IDI CEO DEREK DUBNER: Thanks very much, Bill.
Hello and thank you all for attending today. We very much appreciate it.
IDI stands for Interactive Data Intelligence.
We’re a data and analytics company providing information solutions to a wide array of industries driven by the process of data fusion.
Data fusion is a subset of big data – a term we’ve all heard of.
As you know, the public and private sectors are swimming in massive amounts of data – unable to structure that data – unable to make connections between that data – and to drive insights from that data.
Through data fusion, we aggregate billions of records – public records, proprietary records, and other data sets – and we ingest that data – massive unstructured data -- and we fuse that data together to create comprehensive profiles of individuals, consumers, businesses and their assets and the interrelationships.
And we present that in real-time to the financial services industry, insurance companies, law enforcement, government, retail, medical, just to name a few.
These systems are used and are applicable to just about every transaction in the United States today.
They’re used by those organizations for due diligence, risk assessment, and general counter-party risk, legislative compliance, and of course, debt recovery.
A founder of the company is Dr Phil Frost – a very large shareholder.
If you don’t know, Dr Frost is a medical entrepreneur and current CEO and Chairman of Opko Health (OPK) on the NYSE.
Another founder of our company is Michael Brauser – an early investor and founder of the data fusion industry.
Management and the founders of the company have essentially developed the data fusion industry over the last 15 years.
We created 2 of our leading competitors in this space.
In the early 2000s, we created a company called Seisint – which stood for Seismic Intelligence.
And that company was such next generation technology – a brilliant data fusion platform, massive data repository, and proprietary algorithms – that that company was sold in 2004 to Reed Elsevier’s LexisNexis for $775 million.
We then went on to create and build TLO – again, a next generation data fusion platform – fantastic technology – and ultimately sold that to TransUnion (TRU) in 2013.
And today the founders and management are back together going after the risk management space we created over these last 15 years.
Those 2 legacy systems alone generate well in excess of $1.5 billion in revenue – and the risk management industry is on the order of a couple of billion dollars.
But when we set out to build out this data fusion platform this time we desired to get 3 separate markets.
I should mention the common denominator of those 2 previous companies is a brilliant data programmer named Ole Poulsen. He’s our current Chief Science Officer.
Ole was the primary systems architect of those 2 legacy systems and the developer of our current platform “idiCORE”.
But in developing this platform we not only wanted to go after the risk management industry that we know so well but we wanted this platform to be able to address orders of magnitude larger in markets and transactions.
We’ve always had a desire to develop the platform so that if I can look at our systems and get a comprehensive view of a subject from a risk management profile why can’t I do that for the right product and the right consumer.
As we all know the marketing industry is orders of magnitude larger than the risk management industry.
So we built the systems today to be data and industry agnostic.
We can load that system with data that might not be regulated by federal law for example or highly sensitive information -- more behavioral data or ethnographic data or meta data -- and load that and present that in real-time to advertisers in all of their transactions.
So we had a desire to enter the consumer marketing space.
And the third place we desired to be is custom analytics – and that’s simply to take this very flexible data fusion platform with its intelligent design and applications and layer that system over end user databases – not just our data.
So as you can imagine we could layer it over a shipping company’s data set to create those connections that they don’t have today for logistics.
We could layer it over financial services, layer it over law enforcement or government.
You’ve probably heard of one of the leaders in this space today is Palantir, a private company, $20-$25 billion valuation.
Palantir was born out of Paypal’s (PYPL) anti-fraud activities – much in the way our systems have been designed for years.
In fact, Seisint, had it not been sold...
Seisint was fully entrenched in the federal government.
Seisint’s technology was credited with finding the 9/11 terrorists and the Beltway sniper.
In my opinion, had Seisint not sold in 2004 it would have been Palantir today.
So for us that’s a 3rd market worth addressing.
We’ll layer our platform over those end user databases.
In building out the risk management space like we did in 2015 -- which was more of an investment and development year and we made great strides -- we had a desire to move quickly into the consumer marketing space.
And while we could certainly have built that out organically and we were embarking on that we found an unbelievable acquisition in Fluent, a NY based digital marketing company.
