Nick Turner, CEO of Dreamdata: “Resist the temptation to talk in probabilities”

Nick Turner has spent more than two decades helping SaaS companies navigate periods of rapid growth, building go-to-market strategies and scaling revenue at businesses ranging from early-stage startups to established software vendors. Today, as CEO of Dreamdata, he is focused on one of the most persistent challenges in B2B marketing: understanding how marketing activities influence revenue across increasingly complex buyer journeys.

That challenge is becoming more pressing as the modern B2B purchase process grows longer and involves more stakeholders, channels and touchpoints than ever before. Against this backdrop, Dreamdata has emerged as a fast-growing player in the attribution and revenue analytics space, reporting more than 400% revenue growth over the past two years and recently welcoming renowned SaaS executive and marketing thought leader Dave Kellogg to its board.

In this interview, Turner discusses why traditional attribution models are struggling to keep pace with today’s buying journeys, the common mistakes organisations make when measuring marketing effectiveness, how AI is changing the role of marketers, and why credibility and consistency matter more than ever when connecting marketing activity to business outcomes. He also shares his perspective on the balance between short-term performance and long-term brand building, as well as what the future holds for attribution in an increasingly data-driven world.

Dave Kellogg describes the “original sin” of B2B attribution as trying to solve a marketing problem inside a sales system. What does an attribution model need to look like today? 

Dave is right. We often say something similar to our customers that come to us working out of a CRM who are fed up. In B2B, all the revenue lives in the CRM in the form of a closed won deal or opportunity. The CRM is 99% a sales platform. The only part that marketers get even a little control over is one or two fields usually named ‘lead source’ or ‘marketing source’ and can contain maybe 28 characters. 

What that usually leads to is some sort of single touch attribution (“found you on google” or “heard about you from a friend”) that doesn’t tell you what actually happened and how that buyer ended up at your door, and it certainly doesn’t do a lot to help you make informed decisions around where your marketing budget should be utilised. 

B2B marketers need the ability to track a long and complex buying journey that now spans 272 days, 88 touchpoints and 10 stakeholders, according to our LinkedIn Ads B2B Benchmarks Report. 

It involves a significant amount of data from usually a dozen or so sources, complex tracking, and the ability to stitch data together that was never really designed to be stitched together. You then need the ability to aggregate those hundreds of thousands, sometimes millions, of journeys to understand what are parts of marketing, and what are not, as well as easily explain it to your CFO, CRO and CEO.

It’s much more involved than a single field in the CRM.

Dreamdata’s Benchmarks report says the average B2B buyer journey now spans 272 days, 88 touchpoints and 10 stakeholders. What does that complexity mean for how companies should measure marketing effectiveness today?

There is an argument that it has always been that complex. We’re just now getting the data to find this out, and every day we are seeing more and more of it. What I would say is that you now have the capability to know much more than Mr. Wanamaker did in his famous quote, “half the money I spend on advertising is wasted; the trouble is I don’t know which half”. 

You need to study carefully what data you do have. You have much more, but it would be misleading to say that you have everything and it’s perfect. It is incredibly useful and can help you to know very specifically what happened in certain channels and buying phases (digital spend, events, etc.) as well as make very smart inferences as to what happened in areas where you have less data (billboards, emotional impact, etc.).  

Our view at Dreamdata is that’s the best way to treat it, and this is true for any discipline at any company, whether it’s marketing, sales or product engineering. You work with the data you know is true, then you draw inferences from there.

Dreamdata has reported more than 400% revenue growth in the past two years. What have been the key drivers behind that momentum? 

What it shows is that we’re solving a problem that a lot of B2B marketers are experiencing – and it’s not a problem that’s going away. We aren’t the first attribution or marketing analytics platform out there, but we are benefiting from the changes in technology and best practice over the last 10 years, which has us well-positioned to solve the problem better than those before us.

I would also say that we’re dedicated to the problem and our core customers. We have, and always will, serve B2B marketers. It means our knowledge of their day-to-day problems runs very deep, and our solution is crafted to their needs. 

