Turning the Lights On Revenue-Generating Bottlenecks

A podcast discussion about obvious and not-so-obvious benefits of Dun & Bradstreet's Rev.Up ABX revtech platform

For workspace software provider Citrix, incorporating the revtech platform Rev.Up from Dun & Bradstreet in their marketing and sales processes was not so much about giving people data, but rather transforming how they worked, according to Barry Magee, director of business intelligence at Citrix.

In this sponsored podcast, Magee joins D&B Vice President of Product Marketing Deniz Olcay in a discussion about how the Rev.Up platform “turned on the lights” to reveal where the bottlenecks exist so they could be fixed.

Tim Hagen: Hi everybody. It’s Tim Hagan from Sales & Marketing Management, and today we have another special episode and another returning guest from Dun & Bradstreet. Today’s going to be a little bit different. We’re going to be talking about a case study, not just theory and concepts, but an actual case study. So I want to invite our special guests back, Deniz from Dun & Bradstreet. Dennis, how are you?

Deniz Olcay: Good Tim, good to see you again. Thanks for having me on your show again.

Hagen: Why don’t you introduce our guest and your customer?

Olcay: Sure, absolutely. My name is Deniz Olcay, I lead product marketing at Dun & Bradstreet. We empower sales and marketing leaders with data insights and automation they need for growth. I’m really excited to have Barry Magee from Citrix on the line with us. He is the director of business intelligence at Citrix. Citrix is a globally recognized brand, but for those of you that don’t know, they create technology that creates simple, secure and better ways to work from anywhere on any device. They have over 9,000 employees worldwide across 40 countries. I’m really excited to have Barry share his story today. Barry, how are you doing?

Barry Magee: Good. Hi guys. Welcome. Good morning, good afternoon from Dublin.

Hagen: So guys, tell us about the project. I’ll start with you, Deniz. How did you start engaging with Barry and his team in Citrix? What were some of the challenges? What was the objective of the project? And then we’ll turn it over to Gary and have him share, from his customer perspective, some of the things that occurred.

Olcay: Yeah, absolutely. Citrix and D&B have a longstanding relationship, and Barry, who leads the practice in the EMEA market in Europe, really got to talking to us about some of the historical conversion rates that they had been seeing with their sales and marketing team. As we look at business-to-business or B2B conversion rates, they are pretty low, typically speaking. For every 100 customers, you can average responses typically around 4%. And the marketing baselines [are] typically even lower – probably less than half of 1%.

Barry was really focused on fixing this inefficiency powered by good data. Their typical sales team was buried with dozens of requests coming from different parts of the business. Your sales leader tells you to focus on X; The marketing leader tells you, “Hey, there’s this great campaign or program we’re running. Focused on that.” Meanwhile, sales teams are really hustling to hit their quota and their goals, right? Sometimes they can get inundated with asks from across the business. That’s the inefficiency that Barry was looking to solve. Where can we create a simple, compelling, clear dashboard that revealed the best targets to go after informed by good data collected within the business, but also supplemented from outside of the business? That’s really how Barry and our team got to talking. And I’ll let Barry talk a little bit more about his perspective.

Magee: I agree with everything that Deniz said except one thing, which is in a constructive way, it wasn’t really about creating dashboards. Because as Deniz said, we were faced with how do we basically improve productivity in sales, marketing and even customer success? What we didn’t want to do was just create, “Here’s another super spreadsheet.” Whether it’s on the cloud or on a laptop, that’s what everybody tends to do, and I’ve had experience with this type of thing in the past. What we wanted to do was really about changing processes and workflow, and not, “Oh, here’s a nice, shiny spreadsheet with cool data,” and it sits in the corner of the business. It had to be front and center with what you did when you came to work on a Monday or your go-to-market planning, you know, whatever time of the year that’s at.

