Let’s dream big: is 2026 the year your brand buys that Super Bowl ad?
With Super Bowl 60 ad slots rumored at $8 million for 30 seconds, it’s safe to assume that’s out of reach for most ecommerce brands. The problem is, brands with huge budgets are also making assumptions: that their customer lifetime value figures will justify this large investment.
The vast majority of ecommerce brands aim to keep customer acquisition costs aligned to revenue goals by measuring customer lifetime value (CLV). To find this, they multiply customers’ Average Order Value (AOV), Purchase Frequency, and Customer Lifespan to calculate CLV and – hey, presto! – the optimistic numbers needed to justify a Super Bowl ad.
Here’s the catch: the resulting CLV figures can often be misleading because of easy-to-make faults in the calculations. The result? Teams overestimate loyalty and overspend on acquisition – chasing supposedly ‘loyal’ customers who won’t actually pay them back.
Most often, the problem figure is customer lifespan.
Let’s unpack why lifespan is so difficult to measure in ecommerce, and how to get it right.
Why calculating customer lifespan in ecommerce is challenging
Unlike SaaS businesses with clear, conversion-defined data points – signup date for start and cancellation date for churn – customer lifespan in ecommerce is ambiguous.
Ecommerce lacks a clear cancellation or offboarding event. Customers go dark and return unpredictably: how do you know when a shopper is truly gone?
Customers’ shopping behavior isn’t linear (that’d make calculating lifespan far easier). Instead, it ebbs and flows with seasonal gifting, social trends, product utility, and brand loyalty.
This last one’s a biggie: loyalty is fleeting. Sixty-two percent of customers switch brands after a single poor experience, and in leading economies, many switch favorites multiple times a year.
That loyalty isn’t decided in a vacuum – it’s shaped by the same factors that drive demand in ecommerce and can either extend a customer’s lifespan or cut it short.
Key factors influencing loyalty and demand include:
- Customer Service and Support: responsive help, chatbots, and easy returns reduce friction and encourage repeat shopping.
- Price and Perceived Value: cost, product quality, convenience, payment options, and loyalty rewards influence perceived value.
- Shipping and Delivery: fast, low-cost, and reliable shipping is one of the biggest demand drivers.
- Trust and Brand Reputation: reviews, site security, and brand values influence whether customers feel safe buying again.
- Loyalty Programs and Rewards: loyalty-driven incentives turn occasional shoppers into long-term customers.
It’s enough to make your head spin: cyclical and sporadic shopping based on gift-giving or the latest TikTok virality. Demand factors influencing whether a customer comes back or heads to a competitor; judged during every interaction.
This whiplash is why confidence in CLV often feels forced – or worse, like a total guess.
Faced with these ambiguities, brands default to what’s easy to measure. They lump everyone into one average or lean on shaky industry benchmarks – neither of which tell the true story or reveal how to improve outcomes.
Let’s see how this plays out in real-life examples.
Examples – calculating customer lifespan challenges IRL
Meet Stephanie: a busy mom shopping at Berry Bonnet, a high-end children’s boutique.
Stephanie:
- Buys new clothing sizes every season, approximately 4 times per year.
- AOV is $125.
- Loyal to Berry Bonnet for 5 years (customer lifespan), until her kid insists on choosing her own clothes.
If CLV = AOV x Purchase Frequency x Lifespan:
Stephanie’s CLV = $125 x 4 x 5 = $2,500
Stephanie is worth $2,500 in revenue over her lifespan, giving Berry Bonnet a healthy budget to win over more parents like her. In this case, customer behavior is consistent and the data is clean. But, problems arise when her loyalty is applied across all customers.
Meet Mark: a college student shopping at Berry Bonnet for an aunt’s baby shower.
Mark:
- Buys one premium baby blanket for $120.
- Never shops Berry Bonnet again.
