Fueling AI With Audience Data: Leadspace CEO Talks Data Management, New Funding

January 18, 2018

Demand Gen Report -- B2B organizations, as well as venture capital investors in the tech space, understand that data management is vital to fuel marketing and sales initiatives. So much so that VC firms are keeping a close eye on analytics vendors in the space that are streamlining the process for their customers.

For example, Leadspace, a SaaS-based audience management company, recently received a $21 million round of funding to help grow their platform. The company stated that the funding will position the company to better support B2B marketing and sales teams as better data management remains a top priority to fuel new initiatives and tactics, such as artificial intelligence.

Doug Bewsher, CEO of Leadspace, discussed the company’s recent round of funding and their plans to grow their Audience Platform solution in an interview with Demand Gen Report. Bewsher also shared his thoughts on the relationship between data management and AI and how it will impact targeting capabilities now and in the future.

 

Demand Gen Report: Can you elaborate on your plans for the funding and what parts of the Audience Management Platform you’ll be focusing on growing?

 

Doug Bewsher: I think first and foremost, as we think about the opportunity that bringing on a growth equity partner such as Arrowroot creates, the investment that we have is really to continue to double down on our go-to-market, our sales, our marketing and our customer success. We have tended to focus more of our investment historically on building a product that customers like. Now, I think this next round is to invest really in sharing the stories, helping people understand how to use the product, and so forth. So we will be building out our team in Denver, which is where we’re building our second office, creating a sales team and all that usual go-to-market stuff.

As always, we’re very much focused on being a product company, so the second step is to continue to build out the Audience Management Platform with ABM. So many people in the space are trying to figure out how to deploy an ABM strategy now, and I think what’s really good is there is starting to be a well-defined roadmap for doing that. It’s how you figure out who your audience is, the right way to engage them, how to execute that engagement and then how to measure it.

However, it all starts with the audience — who should you be talking to — and starting from an outside-in view, not from an inside-out. It’s not about starting with your database and figuring out who to sell to, but starting with the whole audience and figuring out where to go. Figure out the right companies, figure out the right people in those companies, figure out the right way to track those first three steps, and that’s really what we’re doing with the Audience Management Platform.

Really, with this funding, we’re going to invest in the activation layer now. How do I enable people in sales, people in marketing, people in advertising and digital media to take the audience, take the intelligence about that audience and make it usable in all of their different engagement activities? We get that activation piece right, that is the final component of really driving the transformation that we’re trying to see in a way that B2B sales and marketing folks see us.

 

DGR: Since AI is fairly new, how are your customers reacting to it and what kinds of concerns have they brought to the table?

 

Bewsher: It’s a great question; there’s a lot of AI hype and myth. A lot of the customers that I talk to are trying to break down, “How do I make AI actually useful and meaningful, and deliver results?” I think most people recognize that if they don’t start to understand, experiment and figure out how they could use AI, sales and marketing teams are not going to be positioned for success in the next two, three, four years as it starts to go mainstream.

Secondarily, as you think about the skill set of individuals five years ago, it was really important for us to be able to do sequel coding or to be able to use Marketo as a core component as marketers and sales people needed to start thinking about being more technically literate. I think we’re seeing the same here in which it’s an important skill set for individuals, not just companies, to think about. We’re very focused on how to make AI actionable, how do we make it real so that sales and marketing folks can use it? This includes not sharing some beautiful vision that no one can execute, which leads to disappointment. There are use cases of people seeing real value from AI today. A really good example of this is look-alike modeling. The ability for us to take 10 or 20 customers, and then to very rapidly expand based on all the intelligence that we can bring together from our data management platform and the modeling in order to say, “This is the next 100 customers you should go after,” is an example of AI. In that situation, what we typically see is maybe 50 out of that 100 customers we bring back are already customers or they’re prospects, which is a really good validation, but then that next 50 is the next 50 that you should go after. I think this is all about that journey for us, finding these simple results of ways to see value and then grow over time.

