Cohort-Based Discount Impact for SaaS: Measure LTV to CAC and Profitability

Cohort-Based Discount Impact for SaaS: How to Measure Profitability

Promotional discounts feel like a straightforward growth lever. You offer a deal, new signups increase, and top-line revenue gets a temporary boost. Yet, weeks later, the critical question remains unanswered: was it actually profitable? This uncertainty makes it difficult to judge whether a discount strategy is building a sustainable business or simply acquiring low-quality customers who churn after the deal expires. Without a clear method for how to measure discount impact on SaaS customer lifetime value, founders are often left guessing. This analysis breaks down a practical, four-step approach for early-stage SaaS companies to move from ambiguity to clear, data-backed decisions about their pricing experiment results, using the tools they already have. See the Dynamic Pricing & Promotion Impact Modeling hub for broader methods.

Why Standard Reporting Fails to Measure Discount Impact

For SaaS startups in the UK and USA, the pressure to demonstrate traction is immense. A successful discount campaign can look great on a weekly report, but it often masks underlying issues with unit economics. The core problem is that fragmented data across billing systems like Stripe, a CRM like HubSpot, and various analytics tools prevents a clear view of revenue impact by customer group. This fragmentation leads directly to several primary pain points for leadership teams.

First, it creates an unclear picture of post-discount lifetime value, making it impossible to judge whether promotions are truly profitable. Second, it prevents the proper isolation of a discount's impact by signup cohort, mixing promotional customers with organic ones. Finally, without forward-looking models, this ambiguity can lead to misforecasting revenue. Over-discounting can easily choke cash flow and shorten a startup's runway. The goal is not to achieve perfect data science, but to build a “good enough” framework that provides directional accuracy to protect your financial health.

Core Concepts: Cohorts, Baselines, and LTV:CAC

Before diving into the analysis, it is important to align on a few core concepts. The entire approach hinges on cohort-based analysis, which involves grouping users by a shared characteristic, most often their signup date. For this purpose, we create two specific groups: a “discount cohort” (customers who used a promotional code) and a “baseline cohort” (customers who signed up in the same period without a discount).

Comparing these two groups allows you to isolate the effect of the discount itself. This is a critical distinction from comparing discounted users to your “overall average” customer, which can be misleading due to seasonality, product changes, or shifts in marketing channels over time. By using a contemporaneous baseline, you get a much cleaner, more reliable comparison of performance.

The key metric for this analysis is the Lifetime Value to Customer Acquisition Cost (LTV:CAC) ratio. This ratio tells you how much value a customer brings in relative to the cost of acquiring them. A healthy SaaS business typically aims for an LTV:CAC ratio of 3x or higher. Success isn't just about maintaining LTV or lowering CAC in isolation; it's about improving the efficiency of the entire acquisition funnel, which the LTV:CAC ratio perfectly captures.

Step 1: Isolate Your Data with a "Good Enough" Setup

The first question is always: What data do I actually need, and where do I find it? The reality for most pre-seed to Series B startups is more pragmatic than you might think. You do not need a pristine data warehouse or a team of data engineers to get started. You can begin with a simple spreadsheet and data exported from your existing tools.

Here’s what you need to pull for a specific period, for example, the month of March:

1. From your payment processor (Stripe/Chargebee): Export a list of all new subscriptions created in March. This data should include the customer ID, subscription start date, the plan they chose, their monthly recurring revenue (MRR), and crucially, whether a discount code or coupon was applied. This is the backbone of your analysis.
2. From your marketing platforms (Google Ads, LinkedIn, etc.): Sum up the total advertising spend for that same period. While perfect attribution is difficult, a total spend figure provides a solid starting point for your CAC calculation.
3. From your finance tools (QuickBooks in the US or Xero in the UK): Ensure you have a clear record of the total value of discounts given. Payment processors like Stripe often provide this directly in their reporting dashboards, which simplifies the process.

Combine this information in a single spreadsheet. You should have one primary tab listing all new customers from the period, with a dedicated column indicating if they used a discount. This simple setup is your foundation for effective customer segmentation analysis. For accounting purposes, remember that significant discounts can be considered variable consideration under revenue recognition standards like IFRS 15.

Step 2: Use Customer Segmentation Analysis to Define Cohorts

Now that your data is in one place, you can answer the next key question: Who am I comparing my discounted customers against? This is where you formally define your cohorts. The integrity of your entire analysis depends on creating a valid comparison group.

Using your spreadsheet, filter and separate your new customers into two distinct lists based on their customer IDs:

• The Discount Cohort: This group includes all customers who signed up in March and have a value in the “discount code” column. For instance, this would be everyone who used the ‘SPRING20’ code.
• The Baseline Cohort: This group includes all customers who signed up in March but have no value in the “discount code” column. These are your organic or full-price signups from the same period.

This method ensures you are comparing apples to apples. Both cohorts were exposed to the same product, the same general marketing campaigns (minus the specific offer), and the same external market conditions. This isolates the discount as the primary variable you are testing. Avoid the common mistake of comparing your March discount cohort to your historical average of all customers from the previous year. An old baseline pollutes your analysis with too many variables, making it impossible to draw a clean conclusion about your promotional offer effectiveness. Treat each cohort like a pricing experiment.

Step 3: Calculate Cohort Retention Metrics and the LTV:CAC Ratio

With your cohorts defined, it’s time to calculate the numbers that tell you if the discount actually worked. You will need to calculate the LTV, CAC, and the resulting LTV:CAC ratio for both the Discount Cohort and the Baseline Cohort. A scenario we repeatedly see is that founders focus only on top-line signups, missing the deeper story told by these fundamental profitability metrics.