Part of the joke with my boss has been is we don’t build dashboards; we build business processes to make people more data-driven. It’s not about giving people data. It’s about transforming how they work. So where D&B came in is they’ve got a component within their product stack which allows us create workflow, which allowed us to move beyond from just, “Let’s create all these insights” – that’s all the data and analytics – and get away from just parking it on a spreadsheet if they use it. [We wanted to] integrate into their workflow. We use Salesforce, so I can say, “Deniz, here’s a prospects [list], go talk to them and let’s learn from those interactions.” That workflow meant that it moved from just, “Here’s a piece of information about a customer,” to “Deniz, did you interact with them? How did that go?” and start creating a feedback loop?

It’s sort of machine learning for organizational transformation in that you certainly start to see, “Well we think the customers we should talk to are over here, were we right or not? Was the spreadsheet a good spreadsheet?” to use the example. You create a feedback loop for your analytics. That’s what Rev.Up did in so far as it gave us a feedback loop that otherwise, no matter how good our analytics were, they were just going to be a spreadsheet sitting somewhere that we could never test how it worked in real life.

Olcay: I think you said it best when you said when data sets on a spreadsheet somewhere, it’s not really valuable, right? It needs to be integrated into the workflow. The days of just relying on data for a competitive advantage are sort of limited because people are inundated with data. Everyone has access to it. It’s about operationalizing that data and integrating it into a workflow for efficiency.

Magee: Even that point, guys, there’s a whole behavioral side to it, which we won’t go into too much, but there’s a tendency to assume that if this data set is better than that data set, that people will act rationally. “There’s a good data science model from the PhD data science people. I’ll go use it.” No, they don’t. People behave emotionally and they’ll work with the stuff they’re used to. They’ll work with the data sets they’re used to. So even if we had a good dataset, they still wouldn’t use it because they’re used to using the spreadsheet they have with inventory or last year’s sales that’s been sitting on their hard drive for the last three years and they update it all the time. So you need something to challenge that paradigm. You have to change their workflow. You have to stop people from going back to doing it the way they used to. It’s not about the data. It’s about how you challenge people’s biases and how you get them to think differently – think more expansively about what they think they know about their client and what a more complete picture might be of that engagement.

Hagen: Taking what Barry just shared, Deniz, can you share a little bit about Rev.Up and how you went about helping Barry and Citrix start to change some of those paradigm shifts?

Olcay: Absolutely, Tim. And again, Dun & Bradstreet comes from a long history of having data as a core competency. But as we’ve evolved in how we execute sales and marketing, and as we’ve become increasingly reliant on technology to do so, a lot of organizations have built out what we call their revtech stack or their revenue technology stack. So you’ve got your CRM, you’ve got your marketing automation, you’ve got your ad platform. And we’ve become increasingly reliant on executing programs and campaigns with those technologies.

So it’s not enough to just have the data and the insights. It’s really important for that data, insights, the prioritization to be embedded in those workflows and those systems, and really unify the data that is translated among those different technologies and make it consistent. That’s why the D&B Rev.Up ABX solution is grounded in an open CDP – a customer data platform that unifies the identity across all of your different technologies and augments it with some of the intelligence that Dun & Bradstreet brings to the table, whether that’s from a graphic data, whether it’s intent or fit data, its propensity models… Being able to merge the customer’s first-party data, in this case, Barry’s first-party data, with the intelligence from Dun & Bradstreet, and then push those insights embedded in a workflow, as Barry has been mentioning. Being able to deliver those insights for sellers to action on in the system that they’re living and breathing in – they’re in Salesforce, for instance.

Magee: And in fairness, that was one of the things about the solution that if we didn’t have our own internal capabilities, we could have used the D&B out-of-the-box analytics. But because we felt that we knew our data better, and we had more sophistication – maybe in-house we’ve got data sets that D&B didn’t have – we said, “We don’t want to use that function. Can we use our own systems instead?” For a lot of people, you’ve got to crawl, walk, run, race, as they say, and you need to build up that trust first before you get into the very, very high, sophisticated level of analytics deliverables. The ability to use the workflow component, but with our analytics helped us bridge that gap. Because if, as a business, I say, “Let’s trust D&B. We’ll use their workflow and their analytics,” the resistance would have been probably a little bit too strong. This was a really useful way to even get the adoption.