 

DGR: There are some notable investors in this round, such as Arrowroot Capital and JVP. What do the conversations sound like with potential investors, especially when it focuses on newly emerging trends like AI?

 

Bewsher: We’re very excited about Arrowroot. Clearly, they’re a great equity company, focused really on helping companies start to scale. What’s also exciting for us is we’ve got the strength in a company like JVP, based in Israel, to help us drive the technology side of our business. I couldn’t be happier.

As we talk to them – and in fact, as you talk to the space in general, I think – everyone is looking for who is going to be able to really drive value through the deployment of AI. This includes the company, the technology, and the product and solution that will enable companies to really see success. The market opportunity and the way that will change B2B sales and marketing is huge. I think what’s exciting for us is that we are the first company, I believe, within two years in our space, to take that next step in terms of Series C with a new round of funding in this AI predictive analytics space. I think that is because the investors are seeing our customers — whether it’s Oracle or Microsoft — and the value that we can really expand and tell that story. I think that’s what’s exciting to them and really, the conversation we have with them now is how do we continue to share those stories and help the rest of the industries be successful? I think that’s important.

Obviously, you have a company like Salesforce with what it’s doing with Einstein within the Salesforce ecosystem and pushing this forward as well. I see companies like Oracle with adaptive intelligence and Marketo with their focus. These are really exciting companies pushing within AI. What’s exciting is that this is a bigger idea that is actually about how we connect the dots across these different engagement platforms to really just drive an understanding of the customer at a higher level and at a complete level, and outside of specific channels.

 

DGR: It’s clear that data management and AI go hand-in-hand at Leadspace. Why has this become such an essential pairing to maximize targeting capabilities?

 

Bewsher: The simple answer to that is this: garbage in, garbage out. It’s very clear from all our customers we talk with that, if the underlying quality of the data that they have about prospects, customers and accounts is poor, then clearly any recommendations and AI that we might run on top of that will be really bad. If you’re in marketing or sales and you’re dealing with people under big pressure to deliver results, you do not want to be taking bad recommendations based on bad data. We’ve always believed since day one that the basis for all good AI is having a good data management platform. We’ve invested heavily behind that with the partnerships we have, such as Bombora and others, to really drive that data platform off so we can build really good recommendations.

Intel was a very good example of this, they ran an RFP for data management and for AI, or predictive analytics as they called it in those days. We said to them, “First, you got to get your data platform in shape and second, you can start to build predictive models and analytics that live on top of that.” They’ve got that entirely. We wanted to work with them on the data management side, and now we’re building out the predictive side for them. This is the journey that we think our customers need to undergo: start with building the data platform, get the right data in place, then start to build the analytics, and then you can drive the ABM programs off the map of that.

This is the journey. We can move through that journey very quickly with our customers and most of the customers move that way. Again, it’s very simple for people to recognize that good AI is based on good data, good data gives you better AI and that’s a continuous learning loop between the two.

 

DGR: What’s next for Leadspace? Where do you see the company heading and where will it be in 2020?

 

Bewsher: We recently did a survey, and the number one thing people hate is managing data. The number one thing that they love is building creative campaigns. We will continue to strive towards helping marketers by solving the data and the intelligence challenges. That way, our customers can focus on building great creative campaigns. We have an awesome creative person here who does brilliant stuff with PFL, for example. Or if you’re a salesperson, you can focus on how to have compelling sales conversations and drive conversion — not spend their time worrying about the data analytics.

We will continue to focus on this journey through 2020 and beyond. Once we get the data right, once we get the models and the analytics right, the next step that we’re investing energy in is how to make recommendations, how to go beyond rules systems which all sales and marketing people use today. Based on all this data I have available, what should I do? Which campaign should I send to this person? What should I do? Then, the marketers themselves can focus more on building great campaigns and the salespeople can focus on selling. That step from data to analytics, to recommendation, is where my focus will be over the next couple of years in the product investment. We’ll continue to focus on the activation and then the recommendation element. 

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