One of the biggest questions B2B SaaS companies have about paid marketing is their expected ROI.
Essentially, they want the ability to forecast, “If we spend X dollars next quarter, we’ll get Y dollars in return.”
But the majority of companies we speak with don’t have the necessary data to do accurate forecasting. Typically they’re missing at least one of the following:
- 1-2 years worth of historical PPC data
- A clear understanding of their allowable cost per acquisition
- Accurate data on their pipeline metrics and/or close rate
Lacking this data stems from having run ads for an insufficient amount of time (ie. their sample size is too small for accurate forecasting), or having the wrong tracking systems in place in their Google Analytics and CRM accounts.
Often, it’s both.
In this article, we’ll describe the exact steps we take to help companies reach a point where they can forecast their paid marketing ROI (based on actual data). It’s a repeatable process that any B2B SaaS company can use.
Are you a B2B SaaS company looking to improve the forecasts and results of your paid marketing? Reach out and schedule a Free SaaS Scale Session to learn how we can help.Prefer to listen to this post? Here’s the audio:
Our 3-Step Process for Developing Paid Marketing ROI Forecasts
Before you can begin the process of forecasting paid marketing ROI, you have to get your Google Analytics and CRM tracking in order. Building a system that connects both is crucial to accurately tracking conversion rates through your funnel.
Once tracking systems for our clients are properly in place, we execute the following steps:
- Benchmarking KPIs: Over the course of 90 days, testing different paid channels for customer-channel fit, and setting baseline levels of performance for key PPC metrics.
- Quarterly KPI Forecasting: Once we have 90 days of data on our KPIs, we forecast the number of leads and customers they can expect from PPC in the upcoming quarter (based on what they plan to spend and averages of their KPIs from the previous quarter).
- Calculating Expected ROI: We then calculate expected ROI based on average customer lifetime value (LTV).
Below we’ll walk through each of these steps in detail.
1. Benchmarking KPIs
Benchmarking is the process of setting baseline levels of performance for the key metrics in paid marketing:
- Cost Per Click (CPC)
- Click Through Rate (CTR)
- Cost Per Acquisition (CPA) — the equivalent of cost per demo or trial signup
- MQL, SQL, and Closed/Won Conversion Rates
- Customer Acquisition Cost (CAC)
Through our customer-channel fit process, we track these metrics over the course of 90 days for each of the channels where we’re running paid ads. This gives us a minimum amount of data to begin creating quarterly KPI forecasts for the months ahead.
For each channel, we start with testing out different ad sets based on our client’s available data (eg. interests, demographics, website visitors, customers, etc.).
Our process works like this:
- Create an initial series of campaigns/ad sets.
- Apply daily budgets or bids evenly across each.
- Track performance of each campaign and ad set in terms of cost per click and click through rate.
- Each week, narrow down their campaigns by switching off the lowest performing ad sets and keywords, and shift the additional spend to the highest performers.
- Continue this process until their budget is being allocated only to the best campaigns from a cost per result perspective.
This process typically takes place over the course of the first 30-60 days. Then we continue running the highest performing campaigns throughout the rest of the first quarter.
This provides us with enough data to begin our initial quarterly KPI and ROI forecast.
Note: Based on the performance of each channel during that first 90 day period, we recommend how to distribute more of the overall budget to higher performing channels in the quarters that follow (and less to lower performing channels). So if Google and LinkedIn performed best, we’d allocate more of our budget there in the upcoming quarter, and less to Facebook and Bing.
2. Quarterly KPI Forecasting
To begin developing our KPI forecast for the upcoming quarter, we calculate averages of the key metrics we listed above for each channel (CPC, Conv. Rates, etc.) from our benchmarking quarter or our client’s historical data.
Then, we’ll take the amount our client wants to spend to calculate the number of leads — and ultimately customers — they can expect through each PPC channel.
Here’s an example to demonstrate:
Let’s say our client has a budget of $215,500 for the upcoming quarter. We’d distribute different amounts of their budget to each channel based on their performance during our benchmarking phase (where we test for customer-channel fit).
In this case, if Google and LinkedIn performed best, we’d allocate more budget to those channels.
From here, we’d use the average CPC and key funnel conversion rates (MQL, SQL, Close) — from our historical campaign performance — to calculate the number of MQLs, SQLs, and customers they’re likely to receive from each channel.
For example, let’s say we decide to allocate $77,500 of their budget to Google Ads.
Based on their historical average cost per click, they can expect about 11,707 clicks.
From there, we can multiply those clicks by their MQL conversion rate of 2.5% to get to an expected number of MQLs: 293.
Then, by multiplying that by their MQL to SQL conversion rate of 37.5%, we end up with an expected number of 110 SQLs from Google Ads.
Finally, by applying their historical SQL close rate of 29.5%, we would forecast them to acquire 32 customers from Google Ads in the upcoming quarter at a CAC of $2,393.67.
We’d repeat this process for each channel, and add up the number of customers expected from each. In this example, our client could expect 91 closed customers from PPC for the quarter.
Based on their spend, this would equate to a blended CAC of $2,362.35.
From here, all we’d need is their average customer lifetime value (LTV) to calculate and forecast their expected ROI from that quarter.
3. Calculating Expected ROI
To calculate expected ROI, we’d simply multiply the total number of new customers we expect to acquire by average LTV. Then, we’d divide that by their total PPC spend that quarter.
In this example, we’ve forecasted this client would expect 91 closed customers based on their historical performance and planned PPC spend. With an average LTV of $10,000, and a quarterly PPC spend of $215,500, the equation would be:
(91 Customers X $10,000 LTV) / $215,500 Spend = 423.31% ROI
This would equate to an LTV to CAC ratio of 4.23. If this forecast were to pan out and be accurate, this company would be profiting more than three times as much as what they’d be spending to acquire new customers.
As we covered in our article on SaaS marketing budgets, this company would be well positioned to continue putting a significant amount of those profits back into their PPC marketing.
Forecasting ROI from paid marketing is a complex process that takes time, especially in B2B SaaS where sales cycles can be 6 months to upwards of a year.
Most of the time companies get forecasting wrong because they don’t have the data necessary — whether it’s their lead to close rates or a lack of historical PPC data.
If you follow the process laid out above, over the course of several quarters you can get your company to a point where you can do reasonable and accurate ROI forecasting.
You just need to ensure your CRM and analytics tracking are set before you begin, and then repeat your quarterly iterations of forecasting, validation, and reforecasting.
Are you a B2B SaaS company looking to improve the forecasts and results of your paid marketing? Reach out and schedule a Free SaaS Scale Session to learn how we can help.