The future of B2B measurement

In this episode, Kate Gleeson, Global Brand Director sits down with Emily Gustin, Senior Associate of Business Development at LinkedIn, and Supermetrics’ Director of Product Marketing, Aleksander Cardwell. They discuss the current landscape of B2B measurement, data privacy challenges, the top KPIs to watch, and managing data effectively.

You'll learn

  • Why there’s a disconnect between measurement tools and B2B
  • Which KPIs are most important today
  • The challenges with third-party data
  • Business advice for the future

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Kate Gleeson:
Hello, and welcome to our session on the future of B2B measurement. I am in the Supermetrics office in Helsinki. And I'm joined by the incredible Emily Gustin from LinkedIn. Emily leads up the measurement and attribution partnerships for LinkedIn. And I'm also joined by our very own Aleks, who is head of product marketing at Supermetrics. Emily, Alex, how are you doing?

Emily Gustin:
Doing really well. I'm so thrilled to be in Helsinki. Thanks for having me.

Aleksander Cardwell:
Yeah, going great. It's always nice to have a partner like LinkedIn join us here in the Helsinki office. So looking forward to talking about B2B measurement today.

Kate Gleeson:
Absolutely. Well, let's get stuck into it. So, we know there has been some pretty seismic shifts in the world of measurement. What do you see as significant changes in terms of B2B measurement?

Emily Gustin:
So first I want to start with what's stayed the same in the world of B2B marketing and measurement. What stayed the same is that it's really complicated and without taking off the laundry list of reasons why, the buying group in B2B tends to be more than one person, which can make it hard to track really an outcome to its completion.

You're not just looking at one path. There's lots of folks involved in that buying decision. The sales cycles in B2B are much longer than they are in B2C. So we find that on average, the B2B sales cycle is about 16 months long, but marketers are actually measuring really not much farther than six months out on average.

So, it means that there's a big disconnect between the outcomes that we're looking to drive and what right now is being measured. So that's always been the case, right? But in terms of what's changed, I think that's twofold. Primarily marketers and B2B marketers are feeling more pressure to drive economic impact, so revenue. LinkedIn has a thinktank called the B2B Institute, and we surveyed global marketing leaders this past year and over half of them reported that the need to drive revenue has intensified over the past few years. So that's one thing. There's more pressure. And the second is, I would be remiss not to mention that the world of data privacy has changed. That's been going on for many years, but it's going to really come to a head in 2024 with the signal loss that we'll be experiencing due to third-party cookie deprecation.

So in summary, B2B measurement is still really hard. What's changed is that marketing leaders are under more pressure and they have less signal to work off of to prove results.

Kate Gleeson:
Absolutely. So how would you describe the current landscape in terms of B2B measurement and tools?

Emily Gustin:
I'd say right now there's not a lot that's really purpose built for B2B. Most of what exists in the ecosystem are tools that first started out as a center on B2C outcomes, right?

And so what that means is they're usually using shorter look-back windows. So definitely nothing close to that 16 months that I referred to, oftentimes anchoring around metrics that we would say are more of the frontline, are my campaigns performing immediately metrics like CPC, CTR and less on those true business outcome metrics like revenue generated or pipeline generated.

So I'd say like primarily, the ecosystem is built for B2C. It's also pretty fragmented because we find that while marketing is really responsible for driving sales outcomes, there tends to be a disconnect between marketing and sales, particularly at large organizations in terms of what KPIs they are anchoring around and how they track those.

So marketers tend to focus on the MQL, the marketing qualified lead. The majority of marketers use this as like their source of truth. But we find that the majority of those MQLs don't actually end up converting to the sales qualified lead. Like what sales blesses as being like, this is someone that we want to outreach to.

So that means that there's both a disconnect in the actual metric there and in how it's tracked. And I'll just close by saying that LinkedIn is really focused on attacking this problem, and we're releasing a suite of tools that are purpose built for B2B measurement. And to give an example of one, our revenue attribution report is a recent offering that enables marketers to really understand the impact of their investment on those B2B outcomes like pipeline generated, like revenue generated by connecting your CRM data in a secure manner to LinkedIn.

LinkedIn's able to compare engagements on your ad to those outcomes, like let's say closed deals in your CRM. So, this is something that we are very focused on addressing, but right now the ecosystem does tend to be a little bit more focused on B2C.

Kate Gleeson:
So you talked a little bit there around KPIs and business outcomes, but in terms of B2B measurement, what do you see as the significant KPIs in the coming years?

