Is Node The Future Of Search?
Former Googler Falon Fatemi bills her startup, Node, as a source of "people-based intelligence" that can "accelerate serendipity" by surfacing previously undiscovered online links between businesses.
While Google, Microsoft, Apple, Amazon, and Samsung are trying to build up their respective, and in some cases collaborative, abilities in the space, new companies are appearing to fill in the gaps with the promise of greater personalization based on machine learning and artificial intelligence.
One company, Fin, is testing the public’s interest in a subscription-based digital assistant to shake up search and discovery, while another relative newcomer is Node.
Backed by Mark Cuban, Node was started in 2015 by Falon Fatemi. She began her career at Google in 2005 at age 19, making her the youngest employee the search giant employed at the time.
After exiting stealth mode this summer, Node has been working with select enterprise customers such as Salesforce and is now making its patent-pending “discovery engine” available to businesses worldwide.
Node’s “relationship graph” has indexed over half-a-billion detailed profiles of people and companies, e”ach loaded with valuable insights that can be leveraged to engage the right people, at the right time, and with the right message, enabling discovery of the next markets of opportunity,” Fatemi says.
As for why the world needs Node, as opposed to simply relying on Google, Cuban says in a statement: “Data by itself is not valuable. Leveraging data to create actionable recommendations and deliver them at the right time, however, can be transformational. By applying a people-centric methodology and using the latest AI technology, Node has created a prescriptive recommendation engine.”
We talked to Fatemi to get a sense of what that all means.
GeoMarketing: How did the company get started?
Falon Fatemi: I’m a Silicon Valley native and I was the youngest employee ever at Google when I started there at 19. I remained at Google for six years and then I spent another six years in the start-up world as well.
I spent over a decade focused on global expansion, building strategic partnerships, go-to market strategy at Google, YouTube and in the start-up world.
I also write a column in Forbes, where I talk about company-building in Silicon Valley, the growth of artificial intelligence and the future of sales and marketing.
I started Node about two-and-a-half years ago with a few ex-Google, Facebook, Microsoft data engineers and PHDs. The impetus for starting Node came directly out of the last six years during my time in the start-up world.
How did those experiences among start-ups lead to Node?
I was making a lot of introductions between people and companies and resources. I decided to do an analysis of all of the introductions that I’d made over the last six years with all of the companies that I had worked with. What I’d uncovered was that one of the outcomes of the introductions that I had made had actually resulted in life changing opportunities for both individuals and businesses. I found out that I’ve helped drive millions of dollars in investments, a number of acquisitions, a number of field and marketing partnerships, hires and even a merger.
In uncovering that and doing this analysis, I had an “ah-ha moment” where I was really trying to figure out, “What was I doing? What was my own matching algorithm that was basically giving me such a high success rate?” In some ways, I was acting as “the node” within my network, working to facilitate discovery of those right opportunities at the right time.
I thought, “What if I could actually apply technology to do this at scale?”
Thinking of myself as that node within my network, I realized, to a certain extent, you could accelerate serendipity in a way. I could help build something to allow other people to be able to discover new opportunities since that is currently done in ways that are really inefficient today.
That’s really where the idea for Node was born.
From a product standpoint, how does Node “accelerate serendipity” and promote discovery?
What I realized was that, from a technical standpoint, the way to solve this problem was by actually looking at how Google solved the issue of helping people find the right information through search. The technical approaches that they took and how you can actually apply that to solve this greater problem of discovery that we uncovered.
The premise behind the company is involves looking at the engagement model with information for the last decade. It’s all been about search — when we know what we’re looking for.
We now live in a world where there is more information created in a single day than we could possibly absorb in a lifetime. The most recent stat on this is that 90 percent of information on the web was created in just that last two years, which is insane.
What that essentially means is that as you and I are having this conversation, there’s information being created that’s relevant to both of us that we just don’t know we should be searching for.
Whether that’s people or companies or your next breakout article or my next hire, at the end of the day there’s a massive discovery problem that’s emerging. We aim, as a company, to solve this discovery problem.
We believe that in the next 10 years, the engagement model with information will change to one of proactive and personalized discovery. We’re solving that by building machines that make sense of all the people, and companies, and products, and places on the web. And then understand what you care about, and can facilitate discovery as those right opportunities at the right time in whatever application you’re in.
Our essential aim is to be that intelligence layer on all applications in the future. That’s how we’re powering discovery.
To give you a sense of the types of recommendations we are making today, we are proactively recommending the right person, at the right company, at the right time and also suggesting the right message to reach out.
Today, that right person and company is your next best customer. In the future it could be your next best hire.
Then we’re building this as a platform to actually power consumer use cases like recommending your next job opportunity or even dating or what to buy or where to go.
What kind of clients does Node serve right now?
Today, we’re focused on the B2B and in the future we absolutely will be powering direct to consumer applications as well. For example, just to give you a sense of this, a personal CRM. This is something that should absolutely exist today – in fact, LinkedIn should have done this, but it doesn’t.