First, calculate the Customer Acquisition Cost (CAC) for each group. The formula for the discount group must account for the discount itself as a marketing expense. A good starting point for the Discount Cohort CAC is: CAC = (Marketing Campaign Spend + Total Discount Given) / Number of New Customers. The CAC for the baseline group is simply the marketing spend divided by its customer count.

Next, you must track cohort retention metrics over several months. Look at the percentage of each cohort that is still active after 30, 60, and 90 days. This churn data will directly feed into your LTV calculation. A simple LTV can be estimated as: LTV = (Average Revenue Per User) / (Churn Rate). Be sure to use the churn rate specific to each cohort, as they will likely differ.

For US reporting, refer to ASC 606 guidance on variable consideration, which mirrors the principles of IFRS 15.

Let’s walk through a synthetic example for a SaaS company, “DataScribe AI,” analyzing its Q1 ‘Q1SALE’ promotion.In this scenario, the Baseline Cohort (full price) acquired 500 new customers with $25,000 in marketing spend, resulting in a CAC of $50. Over six months, their LTV was calculated to be $200, yielding an excellent LTV:CAC ratio of 4.0x.The Discount Cohort ('Q1SALE') brought in 800 new customers with $40,000 in marketing spend. However, the promotion also gave away $16,000 in total discounts. Including this in the acquisition cost brings their total cost to $56,000, for a CAC of $70. This cohort proved less sticky, with a lower 6-month LTV of $150. This resulted in a much weaker LTV:CAC ratio of 2.14x.

In this example, the discount successfully drove more volume, but the unit economics are significantly worse. The LTV is lower and the CAC is higher once the discount cost is properly included. The LTV:CAC ratio clearly shows the promotion was not a financial success, even though it boosted the top-line customer count.

Step 4: A Decision Framework for Your Pricing Experiment Results

Your analysis might show the discounted LTV is lower, but what if your CAC was also lower? Was the trade-off worth it? This is where a simple decision framework helps translate the numbers into a clear strategy. Evaluating success based on the improvement of the LTV:CAC ratio, not just on one isolated metric, is key to making sound financial decisions.

Here is a simple framework for categorizing your results:

• Win (Scale It): The LTV is only slightly lower (e.g., less than a 15% drop), but CAC is significantly lower, leading to an improved LTV:CAC ratio compared to the baseline. This is the ideal outcome. The discount attracts a high-quality customer base more efficiently.
• Trade-Off (Use Strategically): The LTV is moderately lower (e.g., a 15-30% drop), but CAC is also much lower, keeping the LTV:CAC ratio stable or close to the baseline. This outcome is not a clear win, but it can be a valuable tool for specific goals like entering a new market or hitting a user-count milestone for a funding round.
• Loss (Kill It): The LTV plummets (e.g., more than a 30% drop) and the lower CAC does not make up for it. The LTV:CAC ratio is worse than the baseline. As seen with DataScribe AI, this kind of promotion is unprofitable and should be discontinued immediately.

It is also critical to consider that different discount types attract different customer profiles. For example, a first month free offer might attract users with low purchase intent who churn immediately in month two, leading to a very low LTV. In contrast, a “20% off the annual plan” offer requires more commitment and may attract a higher-quality customer, even if the initial revenue is lower. Each offer needs its own separate cohort analysis. Use promotional margin erosion models to get early warnings on profitability.

Putting It All Together: From Analysis to Action

For an early-stage SaaS business, making sound decisions on promotions is not an academic exercise; it is a matter of runway. By implementing this cohort-based analysis, you can move beyond vanity metrics like raw signup numbers and understand the true profitability of your SaaS discount strategies.

Here are the key actions to take:

1. Prioritize the LTV:CAC Ratio: This is your north star metric for customer acquisition. A discount is only truly successful if it improves or at least maintains this ratio relative to your full-price baseline cohort.
2. Embrace "Good Enough" Data: Do not wait for a perfect data infrastructure to get started. The insights you need are likely accessible today with simple exports from Stripe, your ad platforms, and your accounting software into a spreadsheet. The cost of inaction is far higher than the cost of imperfect analysis.
3. Use Contemporaneous Baselines: Always compare your discount cohort to customers who signed up in the same period without a discount. This provides the cleanest and most reliable comparison by controlling for seasonality and market changes.
4. Decide with a Framework: Use the Win, Trade-Off, and Loss thresholds to make clear, defensible decisions about which promotions to scale, use strategically, or eliminate entirely. This disciplined approach ensures your growth tactics support a sustainable, profitable business model. Explore the Dynamic Pricing & Promotion Impact Modeling hub for related frameworks.

Frequently Asked Questions

Q: How long should I track a cohort to get a meaningful LTV?
A: For a monthly subscription SaaS, tracking a cohort for at least 6 months is recommended to get a reliable read on churn and lifetime value. A 12-month period is even better if you have the data history, as it helps smooth out any initial volatility in customer behavior.

Q: What if I ran a promotion without a clean baseline cohort?
A: If you do not have a simultaneous full-price cohort, you can use the month immediately prior to the promotion as a baseline. However, you must acknowledge potential seasonality in your analysis. For future promotions, ensure you always maintain a baseline group for a clean comparison.

Q: How does this analysis change for annual vs. monthly plans?
A: The principles are the same, but the timeline is longer. For annual plans, you must track renewal rates after 12 months to understand churn. LTV calculation becomes simpler (Average Annual Contract Value * Average Customer Lifetime in Years), but requires more patience to gather the necessary data.

Q: Can this method be used for freemium-to-paid conversions?
A: Yes, absolutely. In a freemium model, your "Discount Cohort" could be users who converted to paid using a special offer, while the "Baseline Cohort" would be users who converted organically during the same period. The analysis helps determine if conversion offers attract valuable, long-term customers.