New data: Testing the B2B Marketing time lag
Some home truths need constant repeating, and this is one of them: the marketing strategy you’re executing in Q1 will hardly influence revenue in Q1. Most of it won’t even influence revenue in Q2. Some of it won’t even influence revenue this year!
That’s because B2B marketing operates within a long drawn-out customer journey, and ignoring this reality comes at your own peril. So if you’re making Q1 planning assumptions based on revenue targets, you’re late, very late.
Dale Harrison recently labelled this latency between marketing action and revenue recognition, the ‘marketing time lag’, applying a simplified ‘toy model’ to make the case.
We’ve put some real-life B2B marketing data through the time lag model which I’m sharing in this post with the hope of getting a more accurate picture and in turn, hopefully help you better benchmark and forecast your marketing impact.
Contents
B2B Marketing Time Lag: a definition
The consequences
Time lag benchmark
How to get your marketing time lag data?
TL;DR
From our data on marketing impact on revenue over 24 months:
Only 37% of revenue is impacted within a quarter.
It takes 6 months for 50% of revenue to be impacted.
25% of the revenue impacted by marketing touches will not be generated before the year is done.
B2B Marketing time lag: a definition
Marketing time lag describes the time delay between marketing action and revenue recognition or business outcome.
Unlike in the B2C world, where a purchase often takes place within minutes of an ad click, B2B customer journeys are notoriously long and complex. In fact, they’re 192 days long on average, and some journeys stretch well beyond the year mark.
This journey is further complicated by the number of stakeholders who are typically involved in that buying journey. Rarely is the decision-maker or the holder of the purse strings initiating the process themselves. And far from the simplified assumptions we often make, these journeys are never linear; prospects come in and out of the buying cycle, often with long pauses.
And it is all of these factors together that produce the marketing time lag.
But not all time lags are the same.
Certain marketing activities have longer lags than others, as Dale highlights, brand plays typically have longer time lags than demand (a point echoed by Andrew Davies in this conversation on Brand vs. Demand), not to mention influences of the customer segment, where larger companies usually take longer to close than smaller ones.
The consequences
The time lag presents an obvious challenge for planning and forecasting.
B2B marketers need to account for this time lag for their forecasting assumptions to be realised (more on this below the charts). Failing to do this results in setting unrealistic objectives and timelines, which in turn means:
Operating on misplaced assumptions. Whether it’s experiments, planning or simply expectation setting, not knowing how long it takes for revenue recognition, can result in setting and working towards misplaced timelines.
Misalignment between marketing and sales. In B2B Marketing and Sales need to be in lockstep. Misplacing timelines can create friction (in both directions) that complicates collaboration between these teams.
Missed targets - the culmination of these misplaced timelines can ultimately result in missing targets - that were incorrectly set in the first place. And with missed targets come all the consequences to the individual and the team (we all know the employee turnover rate of marketers…)
Once again, the facts are clear. Knowing how long it takes for your marketing efforts to generate expected revenue can help you better navigate the choppy waters of B2B marketing.
Marketing time lag benchmark
In his contribution, Dale Harrison uses a toy model assumption-based approach to arrive at a ballpark value for his marketing time lag. Which you can read about here.
Fortunately, we’ve got access to aggregated B2B customer journey data from our B2B customers that enables us to put real values into that graph and examine the return curve.
Thanks to collecting end-to-end B2B customer journey data for companies across the globe, at Dreamdata we’ve got access to all the data points needed to put together an accurate marketing time lag curve.
That is, we are able to track when individuals from certain companies engage with marketing sources and channels and can tie this to when those companies become won deals.
Methodology
For our analysis, we gathered data on all marketing interactions in Q1 2022 and observed how many won deals were impacted by the end of 2023.
These won deals represent the total 100% of the revenue after 24 months. We then examined various time points to determine the number and revenue of deals closed by then.
For example, our marketing activities in that quarter may have influenced 10 deals, generating a total revenue of $1M. By the end of the first quarter, 4 deals worth 400k influenced by our marketing were closed, giving us 40% impact.
The reason why we’re using percentages as opposed to monetary value in our graph is because this more uniformly represents all the sampled customers. Using total revenue would have disproportionately weighed towards the top-earning customer.
In all cases, we have taken any touch that took place in Q1, not just the first touch. After all, marketing efforts don’t just operate far up the funnel.
If you’re interested in the average first-touch impact on different channels and company sizes, you can check out these B2B go-to-market benchmarks.
The Marketing time lag graph
Let’s look at the marketing time lag for all marketing touches that took place in Q1 2022.
So what can we learn from this?
For starters, we can definitely confirm that there is a considerable time lag between marketing effort and revenue.
But most importantly, the curve shows us how this latency is spread.
While there is an initial surge in the impact of your activities, in this example it’s 37% by the end of first quarter, most of the revenue will not be impacted until well after this. So we need to be extremely careful about when we evaluate our performance, making sure that this isn’t premature.
The also graph reminds us of, is to always consider this latency when you’re analysing and comparing campaigns that were initiated at different points. For instance, say you’re running an annual report, you should be careful comparing Q1 campaigns against Q3 or Q4 campaigns as they’re at different points in their lifetime.
But the curve does more than tell us what we already know. The biggest takeaway is how a time lag curve can help you predict the ROI of our activities, and how it can help you better compare between efforts that are taking place at different points in time.
How to integrate your time lag into your marketing forecasting and planning
Let’s take a look at how you can do this.
Once you know your time lag curve you can use early results to accurately benchmark and predict the outcomes of your marketing activities at a later point in time.
For instance, say you want to predict the end-of-year return based on your Q1 efforts - which have say, impacted $100k. Using the graph above, we can predict that by Q4 the efforts will impact a further $70k.
So you can expect a return of approx. $170k by the end of the year.
The benefits of these analyses on setting targets (and expectations) is obvious. Once we know when to expect certain return, we can plan our budget and resource allocation accordingly.
How to get your marketing time lag data?
Now that you’ve got a clearer idea of just how long it takes to get revenue recognition on average for these different marketing efforts, let’s turn to the million-dollar question: how can I learn my marketing time lag?
The short answer is clean end-to-end customer journey data.
The problem? It’s not that easy to get an accurate B2B customer journey.
As we described in the first section above, the B2B customer journey is long and complex.
It is multi-touch, multi-channel, multi-stakeholder (from the same accounts) and, as we’ve seen, spans multiple months.
As a result, B2B marketers need to contend with tons more data points than their B2C counterparts. Data points which, to complicate matters further, live in siloed tools on the tech stack.
To remedy this you need to bring all that data together and tie it to revenue. Which involves breaking the silos and pooling the data and then making sure it’s free from any duplication or errors, standardised, mapped, enriched and modelled.
To do this there are only two viable options. You can use in-house resources to build integrations and all the data transformation that goes with it, or you could buy an off-the-shelf tool like Dreamdata, that offers the complete package out-of-the-box.
In either case, these solutions will give you a little more than just time-to-value metrics, you will be able to accurately track and measure the impact of all your marketing efforts on pipeline and revenue.
Of course, if you’re at an earlier stage, you could just rely on this article’s insights to guide your decision-making.
A final word
So, we’ve delved into the complexities of B2B marketing, showing the significant delay between our marketing efforts and their impact on revenue.
This delay starkly contrasts with the quick transactions seen in consumer marketing and highlights how, in B2B, results unfold over a long period… we’re talking two years long...
As we’ve seen, understanding and factoring in the marketing time lag is not just helpful, it’s essential.
It guides us to set realistic goals and present accurate forecasts, and from these, craft strategies that enable us to generate real business value.