What are the biggest mistakes B2B companies still make when trying to connect marketing activity to revenue outcomes?

I think it’s a simple one that has nothing to do with technology. I believe it has to do with consistency and acknowledgement of gaps. Consistency is better than better. In other words, you cannot constantly chase a new metric to track. That leads to lack of historical data from which to make informed decisions, as well as lack of credibility (“why did Nick suddenly switch metrics in this quarter’s board meeting? Did the other one start looking bad?”)

While technology can help greatly there, the fundamentals have to come from internal business decisions. “This is the metric we care about. This is how we define it. We will not be changing it”. You also need alignment on that, even if you find a metric that you might like 5% better or feel captures what you want to represent. It’s still a mistake to change it. 

Dreamdata is investing heavily in AI-powered capabilities. Where do you see AI having the biggest impact on attribution and go-to-market strategy over the next few years?

I’m not a great prognosticator and I think anything said today is a guess. Two years ago, everyone was convinced AI is going to have the biggest impact on creative jobs. Now our engineers don’t write code anymore. 

What B2B marketers can hang on to is that AI will never replicate taste. All of the optimisation, the legwork, etc., work hard to put that in AI’s hands, because that’s where it’s going. Focus on the fun stuff like creativity, branding, messaging, because we can now iterate incredibly quickly. 

Dreamdata’s investments in AI are focusing on the inverse because that is where we feel we can be most useful to marketers. Put another way, we want to do the boring stuff so B2B marketers can work on the exciting topics mentioned above. Generating reports, analysing the data and providing insights and recommendations on how to optimise spend across all channels is our near-term focus. In the long term, we see a lot of opportunity in autonomous agents managing marketing spend. In general, the limitations here are no longer technological, but rather the comfort level of how much responsibility marketers are willing to give to agents. 

The world is moving fast right now, and just about any roadmap is always subject to change, especially anything that goes beyond the next three months. But it’s an incredible time to be in the software space and we are very excited to be developing the next generation of B2B marketing platforms for our customers!

What convinced you that Dreamdata was at the right stage and had the right vision for Dave Kellogg to join the board? 

Like many in the B2B software space, I’ve followed Dave for years. He probably doesn’t remember it, but six years ago I emailed him a question, and he responded within 24 hours with a great answer. Highlight of my year. 

When we had an open board seat and the opportunity to fill it, I knew immediately I wanted to pursue Dave. We care deeply about marketers. He’s probably the most well-known CMO to make the leap to the CEO seat in enterprise software. He focused on companies similar in our size and helped to scale them effectively. We’re originally a Danish company before we went global and Dave has worked with European companies previously. 

For me personally, he’s an incredible coach. He knows how to communicate very effectively, provides great feedback and has seen this movie many times. We’re very lucky to have him. 

Has B2B marketing become too focused on short-term performance metrics at the expense of long-term brand building? 

B2B marketing has not become too focused on that, but structural changes within a company have brought them to this point. Short-term focus on company metrics is driven by quarterly reporting done in public markets, and private market companies who are auditioning to become public companies do the same. 

Marketers are often lumped in with sales, a huge mistake. I was previously a CRO and had a marketing team report to me as a former sales leader. I screwed it up and many sales leaders will because they have quarterly numbers they need to hit so they want their leads now. 

As mentioned earlier, the buying journey is much much longer than sales realise, so you can’t ask a marketer for leads today. But they often do, and that means marketers are forced to focus on short-term performance. A marketing team is much more aligned with a product team. If you want to change things this quarter, change something on your sales team. If you want to change things 6+ months from now, change something in your marketing or your product.

Looking ahead, how do you see attribution platforms evolving as buyer journeys become even more fragmented and data-heavy?

Attribution and any form of marketing analytics need to stay focused on providing credibility. Resist the temptation to talk in probabilities. Many martech vendors have gone down this road and cost themselves their own – and their customers’ – reputations. 

Make sure your analytics are rooted in being deterministic. You can collect a lot of data – and it’s growing every day. It’s also important to surface those data sources. But acknowledge that it’s not everything, and no model is going to be perfect.