We’re actually in the middle of deploying this globally. There will be thousands of [Citrix workers] using this around the world, and the critical piece is we treat our data and analytics artifacts like product. We take adoption, we look at monthly usage, monthly active usage…Who’s using our product? How are they using it? That ability to play around with how we use the product with Deniz and their team was really critical to drive that adoption in a soft way.

Olcay: And again, this is the concept of openness, right? Our open platform really allows for that crawl, walk, run evolution. As Barry mentioned, a lot of companies are at different stages in their journey. Whether they’re starting new, they can use some of the out-of-the-box capabilities, or, as they evolve, if the customer’s already got their master, we don’t want them to replicate that somewhere else and introduce that complexity. If they just want to use the workflow component, as in case of Barry, that’s how we deploy it. That open framework really allows us to do that.

Hagen: I think both of you have touched on this. There’s certainly some challenges, and you’ve talked a lot, Barry, about adoption. I’m going to come back to you, Deniz. What were some of the early challenges and what were some of the outcomes that you experienced not from Citrix’s perspective, but maybe just D&B’s perspective, and then we’ll turn it over Barry.

Olcay: I think it goes back to the concept of prioritization and being able to help sales teams focus on their best opportunities, given that they’ve gotten into a rhythm and habits – their day-to-day workflows – in some other manner. Barry mentioned people are used to their spreadsheet, or people are used to building their list somewhere else. It’s really changing that paradigm and that mindset informed by good data is something that we continually see. Because it’s not just the data challenge. It’s an organizational culture and focus challenge as well – being able to rely on a new data set that you haven’t relied on before. Because ultimately, when you’re talking about sales, you’ve got a really high-pressure job of being able to deliver a quota, right? You’ve got to put food on the table. It’s really important to trust the data that you have. That’s really something that we see as a universal challenge across all of the customers we work with. That’s why we typically work on a pilot approach. It’s starting with a small segment, proving out the results, and then broadening from there. I think that’s how we started with Barry and his team as well. Barry can comment on that.

Magee: Without getting into specific challenges, in the broad, what you’re really dealing with in organizations like us is that you’ve got multiple instances of siloed data. You’ve got marketing data here, sales data there, technology, budget data, and then you’ve got even different variations of the same data. The value proposition of a CDP or even of an analytics capability like we built is to help people make better decisions. But inherent in that is people tend to think, “Oh, let’s get 20 PhD data scientists on it and it wll be a super spreadsheet.” But it’s not that simple, because when you build a DNA capability or a workflow or a CDP capability, the first thing you actually do is unearth all the inefficiencies that are already in the system.

Everybody knows the data is substandard. It’s the elephant in the room. The first thing that happens when you do these transformations is you actually look in the mirror and you see for the first time in quantifiable ways exactly where those bottlenecks and problems were. “Oh my God, we’ve been investing over here in these customers when in actual fact the market is over there. Too much of our marketing dollar is here, too much of our sales sellers here. All the sales territories are disconnected.” So what actually happened when we started was we turned on the lights. The first thing we saw was, “Oh, those accounts aren’t mine.” Well guess what? They were always there in the wrong place. But when you turned on the CDP component, guess what, you were able to see they were in the wrong place. So it’s this thing of transparency and turning on the lights that is a big factor. And that happened everywhere, from marketing leads to every aspect of the business – this aligned or poorly distributed sales territories and accounts, sales resource in the wrong place. You’ve got to anticipate when you do this, you’re going to turn on the lights first. It’s a transformation project first, and a data project second. Data is just the cathartic element that gets it off the ground, really.

Hagen: Last question, and it’s kind of a loaded question. What were your main targeted objectives and what were the desired results of what you can share that you actually accomplished? Deniz?