We acquired Fluent in December 2015.
Fluent brings massive synergies to IDI.
We have our platform -- so now we have massive data of Fluent – 120 million consumers’ self-reported data.
We can move that data into our risk management profiles.
We can glean insights from that data.
We can take Fluent’s data and its customer acquisition technology platform and move it into our cloud environment – which we’ve done much differently than our two legacy systems.
And Fluent is just a brilliant company with a brilliant future.
This is our first presentation after the acquisition.
And with that, I’d like to introduce you to Ryan Schulke, the CEO of Fluent.
FLUENT CEO: Thanks Derek.
I’m really excited to share a bit about our business with everyone.
Fluent is a people-based marketing customer acquisition platform.
Traditional online advertising and offline advertising has historically been about targeting audiences contextually.
Fluent actually approaches the model differently.
We’re working with advertisers across all verticals to help them identify individuals interested in their products and services -- and go out and help them acquire customers.
At the end of the day, our partners are either buying opt-in data from us or traffic from us – that could be through their app, through their website or whatever it may be.
So that’s Fluent’s core model.
What’s perpetuated people-based advertising really started with Facebook (FB) and now Google (GOOG) with Customer Match.
The cookie is eroding as an effective targeting tool – not just due to lack of contextual relevance but also because of the growth of mobile -- cookies are just not an effective tool for targeting.
The email address is quickly becoming the #1 indicator of who an actual individual is and the capability to target them across all digital channels up to and including television.
So our capability enables us to go out, collect massive amounts of first-party self-reported data from consumers and work with our advertisers to more effectively target against that data.
The way we do it is unique and differentiated in terms of what we actually produce.
Fluent hosts and manages hundreds of different promotions and offers which we independently drive traffic to through paid media.
Through that process we’re discovering more about an individual.
So they’re signing up for anything from call it “you and 4 friends to the Super Bowl” or “sales jobs in your area”.
We host and manage all these promotions, we interact with the individuals, there’s interactive content, they’re taking surveys through the experience, and we see over 500,000 registrations every day on our platform in the US alone.
We’re discovering lots of insights about these individuals that are not available on other static data sets you might buy offline or through third parties.
We’re actually asking 1on1 individuals about their lifestyles, interests and then matching them to the best marketing offers in our inventory based on their qualifications.
This is how our platform effectively works…
We go out and drive traffic -- we’ll spend ~$100 million in 2015 on paid media buying traffic through mobile, email, social.
We buy traffic pretty much everywhere to drive to our promotions.
The individuals then landing on a promotion or offer -- they’re signing up, we’re collecting their registration data – name, email, mobile phone number, home address – and then we’re asking them questions about their lifestyle and interests that’s predominantly based on the ad inventory we have available in market.
We’re featuring different targeted ads to them -- and again our clients are either buying opt-in data from us or clicks.
What does that mean?
That could be Western Union (WU) looking for someone to sign up, learn more and get savings from Western Union or any of their new products.
It could be Overstock (OSTK) with a 20% discount off your first purchase and they’re going to buy that opt-in record from us transactionally.
On the other hand, we could be driving traffic to the Western Union app for instance, and then they’re going to pay for that click and for us to drive traffic to them.
While this is all happening we’re collecting all the data associated with the session.
We own all that data from front to back.
So we understand where the click came from upstream -- was it a Facebook ad, was it an email marketing message, was it from a mobile app – right on through the session.
So we have all the metadata associated with it -- are they on an iOs device, desktop -- and all the data that they submitted to the registration process and the survey.
So, we’ll have collected over 2.4 billion survey responses on consumers in 2015 and that number grows every day as we discover more and more about the individuals that we’re in front of.
Our databases have over 120 million unique email addresses that represent over 50 million verifiable US households. So we touch a very large footprint and have a very fluid audience capability.
Just today I was able to text one of our product managers to see who was trending ahead in the Republican race in Ohio.
So we use our data collection capabilities to support our ad serving but also our product roadmap as well as market insights for industries that we’re playing in strategically.