Imagine an application that actually understands who you are and what you care about and proactively recommends the people that you should know, and why. It understands the companies that you should research and why, as well as the job opportunities you pursue, and the articles that you should read, all based on you.
Maybe in the future you’re advising companies on their marketing or sales or PR strategy. You can enter the kind of opportunity you’re interested in and let’s say it’s in New York or San Francisco, and immediately, Node will recommend those right people and companies, explain why and even suggest most effectively actually reach out and realize that opportunity.
Is Node’s technology primarily AI-based, or is there a human element to the mix in terms of the way it powers discoverability?
We believe in driving the future of human-centered AI. AI helps us actually become more human. It almost makes technology disappear, in a way.
The way that we’ve built our system, you know, at the end of the day, we actually have built it as sort of like we’re building a model for every person. So that’s why we call it people based intelligence.
Our system understands the person and user at the center, and it’s providing insights that are very personalized to that individual. You can almost think of it as a search engine without a search box. So it will proactively generate what that query should be, explains the recommendations, why it was made and in a way that’s highly personalized to the end user so they know how to take action on it in an effective way.
So we don’t use humans to curate it, but we do learn from the feedback that we get. It’s both passive as well as active. So we make a recommendation for a new market a company should go into. And if we explain why, then we’ll actually learn from that recommendation if the company executes on it. So we do get that feedback in a systematic way.
With the rise of “connected intelligence” and voice-activated assistants like Alexa, Siri, Okay Google, Cortana, Bixby, and others emerging to provide consumers clear answers to their questions (“Alexa book me a hotel room…”), does Node and its platform fit into that space in any way?
We have already entered the age of ubiquitous computing. And what that means is that for AI to be truly intelligent, it needs to fully understand context. It has to have context awareness. And in order to do that, it needs to have a social layer and an individual layer that we are building. So what does that look like? What does the future look like with human-centered AI and context awareness?
It’s actually projected that by 2025, there will be over 100 billion connected devices. That’s almost 14 devices per person.
What that implies is that these devices have to be context-aware. Imagine, instead of actually getting in your car and driving, opening your Google Map and figuring out where you need to drive to.
Instead, you’re just going get in your car and it’s going to actually drive you to where ever you need to go. Without you having to do anything around it. Because your AI-powered devices has that contextual awareness to understand when you should wake up based on the traffic. And your car will be ready to drive you there.
It’s really a world where we’re almost back to the unplugged age: before the internet, before all of these devices. Because AI is literally pervasive in everything, it’s almost like electricity.
How does Node’s business model likely reflect that notion on behalf of clients?
From a sales and marketing standpoint, to tie it back to that, what does that imply in terms of the future? Imagine a future where ads are actually useful, you’re actually getting ads for products that facilitate discovery for products that I actually want to buy – I just didn’t know I wanted to buy it yet.
So it’s actually connecting the buyers to the sellers they should be connected to. Imagine a world where your customers are coming to you. And that’s more and more what’s happening and how it’s being driven.
How do you expect Node’s business and service models to evolve?
Today, customers of Node are actually able to recoup their costs. So when Node was in the first 8 weeks of deployment, and to give you a sense of what this means, just in the last few months or so, we’ve driven over a $100 million in revenue for our customers.
Over $330 million in increased pipeline and our recommendations have driven 4.7 times higher deal sizes than the company average. Our recommendations are actually making our customers more revenue, faster. And then, in addition to that, we actually save them money, because we reduce the need for about 10 different point solutions on the market.
In terms of the way we operate, it involves everything from individual point solutions of data vendors to critical dissolutions as well.
Node can absolutely identify those additional markets where you can capture market share in. Node actually can recommend the next markets of opportunity for your business, which means we don’t rely on historical data to be able to actually make recommendations for query where you should go next.
For example, you can have zero customers in a particular industry, segment, or location. Because we know the prospects within that industry or location and we identify that they exhibit attributes that will drive more revenue-per-unit. We can recommend those next markets of opportunity for a business. That’s what’s game changing about Node, and that’s what’s every different than the predictive space as well.
Another way to think about the difference between what we do and what predictive does, the notion of predictive is very noble in terms of trying to help you predict your next customer. But the fatal flaw with how it’s been approached in the market, is that, “How can they possibly do that without understanding the world of your potential customers first known as your total addressable market?”
The analogy there is if Google only indexed the web pages you visited, it wouldn’t be very helpful in helping you find new information. Predictive can be really good to do look alike modeling, because it’s based on who you’ve sold to in the past. Problem is the prospects you’ve sold to in the past aren’t necessarily the prospects or markets that will drive more revenue per unit of time in the future and your customers are changing over time. Predictive is not going to help you find those next markets of opportunity or identify existing markets of opportunity that you should go after in a way that drives more revenue, faster. It also certainly won’t help you figure out who you should sell a new product line to because you have zero historical data for them to build models on top of.