Olcay: I’m going to let Barry take the lead on this one, but I’ll just tee it up for him. I think when you look at how Barry’s team was executing before versus after, he’ll talk a little bit about ad hoc and processes that are not grounded in that system of truth – in that data layer – and with the workflow efficiencies. As soon as you turn the lights on and reveal the buyer journey and what’s happening across those systems, and really shift to an integrated process… Barry’s team has seen some incredible improvements in conversion rates, in the time to actually generate pipe, the amount of pipe that has been generated, and also the pipeline velocity – how quickly that pipeline closes. I’ll let Barry talk a little bit on the specifics, but it’s been quite eye-opening and a fantastic story.

Magee: The tactical one that everyone jumps on is the above-the-line one. As Deniz said, in the industry, between sales and marketing, for every 100 clients you engage, whether that’s by marketing or sales or cold call or go physically visit, you’ll be lucky if two or three of those turned into a band-qualified opportunity. We are now averaging at 20%, and that’s in between peaks and troughs. But when we double down – we do like a blitz period for, let’s say, a couple weeks or a big push on a certain thing – we’re actually hitting 30%. So we’ve had a 10 to 15X improvement on conversion rates. The bigger impact, though, is how it affects productivity across the board. [Garbled recording]

The below-the-line impact is now, instead of the “busy being busy” paradigm, now,  everyone is much more effective with what they do. You have a single organism that everybody works against instead of people with hundreds of spreadsheets everywhere. That has created a lot more strategic coherence. Everyone is rowing in the same direction. We now plan our go-to-market movements on it. We can see where is the market, how many resources we should have? And that’s the bigger picture thing. We have a single way of interpreting the commercial opportunity ahead of us and how to go and attack that. That’s sort of less obvious. Everyone always wants to know, “What’s the conversion rate?” But the bigger-picture stuff is we’ve injected this mechanism into the DNA of the company, and then culturally, we’re starting to behave and act in a more data-driven way as opposed to just these sugar hits of a quick list for a call now.

Hagen: Yeah, think about the stress reduction in everything that you just said. I was looking for the term of the cultural transformation. So Deniz, in summary, last thing you’d like to share about Dun & Bradstreet and your work with Barry.

Olcay: We’ve had a fantastic conversation here in terms of Dun & Bradstreet’s role in this engagement. Obviously, we’ve always been grounded in empowering sales, marketing and operational leaders with better data. But it’s growing importance in today’s day and age is to be able to actually integrate that data in your revenue technology stack and in the workflows you have. So Dun & Bradstreet has invested an incredible amount of resources to build the software capabilities on top of our data and integrate it into some of those best-of-breed tools. Whether you have Eloqua, Marketo or Salesforce or HubSpot, whatever you might have in your stack, the data is delivered consistently across those. The results are not just conversion rates, like Barry mentioned, but it’s actually the speed and the efficiency in how you can go to market as well. Right. What we saw with Citrix and with Barry is being able to deliver pipeline in weeks versus months. When you’re a seller trying to hit your quota, that speed to market and getting ahead of your competitors is incredibly important. I really thank you a lot, Barry, for sharing your story.

Hagen: Barry, anything you’d like to add?

Magee: A tip to anyone who’s working in this data and analytics and BI spaces: Don’t, over-rotate on the data itself. The workflow is a component. You can have the best model in the world, but it’s just another spreadsheet sitting on a desk until you make it actionable. That’s how we used the Rev.Up capabilities – to create a workflow to be able to hold ourselves accountable to see, “Our we executing against this?” If so, what’s working? What’s not? If it’s not working, learn from it and create that feedback cycle. That is the critical piece – you have to have a feedback loop for any data and analytics capability.

Hagen: I hope our audience really learned something. A lot of the notes that I took during this podcast is about processes and workflows. I love what Barry shared about the cultural impact, too – the time savings, the stress savings and consolidating workflows – and not only having the return, but not getting caught up in the data. I love that you shared that Barry. I would encourage our audience to reach out to Dun & Bradstreet. They’re doing some amazing work in the industry. Thanks guys. Really appreciate it. A great discussion.

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