E-commerce metrics strategic buyers demand: LTV:CAC, cohorts, and channel profitability explained

The E-commerce Metrics Buyers Scrutinise Before an Acquisition

When preparing an e-commerce business for a strategic acquisition or a significant funding round, the conversation has fundamentally shifted. It’s no longer enough to showcase top-line revenue growth. Sophisticated buyers and investors now scrutinise the underlying health and sustainability of that growth, looking for key e-commerce valuation metrics that prove long-term viability. They demand a clear, data-backed story that proves your business has strong unit economics, a loyal customer base, and an efficient growth engine.

Lacking this narrative, you risk a lower valuation or, worse, a failed deal. The key is to move beyond vanity metrics and prepare a diligence-ready view of your performance that proactively answers the tough questions they will inevitably ask. Getting this right demonstrates operational excellence and builds the confidence needed to close a successful transaction. It's the story the numbers tell about your future profitability that truly matters. For more on this, see our Acquisition Readiness hub for processes and practices.

The Three Core Questions in Every Buyer Due Diligence Checklist

Ultimately, a buyer’s due diligence process is designed to answer three fundamental questions about your e-commerce business. Answering these with clear, defensible data is the foundation of a strong valuation and successful ecommerce exit preparation.

1. Is your growth not just rapid, but also profitable and sustainable?
2. Do your customers genuinely value what you sell, leading them to purchase again and again?
3. Can you identify and scale the marketing channels that attract your most profitable customers?

Question 1: Is Your Growth Profitable and Sustainable? Answering with LTV:CAC and Payback Period

A strategic buyer needs to believe that your growth model is a profitable machine, not a cash-burning furnace. The two primary metrics that prove this are the Lifetime Value to Customer Acquisition Cost (LTV:CAC) ratio and the CAC Payback Period. These are essential online store financial benchmarks for any serious acquisition discussion.

LTV:CAC Ratio: The North Star of Unit Economics

LTV:CAC measures the total value a customer brings over their lifetime against the cost to acquire them. This ratio proves you aren't just 'buying revenue' at a loss. While benchmarks vary by industry, a 3:1 LTV:CAC ratio is the widely accepted standard for a healthy, scalable model. It signals that for every dollar you spend on acquisition, you generate three dollars in lifetime value, providing enough margin to cover overhead and generate profit.

To calculate this, you first need to understand its components. Customer Lifetime Value (LTV) is the total gross margin a customer generates throughout their relationship with your brand. Customer Acquisition Cost (CAC) is the total sales and marketing spend required to acquire a single new customer. A common mistake is to calculate LTV based on revenue; buyers will always recalculate it using gross margin to see the true profit per customer.

CAC Payback Period: The Lens on Capital Efficiency

While LTV:CAC demonstrates long-term profitability, the CAC Payback Period is the cash flow lens on your acquisition strategy. It answers a critical question: how many months does it take to earn back the money spent to acquire a new customer? This metric is vital for any e-commerce brand managing tight inventory cycles and marketing budgets.

For buyers, it indicates capital efficiency. A customer acquisition cost (CAC) payback period under 12 months is a strong signal of a healthy business model. Conversely, if your payback period stretches beyond 18 months, it raises serious questions about your cash flow and the sustainability of your growth, as it means you are funding new customers for a year and a half before breaking even. The reality for most startups is more pragmatic: focusing on channel-specific CAC, rather than a misleading blended average, is essential for showing a buyer you truly understand your acquisition economics.

Question 2: Do Customers Genuinely Value Your Product? Answering with Customer Retention Analysis

Buyers are wary of brands built on fleeting trends or aggressive, one-time promotions. They seek genuine product-market fit, which is best demonstrated by a loyal, returning customer base. The most effective way to prove this is not with a simple site-wide repeat purchase rate, which can be easily skewed, but with cohort retention analysis.

Why Cohort Analysis is Non-Negotiable

A cohort is a group of customers who made their first purchase in the same time period, typically a specific month. By tracking each cohort's purchasing behavior over subsequent months, you can show buyers precisely how customer value evolves over time. It answers the key question: of the customers who joined in January, what percentage came back to buy again in February, March, and April?

This method smooths out the noise from one-off sales events and reveals the true stickiness of your product. It directly addresses a common pain point for founders: struggling to pull accurate cohort data from multiple e-commerce and marketing platforms. Presenting this data in a clear cohort chart is a powerful tool. In this chart, a buyer wants to see the retention curves for each monthly cohort flattening out over time, not dropping to zero. A flattening curve is the signal that you have a core, loyal customer base that consistently returns.

While tools like Shopify Analytics provide a starting point, and platforms like Triple Whale or Glew offer deeper insights, a basic analysis can be built in a spreadsheet. To do this, you need to export order data with customer IDs, order dates, and order values, then pivot the data to track repeat purchases by the month of the customer's first order.