Emily Gustin:
So I think the True North is going to move toward a focus on business outcomes rather than some of those more frontline metrics. And what I mean by business outcomes is something that actually moves your business forward. It makes a difference in your business's future. So that could be something like revenue or pipeline generated. It could also be engagement from accounts that are really a top priority to your sales team. And in the world of marketing is focused on sales outcomes and we know that everything is not about immediate demand generation.

So there's definitely a role that “brand” needs to play in B2B marketing. And I would predict that that goes similarly, we're focusing less on those frontline metrics like reach and frequency and more on stuff like mental availability. And what I mean by mental availability is does someone think of your brand when they're in a potential buying situation?

So, if you're going to buy toothpaste, you immediately might think of Crest, right? That is a very hard thing for a B2B brand to accomplish. And so, I think that's more of the outcome that you'd be looking for higher up the funnel. I do want to say though, that like there is a role that these leading metrics, these frontline metrics play. You're not always going to be able to look at close one deal on the CRM immediately.

So it's important to know for your business, what does the long and short look like? It might start with, we've gotten some good engagement on our ads and that's where CTR is more important than looking at engagement from accounts that are very relevant to your business, and ultimately tracking toward pipe-end and revenue generated. There’s a role for those front-end metrics but I don’t see them as being the source of truth for B2B measurement.

Aleksander Cardwell:
So yeah, if I can build on that. Something we see here at Supermetrics is that no two businesses are alike. Most businesses have pretty unique goals that they're aiming for, which means that the KPIs and the measurements that they're looking at need to be slightly different.

I think that the easiest example of this would be you can have a B2B business that's highly PLG, so product led growth strategy, where volumes are a little higher, and then you can have an enterprise sales-led business where volumes are lower, but average contract value is higher. And in these businesses, the KPIs are very different. For the PLG business, those front-line metrics that you were talking about are a little more important when you're looking at high-volume games.

Now obviously, the same thing is happening there where they're also moving more towards revenue generated, but in a PLG business, they tend to lean a little heavier on those early indicators, whereas on the enterprise sales side, obviously revenue is big. Pipeline generated is big, pipeline influenced is big. So that's kind of one way to think about it as well, is that as you're looking to evolve your KPIs and what you're measuring, really think about your organization. What works for your organization? What are your goals? What is your customer like? And, kind of go from there.

Kate Gleeson:
So we're not going to have a chat on B2B measurement without bringing up the world of privacy and security. How should marketers proceed in a privacy conscious way?

Aleksander Cardwell:
Well Emily was alluding to this in the beginning as well, that there are some pretty significant privacy and tracking changes on the horizon that are going to affect the way we do marketing measurement as a whole, and that's definitely impacting B2B measurement as well as general marketing measurement.

Now, the most obvious one here is that in the future, you're not really going to be able to benefit from that third-party data. Due to stuff like cookie deprecations, etc., you're not going to be able to amass and optimize your marketing activities based off of data that's not yours. What that means is that marketers should really be proceeding and thinking about this in a way that they need to build out their own first-party data strategy.

What that starts with is looking at all the touch points you have, all the data that you have in various locations and starting to streamline and collect that in some kind of centralized location. Once you have your first-party data in one location that you own, that unlocks all sorts of cool measurement applications that don't require that privacy-restricted data that we're going to be losing access to.

Another thing when it comes to data strategy in general is access control. So who has access to your data and the applications that are generating that data? Something that we do at Supermetrics, for example, is delegated authentication, which means that you can invite users to grant Supermetrics access to access data, but you don't have to actually add new users to these platforms, which could be a privacy concern going forward.

Stuff like monitoring your data security, your data pipelines, all of that is going to be super crucial in the future, especially as these privacy legislations take effect. And with those, you really want to either be monitoring them closely yourself or be working with partners that will do that for you. It might be really hard to monitor all these privacy changes yourself. So, work with people who can do that for you and take that load off you.

Kate Gleeson:
So based on that, do you think that, in terms of B2B measurement, a data pipeline gives a business a competitive advantage?

Aleksander Cardwell:
Oh yeah, definitely. And here at Supermetrics, obviously we're very passionate about data pipelining and data work in general. And the way we typically see our clients using data pipelines as a competitive advantage is that they employ it in two different models. We sometimes call those our centralized data access model versus our decentralized data access model for your data pipeline.

But what that really means in a more simplified way is that on the centralized side, you want to take all of that first-party data that you have and centralize that in one kind of larger scale storage solution, like a data warehouse or something like that. Essentially one storage solution that you own because once you're owning all that first-party data in one location, it enables those future-looking measurement applications like marketing mix modeling, like any kind of predictive algorithms, etc. And that's really not possible, like I mentioned earlier, unless you centralize all that data in one place.