If Berry Bonnet averages Mark’s behavior with Stephanie’s – assuming the same order frequency (4/year) and lifespan (5 years) – the math stops mathing:
CLV = $120 x 4 x 5 = $2400.
Reality check: Mark’s actual CLV is $120 – he’s a one-and-done shopper. Berry Bonnet overestimates his value by $2280, due to faulty assumptions about order frequency and lifespan.
By blending loyal parents and one-time gifters, the brand assumes the average customer is worth ~$2,000+ and overshoots its marketing budget.
To afford that Super Bowl ad, Berry Bonnet would need 3,200 Stephanies or 66,667 Marks, without adjusting for margins.
Cough: yes, our Super Bowl example is far-fetched. But the point is clear: get lifespan wrong, and CLV is wrong. Get CLV wrong, and you burn marketing dollars making decisions that don’t reflect reality.
The problems with drawing on industry averages
Customer lifespan varies by industry, customer needs, product utility, price point, and business model. It’s unique to every ecommerce brand, making reliable benchmarks nearly impossible.
In fact, Googling “average ecommerce customer lifespan” doesn’t return true benchmarks. Instead, it suggests three-years is a popular baseline, with no source attribution.
Not all ecommerce brands retain customers for three years; Mark proves that. A subscription pet food store may see monthly repeat orders for 10 years, while a high-end camping shop might see the same customer every few years.
If we’re all using the same generalized, three-year lifespan, then we’re all skewing CLV.
Instead, build a reliable benchmark using your customer data. Calculate customer lifespan to get a more accurate CLV, optimize marketing spend, and reveal retention problems.
How to accurately calculate ecommerce customer lifespan
Calculate average customer lifespan by adding up all of your customer lifespans and dividing that sum by the total number of customers, for a set period of time (like one year).
ACL = Sum of Customer Lifespans / Number of Customers.
Find each customer’s lifespan by taking the difference between their first purchase date and their churn date. Separately, calculate purchase interval by measuring the gaps between repeat purchases.
Here’s a step-by-step breakdown:
- Calculate customers’ purchase interval (how often do customers usually buy?).
To do this manually, you’ll:
- Export order dates into a spreadsheet (customer ID and order dates).
- Filter by one customer.
- Calculate the interval (or difference) between each order:
- Customer orders on Jan 1, then Feb 15 and April 1.
- The interval between Jan 1 – Feb 15 = 45 days.
- The interval between Feb 15 and April 1 = 45 days.
- Keep going.
- Calculate the average across order intervals – that’s your order interval for one customer.
- In our example: (45 + 45) / 2 = 45 days).
- Average those intervals across all of your customers (add all of the intervals together and divide by the total number of intervals).
Shopify users: you’re in luck. Find purchase interval data in Shopify’s Analytics dashboard, under the “returning customer rate” and measure the average repeat purchase timeline between your customer cohort (the time period set for measurement).
- Estimate customer churn date (lifespan end date).
General rule of thumb: if a customer hasn’t purchased again within three-times their usual purchase interval, they’ve churned.
So, if our average order interval is 45 days, and our customer has not bought anything in 135 days = churned.
- Now, calculate each customer’s lifespan.
For each customer, add first order date and churn date in a spreadsheet. Take the difference and add it to a new column (the customer lifespan).
- Take the average across all lifespans.
Average lifespan = sum of all customer lifespans / number of customers
This gives you a far more reliable measure than defaulting to the same three-year guess everyone else uses. CLV based on lifespan is a solid start, but it still leaves us with blind spots.
Layer ecommerce retention metrics to strengthen CLV
Even with cleaner lifespan data, customer behavior adds unpredictability. Layering retention metrics fills the gaps and supports more confident forecasting.
1. Repeat Purchase Rate (RPR)
RPR measures the percentage of repeat customers and is a direct indicator of customer loyalty.
