How granular reporting helped VEED.io increase 6-figure monthly Paid Search spend while flatlining CAC
We cover how a shift from CAC to ROAS measurement led to a new campaign taxonomy and improved performance for SaaS VEED.io
Leveraging marketing analysis to scale paid media spend while maintaining efficiency
Valuable marketing analysis leads to insights that can directly translate into campaign changes. For a recent project with VEED, the company wanted to scale thir 6-figure monthly Paid Search 20% month-over-month without sacrificing efficiency.
Our analysis led to significant tests on Paid Search: the new model was tested in one of the most important campaigns, which resulted in an increase in subscriptions (20% increase with a flat CAC), impression share (10% increase), and ARPU (12% increase). In this article, I cover how we achieved that with:
Dashboard and models that calculate ROAS across different granularity levels
Changes to campaign taxonomy, switching from a traditional market-level to use-case level
The challenge of scaling spend, with a 20% MoM target
VEED is a market-leading video editing tool with over 4 million users worldwide. Search has been a backbone in their growth—VEED.io has 156,000+ backlinks. The organisation was spending a six-figure sum on Google Ads across Paid Search and PerformanceMax and was looking into increasing that spend 20% month over month.
But to scale efficiently, the organisation needed to expand its budget while maintaining CAC—a familiar yet tricky challenge. To do that, VEED needed to identify which strategies to double down on and which to remove from the plan. That’s where I came in.
VEED’s bidding strategy and campaign taxonomy
Target CPA for “subscription” and market-level taxonomy
I always start projects with an audit. This one was no different.
VEED was running target CPA strategies. The conversion event fired when a user successfully subscribed for the first time. They were using Enhanced Conversions and had a high consent rate. Almost all subscriptions were occurring within the lookback period.
Their campaign taxonomy was conventional, with “market” defined at the highest level (Campaign) and “use cases" at Ad Group level. Most companies use this structure to control the advertising budget for each country.
Gap in existing structure: homogenous treatment of users and use cases
I identified a possible gap that could harm their campaign efficiency: the CAC bidding structure treated all converted users as if they had the same ROI.
But since VEED is a subscription product, it was likely that not all users were worth the same to the business. Some would have higher churn or retention or even multiple seats (for B2B users), leading to a diversity in ARPU. Therefore, our analysis needed to go beyond CAC and identify the most valuable users.
A possible variable that could affect ARPU was market: for example, users in the United States being more valuable than users from Brazil. Another likely impacting variable was the product use case. For example, certain editing features were mostly sought out by businesses and some features are only used sparingly.
Measuring ROAS on a keyword-level
The analysis VEED needed
Based on these gaps, I identified that we had two challenges to tackle with our analysis:
Move from CAC reporting to ROAS reporting. Instead of reporting on Google Ads conversions alone, VEED’s first-party payment data needed to be integrated to identify which attributed subscribers had repeated payments or multiple seats.
Calculate ROAS across different levels. Data needed to be connected and segmented in order for us to identify the most valuable use cases, keywords, and markets. For this, we needed to leverage Ad Platform naming conventions.
Building the ROAS report
The solution put forward was to build a report that calculates ROAS on different levels: Campaign, Ad Group and Keyword. This report consisted of three separate data sets from VEED:
Ad Platform Spend, where we used Google Ads Reporting on BigQuery.
Attributed events, VEED’s custom attribution model table that attributes new users to a utm_source, utm_campaign and utm_term.
Revenue, VEED’s table that stores recurring payments on a user-level.
We built two different reports on Metabase, so it could be easily accessible by leadership:
Overall ROAS: Calculates the total return on ad spend for each campaign, market, ad group, use case and keyword.
ROAS in X days (payback period variation): Calculates ROAS in a custom lookback period. For example, how much revenue was recouped in the first 30 days of the campaign?
To gain insights on a use case level, we leveraged the existing Ad Platform naming convention. This enabled us to report on use cases as a whole (e.g., Add Subtitles) regardless of which campaign (or market) they belonged to.
Disclaimer: this report was only possible for Paid Search, since Performance Max doesn’t enable you to measure results on a keyword-level.
Increasing spend by 20% while flatlining CAC
Identifying the most valuable strategies
After building the report, I analysed the results and prepared recommendations for leadership. The biggest uncovered insight was regarding use cases. Certain features were very sought after, but did not lead to high value retaining users. On the other hand, certain features were likely to lead to attributed subscribers that had significantly higher ARPU.
With this information, VEED could identify which strategies to stop and which to receive further budget.
Shifting towards a use case budget allocation
Based on this insight, the agency prepared a test where the Campaign focused on a single use case (feature). Within the Campaign, each Ad Group targeted a market. The initial result was positive, so the new taxonomy was scaled across multiple use cases.
Since VEED now knew which use cases were the most valuable, they could max out spend in these keywords before allocating budget to lower quality search terms.
Increasing scale while maintaining efficiency
The granular insights on ROAS, accompanied by changes in the campaign structure, enabled the company to:
Grow the overall number of attributed subscriptions by 20% while maintaining a flat CAC in the tested campaigns
Increase impression share for their Generic terms by 10%
Acquire users with a 12% higher ARPU
Moving forward with ARPU prediction as offline conversions
VEED is continuing data projects that will enable them to optimise campaigns for tROAS over tCPA. Because recurring payments occur outside the maximum 30-day lookback period of Google Ads conversions, the company is looking into leveraging lead scoring to predict ARPU. In this future set up, campaigns would optimise towards an offline event that fires from the API instead of the tag-based subscription event.
Upcoming guide on how to measure Google Ads ROAS for subscription products
In this article, I covered the business impact the ROAS dashboard had on VEED.io’s Paid Search performance. My next article will be going deeper into how to build your own ROAS dashboard, including:
Naming convention requirements
Data sets structure
Examples of ROAS reports
If you’re interested in learning how the sauce is made, don’t forget to subscribe (for free!) and be notified as soon as the guide is live.