Expert Interview: Patrick Edmonds, Proposify

Thinking about the changing role of the CMO or new challenges that come with modern attribution models? So is Patrick Edmonds, CMO of Proposify, one of today’s leading proposal software solutions.

In this instalment of our expert interview series, Patrick dives into how he applies his background in technology and data to performance marketing, and the bottom-up approach Proposify takes to growing their business.

OS: Patrick, can you tell us a bit about your journey into the world of B2B SaaS marketing?

Patrick: I think it’s fair to say that not many people say that they want to grow up to be a B2B SaaS marketer. I certainly didn’t plan this path! But, I’d always been good at math and tinkered with technology, and when I did minor in computer science with my Bachelor of Commerce, I saw the value in being a translator between those who focus on the technical side and those who focus on business questions. Being the bridge between them is a skill-set I cultivated, and it meant that the emerging digital marketing space was a natural fit for me. I was in the industry when Facebook advertising began and as Google Ads and Analytics became more established. I dug into things like SEO and content optimization from the technical side, and grew into my role as CMO as I solidified expertise in building marketing automation funnels and growth-oriented paid advertising activities.

OS: Given your background and experience, how do you see the role of CMO evolving in today’s landscape?

Patrick: I like to focus on marketing leadership, regardless of titles. Marketers are champions for the customer, we must understand them and the landscape no matter what. And though we have more data than ever, that fundamental part of marketing can’t be forgotten.  So while being customer-focused remains consistent, today I also see CMOs doing more and more—for instance, I have a running joke with my wife that I do more in terms of finance than she does, even though she’s an accountant. Now we’re seeing Chief Revenue Officers (CROs) emerge, reflecting a holistic view of marketing and taking responsibility for product, sales, customer success, business intelligence, and marketing. Anything that’s a revenue-generating activity within a company. I see SaaS companies moving towards this model because the flow of data and information impacts all these areas of business, and if the data sits with marketing, integrating this role can make sense.

OS: Speaking of data, Proposify’s marketing success has been based on a bottom-up approach. Why do you think this has worked particularly well, and what would you tell other marketers about this strategy?

Patrick: I think the real value of a bottom-up approach is the volume of leads, and therefore data, you get early on and what that allows you to do. Recently, we analyzed over 1.6 million proposals in 75 industries, from 35 countries and $2 billion of won deals from our system and learned so much about our customers. A bottom-up approach does the same thing—the volume that comes with a lower barrier to entry means a greater understanding market fit, features to explore, and customer needs which is critical to both product and marketing success. I could look at the complete funnel journey and touch points along the way and have confidence in the insights I was gathering. It wasn’t just about top-funnel metrics, but what the conversion rates were like along that path, who was converting where, and what’s retained them day-to-day. A top-down approach doesn’t necessarily give that same insight early on. Sure, you might have a higher initial contract value, but it’s harder to both analyze what’s working or what isn’t, and scale what’s creating those conversions you want. It doesn’t mean that you can’t initiate top-down marketing tactics later on. But as you add touch points to the customer journey it’s logistically harder and more complex to create a marketing attribution model using both approaches.

OS: On that note, what’s having the biggest impact on creating the foundation for marketing attribution at Proposify?

Patrick: Two things. First, creating a scalable tech stack. Like many others, our technology used to be very ad hoc and nothing was integrated, accurate or shared correctly. We’ve invested time and resources (both people and financial) in building a tech stack that can scale. We’re using everything from Salesforce and Marketo to Clearbit enrichment and Heap Analytics. Today we can be so much more confident in our data, and we have the people in place who can leverage it. Second, we recently updated our target personas and that’s been hugely useful. Because of our marketing approach, we can combine one-on-one qualitative data from our customer success team with data from customers in our marketing funnel to understand their pain points and behavior. That data can be leveraged across so many teams and functions—across sales and marketing, paid advertising and targeting, all the way to lead qualification criteria on the data side to ensure we’re handing off the right leads to the sales team. Those two things are what’s making more accurate marketing attribution possible.

OS: Finally Patrick, what do you think marketers in B2B SaaS should be paying attention to right now? Is there something you’re watching with particular interest?

Patrick: Of course everyone’s talking about machine learning and AI. It’s the hot button topic. While a lot of people might say it’s becoming over-hyped, I think we’re at a tipping point. Technology like AI has been around for a long time, but not in a usable way for companies. Data wasn’t being tracked properly, it wasn’t clean enough, it wasn’t being aggregated into a data warehouse where it could be manipulated and passed back into other systems to be useful. That’s changed. Strong data and analytics practices are becoming table stakes, especially in the B2B world. And alongside more internal sophistication, the AI and machine learning algorithms have evolved to a point where they can be more easily applied to many practical use cases. So we’re seeing this alignment happen between data quality and technology, and the algorithms themselves. So I think the promise of AI and machine learning will be realized by more companies than ever.


Note: This interview has been edited and condensed for clarity.  

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