“Thank you, Judah. To quickly introduce myself, as Judah had mentioned, I run product marketing and SaaS demand generation for the Nielsen DMP and the Nielsen Marketing Cloud. I’ve done this for the last few years, and what we’re looking at here in this particular slide to give you a specific use case on how we’re using Squark, and predictive AI to better our ability to effectively generate revenue for our sales organization.
And this slide sort of sets the context for that. We’re looking at a B2B marketing demand funnel. From inquiry, which is sort of initial conversion, all the way through closed deals. We really needed to understand, as a marketing organization, how to better predict what drives prospects and new audiences through the demand funnel. What brings and initial inquiry to the Nielsen Marketing Cloud or the Nielsen DMP to actually being accepted as a sales lead and closed as a deal, which we can then attribute back to our own activities?
It really comes down to three, core criteria, which, very simply put, are understanding how we can predict WHO is most likely to become a sales qualified lead and a revenue point for the Nielsen Marketing Cloud, which comes down to personas. Who are we targeting? What is their title? What is the industry vertical that they are a part of? What is their function within their company? And which one of those attributes is most predictive of someone’s purchasing one of our products.
Number two is really WHAT. What kind of content are they engaging with from a B2B marketing perspective? We develop content that runs the gamut, from webinars, to owned events, to reports and white papers, to bylines and videos, to podcasts, and we’re tracking every touchpoint…
…across what you see as number three, WHERE these particular personas are interacting and engaging with our brand and with our product. And that’s really the channels of engagement. Media channels like pay-per-click advertising or display advertising; owned channels like nielsen.com product pages and solutions pages for our various products; social media, which could be earned content. And we’re tracking each one of these engagements through each one of these three criteria to understand who is most likely going to purchase a product from us and who is exhibiting purchase intent.
What Squark has really allowed us to do is to be more predictive of that flow from the top of the funnel, when someone downloads a white paper, or RSVPs to a webinar, or commits to an initial action, all the way through to the qualification phases which you see in the middle of the funnel, which are marketing qualified and sales qualified.
Marketing qualified we define as someone who fits two key metrics. One is an engagement metric, meaning how often have they engaged with us and where are they engaging with us? So, it’s really number two and number three – the what and the where. Whereas the fit metric, which is another way that we help predict purchase intent, is really number one – the who. Who is this person? What is their title? What is their function? And how does that help influence the purchase decision, and that path to purchase – all the way down to the bottom of the funnel?
At the end of the day, the whole purpose of this is to increase the size of the sales pipeline from a B2B marketing perspective, and it’s very reminiscent of what you see in B2C marketing as well. We look at every stage in that demand funnel. Our fundamental goal is really to build out the pipeline across all of those stages, because at the end of the day sales is a numbers game, and you want to ensure that you have enough people at the top of the funnel to feed those lower portions of the funnel, and to be able to better cater to those people’s needs as the interact with our brand. So, when they are coming to our product pages; when they are attending our events; when they are engaging with our content, we want to have a better understanding of where they are in the purchase cycle. Are they in the process of putting an RFP forth to other vendors? Can we get ahead of that curve, so that we are able to get in front of them before they release an RFP to the general public?
Squark is really what is allowing us to be more predictive – using a lot of data from a lot of disparate systems to enable us to understand who they are, what they like to see from a content perspective, and where they like to engage with us as a brand and as a product. And that helps us to optimize our media plan, optimize our media cross-channel strategy – whether it’s pay-per-click, or it’s owned email, or it’s what we are doing on nielsen.com. And optimize our audience segmentation so that we’re more effectively targeting the right people with the right content at the right time. And all of that, just like with B2C marketing, creates a better customer experience and enables us to more effectively and more inexpensively (most importantly) move people through that demand funnel, closer to that final, closed deal.
And AI is really what makes it faster; it makes it more accurate; and it helps us optimize our programs more effectively. Without that – and, believe me, I’ve worked in those circumstances without AI in the past – it’s a much more manual, much slower process, and your predictive capabilities are really diminished. Considering the amount of data; the amount of media inputs we see out there, it’s very difficult without putting your finger in the wind, to predict what’s going to be most effective in driving ROI for your marketing organization.
That brings me to this: We’ve implemented some of these predictive capabilities to get a baseline form which to develop our personas, our content, and our channel strategy. Now we really need to prove the impact of this – to measure the ROI and connect the dots between the media touch points – the engagement and conversion touchpoints that we are experiencing by distributing content across various channels – to our actual, marketing KPIs. As you saw in the earlier slide, those are connected to sales-accepted leads which, at the of the funnel, are closed deals and won revenue.
There is a wall between these two things. It’s very easy to track to the point of conversion – someone fills out a form; someone downloads something; someone attends an event – but to connect that to what’s going on in your CRM (and in our case we use Salesforce); what’s going on in your marketing automation platform (we use Pardot) is another challenge altogether. So you really need a system that’s able to integrate these various data sets and various platforms so that you can look at it holistically, and connect the dots between everything you are doing on the media and content side to how those things are impacting your most important KPIs that you present to the executive leadership.
So, this sort of summarizes it best. It’s how we attribute credit to the right channels and connect sales wins or conversions with tactics that is faster; that is more responsive; that is more intelligent than a small team would be able to do on its own. Even a large team with a large group of data scientists (and most marketing teams do not have the privilege of an army of data scientists) can’t really do things as fast as you need to be able to do them. To adjust creative. To adjust workflows in terms of they type of content we deliver and where we deliver it, based on consumer’s or customer’s past behavior or current behavior. All of those things need quick responsiveness, and AI is what helps us get to that point.
So, most importantly, Squark has really allowed us to quantify the marketing life-cycle. What I’ve depicted here, using the demand funnel I used in earlier slides, is some of the data inputs that are going into this decisioning engine, like marketing conversion rates, sales accepted rates, win rates, or average deal size. All of these things are helping this AI engine to be more predictive of sales and revenue and our core KPIs as a marketing organization.
The results have been phenomenal. And I attribute this to really the ability to automate a lot of the optimization that we need from both a personas perspective – who we’re targeting; a content perspective – what we’re targeting them with; and a channels perspective – what type of media, be it owned or earned, to engage with these consumers. By automating some of the decisioning around those three criteria, we’re better as a marketing organization. Better at predicting marketing qualified leads to sales qualified leads – the conversion point between those two. Which of our marketing leads are converting into the sales qualified category and why and how based on those three criteria? Which sales qualified leads are being accepted by the sales organization and followed up on for meetings? And finally, the ability to predict revenue, which serves obviously a few purposes – mainly our ability to forecast what we’re doing and the business results we’re, as a marketing organization, able to drive. This gives us buy-in with the sales organization. It gives us better integration with the sales organization for bringing these programs to life and closing the leads that we are generating. Without that, the system falls down from a B2B marketing perspective.
And finally, driving return on investment. So, we look at every dollar we spend across our paid media, across our content creation, etc. and we’ve seen, since implementing Squark, an 8X increase in our return on investment. So that ability to tell compelling and true stories about how your marketing investment and time is generating pipeline and revenue is essential for demand generation. That’s exactly what we are able to do more effectively with AI powering a lot of our decision making.”
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