We’re very well diversified operating health, financial services, retail, consumer packaged goods -- just to name a few.
We’re working in verticals across the board – of course, right now seeing good lift from the political and college based marketing categories.
We see great client retention.
We work on a pay-per-performance model. All our advertisers are very ROI centric and we work with them to understand attribution, what their conversion goals are, and we work towards those.
Our model is very scalable.
We announced today that we did $144 million in topline.
I believe we had a pro-rated headcount of about 65-70 people in 2015.
So it’s a very scalable model as we continue to drive into verticals on the strategic front.
Obviously everyone knows digital advertising is growing.
I like to remind people that everything is moving to digital.
You can already go out and target individuals on their set top box – not audience-based or contextual anymore -- but by actually knowing who they are.
So this will continue to grow over time -- digital and data focused digital advertising is really here to stay and it’s something that’ll grow at a very quick rate.
Going into our differentiators, it really comes down to a suite of ad targeting products that are differentiated that are really not available in market.
We're really going at this a different way.
Rather than trading third-party static data sets like a lot of the consumer marketing companies you’ll see in the marketplace, Fluent is collecting them directly.
We have the ability to pull in third party attributes should we want.
But for the most part we’re collecting insights directly from the consumer. It’s real, self-reported, it’s recent.
We’re seeing 500,000+ people a day that we’re dialoging with across the country and we’re able to perform superior ad targeting as a result.
When it comes down to our data, our 4 pillars of data drive them to demographics, psychographic, metadata as well as behavioral data. So we really understand a lot about how these consumers behave.
Our advertisers are also supplying us with conversion data so we’re able to optimize their campaigns and better understand what’s that best customer for that advertiser. But we also understand more about our audience and how they behave and how to convert them for the category at large.
We’re 80% mobile. That’s the way world is heading right now. We see a lot of activity on mobile devices. We’re a mobile first company.
When it comes to optimization, digital media, and now being able to build content and re-target individuals that we’re dialoging with daily on our platform is something that’s just starting to take off in the industry – being able to cross channel with the targeting capability -- when we know the individual suffers from heartburn twice a week and we have a new offer to market for it -- and we’re able to go out and re-target them through social or Google Display or onboard with LiveBrand who we just recently announced a partnership with.
Our pricing model is really built for scale. We want to know that our advertisers are getting return on ad spend.
Once we’re hitting those metrics often they don’t really enforce caps on what we do – that’s what really enables us to scale the business so quickly.
In terms of our technology, we have touchpoints and integrations with all the major ESPs out there.
So, when their clients are going out looking to grow their database Fluent is being referred as the partner of choice to go out and accomplish that goal for them.
We’re working across all verticals.
These are just a couple of case examples:
We’re starting to see larger demand for opt-in data from advertisers almost across every vertical right now because of the ability to cross channel target.
It’s not just collecting an email address with some insights on it to just send email marketing – though that’s a very effective tool.
Now our advertisers are able to go out and target across channels – they might be buying an email address and start an email marketing campaign but are also then going out and featuring their Facebook ad to drive an app install through social media.
So, as we see advertisers start to get a lot more utility out of opt-in data and owning that data we’re seeing more demand for our products -- and our business is growing at a healthy clip as a result.
We saw a lot of growth in 2015 on the back of certain advertisers starting to understand how to utilize our platform.
We’re really only a 5.5 year old company founded in 2010 completely bootstrapped grown organically from Day 1.
And as we have started to educate advertisers on the value we can provide them we’re starting to see their media dollars shift over to us and them start to spend more rapidly. So it’s a very exciting trend for us.
In terms of our growth opportunities, we’re only focused in the US.
This product can operate across and is transferrable completely internationally.
Myself and our co-founders have experience with international businesses prior to Fluent.
Increased sales capability will allow us to drive into verticals more strategically.
We’ve historically been pretty lean as a business and now we’re starting to build up and bring in some senior expertise across different verticals we’ve pegged for high growth.
There’s a lot of opportunity to grow our business.