If RPR is low, most of your customers are one-and-done, meaning your average lifespan is shorter and your CLV shrinks. You might need to enhance your product’s utility, switch up your target audience, improve customers’ shopping experience, or rethink your pricing, marketing, or incentive strategy.
If your RPR is high, you are in good shape and can safely assume longer lifespans and more reliable repeat revenue.
Formula:
RPR = (Number of Customers with 2+ Purchases / Total Number of Customers) x 100
See it in action:
Let’s say you have 1,000 customers and 300 of those customers place more than one order:
RPR = (300 / 1000) x 100 = 30%
This means, 30% of your customers are loyal enough to return.
2. Repeat Purchase Frequency (RPF)
RPF analyzes how often repeat customers buy, indicating how engaged they are with your brand.
Combined with purchase interval, it helps you set a realistic cadence for lifespan. This will help you avoid overestimating CLV by assuming customers will buy more often than they actually do.
Formula:
RPF = Total Number of Orders / Number of Repeat Customers
See it in action:
If you have 1,000 total orders and 200 repeat customers:
RPF = 1000 / 200 = 5
On average, repeat customers purchased five times in the timeframe measured.
3. Repeat Purchase Value (RPV)
RPV shows the average spend per repeat order. Marketers use RPV to tailor upsells, bundles, or loyalty incentives to increase cart value.
Essentially, RPV is a refined AOV for loyal customers. If RPV is increasing, CLV forecasts should increase too. If it’s stagnant, you will need to optimize your loyalty strategy to increase long-term value.
Formula:
RPV = Revenue from Repeat Purchases / Number of Repeat Purchases
See it in action:
If you have $50,000 in revenue from 1,000 repeat orders:
RPV = 50,000 / 1,000 = $50
On average, each repeat purchase is worth $50.
4. Customer Retention Rate (CRR)
CRR measures the percentage of customers a brand retains over a set period. CRR helps you validate customer lifespan assumptions, CLV, and forecasting, because it shows the stickiness of your customer relationships.
High customer retention rates are highly valuable – increasing customer retention rates by just 5% can boost profits by 25% to 95%.
If retention is dropping, it means your customers aren’t staying as long as your CLV formula assumes, and you’re likely overspending on acquisition.
Formula:
CRR = (Number of customers at the end of a time period – New customers acquired during that period) / Number of customers at the start of the period X 100.
See it in action:
If over a one-year period, you start with 800 customers, gain 200 new customers, and end with 900 customers:
CRR = (900-200)/800 x 100 = 87.5%
Your brand retained 87.5% of your customer base that year.
5. Customer Churn Rate (CCR)
CCR is the flip side of retention; it’s the percentage of customers lost in a given period.
If your customer churn rates are high, your CLV projections are inflated. You may have a customer experience or loyalty problem, or your brand is not differentiating itself in your category.
Keeping churn low extends customer lifespan and makes your CLV more accurate.
Formula:
CCR = Customers Lost During a Period / Customer Total at the Start of a Period x 100
See it in action:
If over a one-year period you start with 1000 customers and end with 850 customers, you’ve lost 150 customers.
CCR = (150 / 1000) x 100 = 15%
Your brand lost 15% of your customers that year.
Knowledge is power: go even deeper into customer loyalty metrics and analysis techniques to decide the best set of metrics for your brand. Use them to gain clarity, boost loyalty, and incrementally increase your customers’ lifespan.
Accurate CLV hinges on getting customer lifespan right
If you get customer lifespan wrong or use generic benchmarks, you risk overspending on acquisition, misjudging loyalty, and making very real, very wrong business decisions.
When you calculate lifespan correctly and layer in the right set of supporting metrics, you replace guesswork with clarity. That’s how you set budgets you can trust, build loyalty strategies that work, and make bold bets with confidence.
Ready to sidestep the guesswork? Check out LoyaltyLion’s analytics dashboard for insights into CLV, revenue from loyalty programs, conversion by segment, and more – turning loyalty data into action.
