At Bizible we love looking at data to help us improve the profitability of our marketing. We're revenue focused and love sharing data in order to help you be performance-minded when it comes to managing B2B marketing teams.
We're working on a set of benchmarks for all B2B marketing channels and want to share some early results. In this post we'll look at LinkedIn paid media ROI.
How We Created The Benchmarks
This wouldn't be possible without revenue attribution and touchpoints tracking.
We looked at every paid LinkedIn touchpoint (e.g. paid social ads) across our customer base from 2017 through July 2018. Because our LinkedIn integration is somewhat new, only a subset of our customers have both revenue and costs being logged for this channel.
For our subset of customer data, we look at every closed-won opportunity between 2017 and July 2018, and every LinkedIn touchpoint attributed with revenue across that same time period.
We use a full path attribution model in order to give revenue credit across the entire customer journey, including marketing that happens post-opportunity creation.
Before diving into the results, a few important points to make about the data.
We look at cost data and closed-won opportunities across the same 1.5 year time span, meaning we don't take into account the B2B sales cycle.
In other words, this includes costs for LinkedIn ads directed towards leads and current open-opportunities.
Furthermore if a deal was closed-won at the start of 2017, we would not take into account of the cost for those LinkedIn touchpoints prior to 2017, because again, our cost and revenue data covers the same 2017 - 2018 time span.
Why does this matter?
For SaaS businesses, customer acquisition cost (CAC) is key to understanding the health of the business. CAC is a ratio that takes into account average sales cycle length. And rightly so, because a dollar spent in marketing today is (hopefully) some dollar value greater than one in several months.
Still, this data sheds light on some interesting points. So let's dive into the results.
Basic Statistical Measures of LinkedIn ROI
Average ROI for a subset of our customers is $9.59.
It's always nice to see marketing ROI for a channel be greater than one, i.e. for every dollar spent on LinkedIn our customers on average enjoy $9.59 in revenue.
There is quite a range of ROI rates in our data. We find a standard deviation of $6.07. Standard deviation is a measure of how much variation there is around the average (mean).
Some customers have ROI levels below $1 and some in the $20 - $35 range.
After doing a simple test, we see evidence the data comes from a Normal distribution. With that let's simulate some ROI numbers and make some probabilistic statements.
Simulating a sample from a Normal distribution, using the mean and standard deviation from our customer data set, we can create a histogram below.
The Y-Axis shows the count of customers (from the simulated data) and the X-Axis shows the ROI rate. We use this to get an idea of what the chances are that we'll achieve certain rates of ROI.
We can see that it is rare to get ROI rates at the tail ends of the curve and that it's centered around the average ROI of $9.59.
Using the empirical rule, we can say, assuming ROI data comes from a Normal distribution, that 68 percent of businesses investing in LinkedIn can expect ROI between the range of plus or minus the standard deviation ($6.06) of the mean ROI ($9.59), i.e. they can expect and ROI between $3.52 and $15.66.
A reminder of this concept below:
(image credit to Statistics How To)
Making Sense of the Data
There are many reasons for the wide variance in ROI we see across our customers. These reasons can include:
Bizible currently only has a small subset of customers tracking LinkedIn cost (it's a new integration)
Customers lack long term historic tracking because they are new to the channel, or they only recently started using Bizible
Their target markets respond differently to LinkedIn ads
They are using LinkedIn to build awareness and ROI isn't important
But the value here is the ability to set baselines, set revenue-based performance benchmarks in marketing, and understand expected return on investments.
When we can do this, we as marketers can make great decisions that make marketing relevant to the core business.
What is your monthly ROI? This is a great question to answer and metric to collect. Over time you can construct your own ROI analysis to better predict and identify patterns. For more information on optimizing LinkedIn for revenue, download the ebook below.
Stay tuned as we examine and compare the ROI of other channels, many of which have a larger sample size.