build our business internally, but also to how we build our product and how we work with our customers. When I talk with the team about ownership, it’s about aligning goals and making sure that everyone individually is in the right spot to affect outcomes directly.

Yet for so many marketers today that isn’t the case. Taking ownership of their enterprise data is just too hard – yet doing so is more critical than ever to affect the data-driven requirements for success in today’s hyper competitive environment.

In short, the problems we are solving are bigger than ever, and the solution we’ve been building over the past eight years is now perfectly timed. We’re thrilled to get back to work – and enable our customers to take full ownership around a next-generation of customer experiences.

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