CRM capability – the ability to re-target individuals through both push and pull channels – push being things like email, push notifications, SMS – low cost channels to deliver messaging, to re-market to our audience -- but also through paid channels like Facebook or Google Display or onboarding with LiveBrand -- really accessing any of their media partners we’ll be able to scale out our business -- as we have a heightened understanding for who these individuals really are and the types of marketing messages they’re going to be most receptive to.
In terms of synergies, let me pass it back to Derek.
IDI CEO: Let me first address the question people ask:
How are we different from the legacy systems we created?
There’s a big difference.
Today we’re built in the cloud.
It’s almost inconceivable today to think about massive parallel processing and not be in the cloud.
So what that delivers for us today is in the traditional construct -- you have to build the data room, you have to spend tens of millions of dollars on servers, raised floors, and the personnel around it -- today we don’t have that.
Being solely in the cloud we have elasticity -- so we can ramp up during peak periods – and non-peak periods we bring that down.
What that does is give us greater efficiencies and we deliver those efficiencies to customers.
And we’re already seeing that pay off. So we’re very excited about that opportunity.
Also different today is with the proliferation of data over the last 15 years.
Think about where we’ve come from.
There was no social media. Online commerce was just breaking in. There were no mobile phones or other devices.
Now we have massive amounts of data with greater relevance today than that period.
So, for example, your landline is far less important in certain scenarios than your cell phone.
And so with this proliferation of data you have to be able to process this data.
We have unbelievable processing power that I haven’t seen in my 15 years in this industry.
And so that’s a game changer.
We believe that’s very unique and compelling.
Also, with the massive data sets we aggregate, we house that data in our cloud environment. We have that data in-house.
It’s a fixed cost model.
We license that data where we aggregate that data in mass and we have fixed costs wrapped around that for long term periods, unlimited use of the data.
And then as we build out our infrastructure built out in the cloud we have fixed costs, controllable costs.
We’ve already demonstrated previously in the last 15 years and we’re doing it again – that you’re going to see very high double-digit margins.
All over the country people are searching and transacting with our systems – everything is a search, a payable search.
We also have models where computers talk to each other where we process millions and millions of records on a frequent basis -- all billable and transactionable.
All of that drops to the bottom line as our model matures.
Also important is that on our side, the risk management side, on the IDI side prior to the Fluent acquisition – we get transactional data where consumers leave their footprint – if you rent a house, you buy a house, you buy a car, you get professional licenses, planes, boats, automobiles -- all of that is transactional data.
But what’s very unique in this broad-based data analytical company that we built is Fluent -- because now we have Fluent driving in massive scale consumers into that funnel off the Internet and self-reporting data.
So the insights we glean from those data sets…
For example, we know that a new address popping up on our risk side is some type of establishment of a household. That’s a monetization event for Fluent – without cost.
Fluent then shows home warranties, home insurance, home furnishings and the like.
So those are some of the insights that are very important.
Fluent, also, while working with the largest advertisers in the country has built their trust.
So they’ve got the very largest organizations deliver back the conversion data to Fluent saying: “These are the customers you’ve brought me. Now model against it and figure out who that next customer is”.
With IDI and our cloud infrastructure we’re now approaching them and saying: “deliver us more than the conversion data because we already have all the sensitive data, we have the security in place, we understand how to do this. Deliver us that customer database, let us use machine learning which is changing the paradigm and identify and segment those best customers and then model it against Fluent’s transactions going forward”.
So those are just a couple of use cases here that are very important to understand.
Also, in that environment we can take Fluent’s data, load it into that platform I told you is data and industry agnostic, and let Fluent build its own platform to deliver to advertisers to do real-time searches to find out the right consumer for the right product.
Some of the top use cases in big data analytics -- IDI addresses all these problems in the market and brings innovative solutions to them.
And lastly, just a quick sheet on the competitors, which I’ve already talked about -- 2 of which are companies we built and sold to larger competitors.
As well, now that we have the marketing division we have additional competitors including Alliance Data Systems (ADS), Acxiom (ACXM), and Criteo (CRTO).
That concludes the presentation for today.
We’re happy we could tell you our story.
NOTE: More IDI analysis HERE.
NOTE: More IDI analysis HERE.