Then on the decentralized side, what we talk about is having a data pipeline that's also flexible enough for allowing your frontline marketer to quickly grab any data that they need in any reporting tool that they're using, whether that's a spreadsheet, BI tool, something like that to make those kind of quick, like Dynatrace was talking about this kind of quick, real time optimizations on the fly.

So, in those situations when you don't have the time to look forward and do long scale predictive modeling, you still want something flexible enough that will allow you to measure those slightly more front-line metrics on a more ad hoc basis, basically.

Kate Gleeson:
Okay, so it's going to be a difficult question to narrow down, but if you could give a business advice in terms of B2B measurement, what advice would you give them in preparing for the future?

Emily Gustin:
I think it first comes down to having alignment on your goals and priorities across an organization. We've talked about how B2B measurement is a long game, but you need to have the right like long, short strategy. So what are the outcomes that you're ultimately looking to drive in the end? What are the things that lead up to those outcomes, those conversion actions that come before.

And then one of the more frontline indicators that you should be looking at immediately to evaluate performance and getting everyone aligned on those goals and then the metrics that matter underneath them is the starting point to do B2B measurement well.

I think in the world of privacy and how that's evolving, it's going to be really important to invest in building up your own first-party data. So where there is a true value exchange with customers or potential customers, really aiming to collect first-party data in a secure way so that you can use that data to inform smarter models.

And where we need to do predictive or aggregate modeling that you have really smart inputs that have gone into form that model and work with vendors, that can help to connect that first-party data using privacy enhanced technology, stuff like clean rooms, for example.

And then finally, I would definitely have to bring in a corny metaphor here. So I'll use the Swiss cheese metaphor. I'd say it's important to layer multiple different types of measurement on top of each other. No one solution is going to be perfect. Everything's going to have holes. But when you layer on top of each other, that's when you have the soundest view of performance.

And some of the types of measurement that you should be looking to layer are those that do not actually rely on unique user level data to inform the measurement. An example there would be like incrementality, right? So incrementality looks at the lift in an outcome based on a specific marketing activity, but it doesn't actually need user level data inputs in order to correlate the activity with the lift. So this enables marketers to make data-driven decisions, but in a way that's done in aggregate.

So if I could hammer those three, it's educate and align with internal teams, build up your access to first-party data and share it in a secure way where it's needed to inform modeling. And then third, leverage multiple types of measurement, particularly measurement that does not rely on those user level data inputs.

Aleksander Cardwell:
Yeah, 100% agree with the sentiment around leveraging multiple types of measurements. You used your Swiss cheese analogy, I'll use a different one, which is flying a plane. I heard this from somewhere, I can't remember where, but marketing measurement these days is a lot like flying a plane. If you're flying a plane, you have multiple instruments you're looking at. You're looking at your altitude, your speed, your wind conditions, stuff like that. And you wouldn't want to fly a plane just with one unit of measurement. And it really is the same with marketing. You wouldn't want to measure your marketing based off of just one single set of KPIs or in one specific fashion. So we are moving into a world where there needs to be a joint approach to how you do measurement.

There will still be a place for your click attributions, your first touch, last touch, whatever type of touch attribution model you want to use. But we're also entering this world of marketing mix modeling, predictive analytics, all sorts of things. So really a joint combination of these is going to be important.

Another piece of advice I'd give is we've mentioned first-party data a few times here. I think now is the time to start building that historical catalog of your first-party data of your marketing performance right now. We, to a large extent, can still get by doing what we're used to doing because a lot of those optimization techniques and measurement technologies are still in play, but they are being phased out.

And once they are phased out, if you already have a historical catalog of that data, of those first-party actions, you're going to be in a really good place to start building more advanced measurement techniques, again, that don't require privacy-restricted data.

And lastly, I'd really like to leave on a thought that's a little more outside the box, but I'd encourage anybody working with marketing measurement to consider. As we enter this age of new marketing measurement where the ability to track, the ability to measure across third-party sources is going to be diminished, and this way we measure and the signals we get are going to be a little less accurate, because of that, your ROI goals might change, and the way you evaluate your ROI goals might change. So I'd encourage everybody to reevaluate those goals in a sense.

And for example, one clear thing could be that, because the signals are less accurate that we're getting, it just means that naturally, our cost per acquisition might start going up. That might not be due to us fundamentally doing something different where the CPA actually increased, but just due to the way that we're measuring, due to the new privacy legislation, due to the kind of user level tracking that we're going to be having. Those goals might shift. So, I encourage everybody to keep that in mind as we go forward, that those goals just might need a reevaluating at some point.

Kate Gleeson:
Absolutely. So I have loved speaking to you both today, Alex and Emily. So thank you so much for your time and thank you so much for watching.

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