Although in most of our reports we touch on the relationship between spend, pipeline, and revenue, we haven’t yet published a dedicated report that analyzes the relationship between ad spend and revenue. Initially, we planned to publish a comprehensive report analyzing the entire motion from ad spend to churn, but once I started writing the first part, I realized that it needed to be split to avoid publishing a twenty page report. Therefore, in this report, we’re analyzing the journey from spend to revenue, and in the next part, scheduled for release next week, we will analyze the new business pipeline, close rates, and churn rates.
Most of these Labs reports start with the same question: "What would my CMO want to know?" A prominent question that often arises concerns the cost of customer acquisition and churn data. Therefore, I'm surprised we haven’t published this report earlier. As mentioned, the initial goal for this report was to calculate customer acquisition costs, and in an ideal world, I would have liked to analyze this data with complete headcount and total marketing expenses, including tools and offline spend. However, this report is limited to paid media spend as we lack the other necessary data points.
- Spend: Any paid media spend, including but not limited to Linkedin, Google, Facebook, Reddit, YouTube, Bing, Capterra, or spending through ABM platforms.
- Sales Cycle: Time between the become an MQL date to closed won date, unless otherwise specified. Outbound sales cycle means time between the first meeting date with sales to closed won.
- Inbound: Deals created from a marketing touchpoint or organic/direct deals.
- Outbound deals: Deals created from a sales touchpoint.
- Total Deals: Deals created from inbound, outbound, and other sources, such as partnerships or similar activities, all new business.
- Deal Created: Defined as the point at which the deal amount field is filled.
- Revenue: The total value of closed won deals.
- CW: Closed won deals, new business, or new revenue.
- Inbound Ratio: The proportion of inbound deals in the total deal count.
- Date Range: From January 1, 2022, to March 31, 2024, a total of nine quarters.
- Sample Size: 28 B2B SaaS companies using HockeyStack.
- Sample Description:
In the 2023 Benchmarks report, we found out that the average sales cycle was 69 days; since this dataset covers almost the last two and a half years, we decided to start with calculating the average sales cycle for this time period. According to our data, the average sales cycle between January 1, 2022 to March 31, 2024 was 81 days. Since the first quarter of 2024 couldn’t have impacted the average so much, it’s safe to say that this data suggests that 2022 had much longer sales cycles than 2023.
Furthermore, this reduction in the sales cycle from 2022 to 2023 suggests an improvement in macroeconomic conditions. After calculating this average sales cycle, we decided to present the data in two ways: firstly, by examining the spend and revenue within the same quarter, and secondly, by analyzing the spend and revenue with a one-quarter delay (since a quarter delay would be enough to cover a 81-day sales cycle).
Similar to the trends observed in the sales cycle front, this chart above reveals that 2022 was a challenging year with the ROI barely above 1. However, with the beginning of 2023, we are seeing significant increase in ROI in the first two quarters. This increase then followed by a sharp decline in the latter half of the year. The first quarter of 2024 also mirrored the trend of the second half of 2023. However, it's important to note that this analysis disregards the impact of longer sales cycles. Let’s now examine this data with a quarter delay.
The second graph assumes that the spending in a given quarter influenced the revenue of the following quarter. Thus, although the revenue was added in the next quarter, this graph portrays it as if added in the previous one (i.e. spend happens in Q1-23, 81 days sales cycle, revenue is added in Q2-23, but instead of showing this revenue as Q2 revenue, we showed this as Q1 revenue in this chart to understand how ROI would change). With that in mind, I reckon this graph displays healthier data. We don’t see as significant a spike as in the previous graph, but it shows that, despite a tough 2022, things improved in its last quarter of 2022, and 2023 was generally a better year. Perhaps we don’t see a substantial spike at the end of 2023, but the overall ROI was more than 50% better in 2023 compared to 2022. This data also aligns with our findings from the 2023 benchmarks report; Q4 2023 saw the highest sales volume, yet due to a decrease in the ACV, it did not correspond to the highest revenue and the most revenue was actually added in Q1 2023.
Following the spend:revenue ROI analysis, the next thing I want to explore is the inbound ratio in total revenue. I am interested in this because I want to understand if there is a relationship between the increase in ROI and inbound; specifically, whether an increasing inbound ROI is accompanied by an increasing inbound ratio in total revenue, which could indicate that the ROI from outbound activities isn’t improving. Alternatively, if the inbound ratio remains stable, it might suggest that both inbound and outbound ROI are on the rise.
I find this graph particularly interesting because when we look at the first three quarters of 2022, no clear pattern emerges; despite the inbound ratio increasing throughout the year, there was no corresponding increase in spend:revenue ROI, so we don’t see a rise in inbound ROI even if the ratio of inbound in the total deals improved. However, looking at the second half of the graph, we see an undeniable overlap between spend:revenue ROI and the inbound ratio in revenue.
According to our "Marketing Influence in Outbound" report, 53% of revenue was inbound between January 2023 to February 2024. Yet, according to this data, we see that it was around 40% in early 2022 - suggesting that companies were heavily reliant on outbound strategies at that time. Conversely, in early 2023, the inbound ratio reached the highest level of the past 9 quarters and exceeded 60%, then it decreased around 50% levels and then surged again to 60% in the last quarter of 2023.
The graph above might not be the most relevant chart for this report, but I wanted to share it because I found it genuinely intriguing. Contrary to what we often think, our dataset shows that during 2022, the second month of each quarter was when the most revenue was added —not the last month - from inbound deals. However, starting in 2023, this pattern shifted towards what one might consider more "normal," with the most revenue typically being added in the last month of each quarter. But as we've observed throughout this entire report, 2022 was a weird year!
You might need to look at the data points closely, but this chart above shows the relationship between inbound and total revenue added over the last 9 quarters. In 2022, although the most inbound revenue was added in the second month of quarters, still the most revenue was added in the end of quarters, here we can see the impact of outbound deals closing at the end of quarters. With the beginning of 2023, we’re seeing an alignment where the most revenue from both inbound and outbound are being added at the end of the quarters.
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