Question 3: Where Do Your Most Profitable Customers Come From? Answering with Profitability by Sales Channel

Knowing which marketing channels work is essential, but the definition of 'work' is often misunderstood. Many founders focus on Return on Ad Spend (ROAS), a metric that sophisticated buyers view with skepticism. The problem with ROAS is that it measures revenue, not profit. A channel can have a high ROAS but be deeply unprofitable once you account for product costs, shipping, and transaction fees. This is why buyers demand to see profitability by sales channel, measured by Contribution Margin.

Contribution Margin: The True Measure of Channel Performance

Contribution Margin reveals the true cash profit generated by each marketing dollar. It is calculated as Revenue minus Cost of Goods Sold (COGS) and all other variable costs tied to that specific channel. These variable costs include ad spend, payment processing fees, and fulfillment costs. Using a method like activity-based costing can help you allocate these costs precisely.

A scenario we repeatedly see is a founder over-investing in a channel that looks good on ROAS but drains cash on a contribution basis. Consider a comparison between two channels. A Facebook Ads campaign might show a 4.0x ROAS, turning $25 in ad spend into $100 of revenue. However, after accounting for $40 in COGS and $10 in other variable costs, the contribution profit is only $15, for a 15% Contribution Margin. In contrast, an email marketing campaign with a 20.0x ROAS generates the same $100 in revenue from just $5 in ad spend. With the same costs, its contribution profit is $45, for a 45% Contribution Margin. While both are profitable, one generates three times more cash per sale. Presenting this level of analysis demonstrates a deep understanding of your business levers.

The Practical How-To: Building Your Diligence-Ready Data View

For most founders, there is no dedicated data team to build a sophisticated business intelligence dashboard. Data is often scattered across Shopify, your accounting software like QuickBooks or Xero, Google Analytics, and various marketing platforms. Here is a step-by-step approach to building the analysis yourself.

Step 1: Consolidate Your Raw Data

The first task is to centralise your data. This means exporting order and customer data from Shopify, your Profit and Loss (P&L) and COGS data from your accounting software (typically QuickBooks in the US or Xero in the UK), and your channel-specific spend data from platforms like Facebook Ads and Google Ads. A well-organized Google Sheet or Excel workbook is the most effective starting point. Documenting these data flows is a key part of process documentation.

Step 2: Structure Your Workbook Logically

Next, structure your workbook to mirror the buyer’s questions. Create separate tabs for each of the core analyses: one for your LTV:CAC and payback period calculations, another for your cohort retention grid, and a third for your channel-level contribution margin breakdown. This approach forces you to understand the mechanics of your own data before attempting to automate. Follow standard financial process documentation when handling P&L and COGS exports.

Step 3: Build the Calculations Manually First

Build your calculations step-by-step. Do not aim for a perfect, automated dashboard on day one. Start by calculating a historical 12-month LTV for a few past cohorts. Map out your acquisition costs per channel for the same periods. The first step is proving the model and the narrative manually. This hands-on process ensures you understand every number you present. A clear, well-sourced spreadsheet is often more impressive to a buyer than a flashy but unsubstantiated dashboard.

Conclusion: From Growth Metrics to a Profitable Growth Narrative

Preparing for an acquisition or fundraise requires shifting your focus from top-line growth to the underlying metrics that prove your business is healthy and scalable. Strategic buyers are looking for a clear story told through data. Getting this right not only prepares you for due diligence but also helps you run a more profitable business day-to-day.

To build a compelling case, concentrate on three areas. First, prove your unit economics are sound with a strong LTV:CAC ratio (aiming for 3:1) and a CAC payback period under 12 months. Second, demonstrate genuine customer loyalty with cohort retention analysis, not a simple site-wide repeat rate. Finally, ditch vanity metrics like ROAS in favor of true channel profitability measured by contribution margin. This shows you understand precisely where your most profitable customers come from. By mastering these metrics, you build a powerful narrative of sustainable, profitable growth that will resonate with any sophisticated buyer. Explore more in our Acquisition Readiness hub.

Frequently Asked Questions

Q: What is a good LTV:CAC ratio for an e-commerce startup?

A: The industry benchmark for a healthy LTV:CAC ratio is 3:1. This indicates that for every dollar spent acquiring a customer, you generate three dollars in lifetime gross margin. A ratio below this may suggest unsustainable spending, while a much higher ratio could mean you are underinvesting in growth.

Q: Why is cohort analysis better than repeat purchase rate for due diligence?

A: A site-wide repeat purchase rate can be easily distorted by one-time promotions or seasonal spikes. Cohort analysis provides a much clearer view of customer loyalty by tracking the behavior of specific customer groups over time. It reveals the true "stickiness" of your product and proves long-term customer value.

Q: How do I calculate Contribution Margin for a specific marketing channel?

A: To calculate a channel's Contribution Margin, subtract its specific variable costs from the revenue it generated. The formula is: Revenue - COGS - Ad Spend - Payment Processing Fees - Fulfillment and Shipping Costs. This calculation shows the true profit generated by that channel, unlike ROAS which only considers revenue.

Q: What if my CAC payback period is longer than 12 months?

A: A payback period over 12 months can be a red flag for buyers, as it signals capital inefficiency and potential cash flow strain. If your period is longer, you must have a clear narrative explaining why, such as a high LTV from a subscription model that justifies the longer payback window.