Marketers pour time and resources into tracking the entire customer journey. They optimize every touchpoint, refine content and strive to create seamless user experiences. But there’s a problem: many strategies are still too focused on short-term wins. In the long run, this approach can hold businesses back.
Because here’s the truth: not every customer is equally valuable, and not every churned customer is a true loss. For brands aiming to grow sustainably, Customer Lifetime Value (CLV) should be at the heart of every marketing decision. That’s where CLV attribution comes in.
What is customer lifetime value (CLV)?
CLV measures the total net profit a customer brings to a business throughout their relationship. It accounts for revenue generated across all purchases minus production, acquisition and retention costs. In other words, CLV tells you which customers are worth the most over time.
Great marketing isn’t just about acquiring as many users as possible. It’s about attracting and retaining high-value customers — those who generate long-term profitability.
Moving beyond short-term attribution
CLV-based marketing moves beyond short-term goals like a single transaction or acquiring the highest number of new customers. Instead, it maximizes the long-term value throughout the customer's lifetime.
A holistic marketing attribution approach should allocate marketing resources based on CLV rather than short-term gains. For example, if channel A has a better CPA (Cost per Acquisition) than channel B, but channel B has a better CLV-based ROAS (Return on ad spend), then a CLV-based approach would allocate more budget toward channel B.
Real-world case: FlixBus boosts ROAS by 233%
FlixBus, a leading international mobility provider, increased its social ROAS by 233% by switching to CLV attribution. This shift enabled FlixBus to move budget away from low-value acquisition channels and toward the campaigns that truly drove long-term revenue.
Why CLV matters especially in e-commerce
Many e-commerce companies still struggle to incorporate CLV into their marketing strategies — often due to a lack of the necessary knowledge or access to the right data. As a result, they’re often unsure whether their marketing efforts will ultimately pay off.
A common challenge arises when customers are acquired through multiple marketing channels but bring little to no profit on their first purchase. Only with subsequent orders do these customers become truly profitable.
The goal in marketing is to minimize spending on these follow-up purchases — for example, by reaching existing customers through platforms like Google or Facebook. Typically, lower-cost channels such as branded paid search or retargeting are used here. As a result, these later conversions are attributed to those cheaper channels, while the original acquisition channels are left without credit.
When marketers review key performance indicators like ROAS (Return on Advertising Spend) or CPO (Cost per Order), the initial acquisition channels often appear too expensive. But in reality, they played a critical role in generating long-term value. That’s why it’s essential to consider acquisition efforts across the entire customer lifecycle.
This is where CLV attribution becomes crucial. The goal is to evaluate marketing costs more accurately and optimize campaigns more effectively. Each touchpoint leading up to a conversion is assigned a share of the customer’s lifetime value. For every subsequent purchase, attribution values are distributed across all relevant touchpoints — including those from the very first interaction.
This leads to more accurate performance analysis. Campaigns that seem costly upfront may actually deliver strong long-term returns when evaluated through a CLV lens. With this insight, marketers can confidently shift budget toward the channels that bring in high-value customers. As competition in e-commerce continues to rise, this perspective offers a significant advantage for future growth.
How to estimate CLV (without a data warehouse)
Calculating exact margins and per-customer profits — essential for accurate CLV measurement — can be complex and resource-intensive. It often requires integrating data from backend systems and data warehouses, which is time-consuming and tedious.
As a practical first step, many marketers use a proxy: the total revenue a customer generates over a 12- to 24-month period. While not perfect, this approach is significantly easier to implement and offers a more meaningful perspective than short-term, single-transaction CPO-based attribution. In most cases, it provides a solid foundation for decision-making.
However, there are situations where this revenue-based proxy diverges significantly from the true CLV — for example, when customers who frequently buy or purchase high-priced products also tend to return items at a higher rate, incurring disproportionate costs. In such cases, it’s recommended to incorporate margin data from the data warehouse and calculate CLV based on actual profit, not just revenue.
Data-driven attribution modeling as the basis for CLV attribution
In marketing attribution — including when using data-driven models — the focus is often on assigning the value of a single conversion to the preceding marketing touchpoints. Typically, each touchpoint is only credited for one conversion or order.
Consider the following example: A customer makes their first purchase after seeing a display ad and then clicking a paid search ad on a generic keyword. About a week later, they return via a brand paid search ad, followed by a retargeting ad, and complete a second purchase.
Under a short-term, non-CLV attribution model, only the brand paid search and retargeting clicks would receive credit for the second conversion.
The simple CPO or ROAS perspective only includes the first order for calculating the effectiveness of the generic paid search ad and the display ad view.
However, the CLV-based approach applies data-driven attribution modeling to calculate how much of the value of the second order should be attributed to the display ad and the first paid search click. The attribution model should answer how much of the generic paid search click and the display ad view contributed to the second order.
This approach ensures early marketing touchpoints — those that play a crucial role in acquiring new customers — receive fair credit. Under a short-term, CPO-based model, these campaigns often appear unprofitable because they’re only credited with the value of the first conversion.
The goal of CLV-based attribution is to reflect the full long-term value of a customer. It distributes a portion of that value to the marketing interactions that occurred before the first conversion, using a data-driven attribution model. This enables more intelligent, efficient and future-focused marketing decisions.
To support this, a data-driven, machine learning-based attribution model must be capable of recognizing the complex interdependencies across touchpoints. Even short-term CPO attribution is challenging — building robust training datasets and properly sampling non-converting journeys is no small feat. But modeling a holistic customer journey that spans multiple conversions and channels — and teaching the model to identify and quantify relationships between those events — is even more demanding.
Proprietary customer data as a competitive advantage in biddable channels
By adopting a CLV-based attribution model, advertisers can prioritize Google Ads campaigns, keywords, and strategies that deliver the highest CLV-based Return on Ad Spend. This shift moves marketers beyond short-term metrics like CPO or ROAS and enables more intelligent bidding decisions — including the ability to outbid competitors on high-value, highly competitive keywords.
Using the example from the diagram above, an advertiser could significantly increase their bid for the generic paid search ad — not based on an attributed value of $90, but on a more accurate CLV-based value of $170. In highly competitive environments, where multiple advertisers are bidding on the same keywords, a CLV attribution model becomes especially valuable. It enables marketers to outbid competitors on high-intent keywords and acquire more high-value customers.
Leveraging proprietary customer data—such as total revenue or individual CLVs—empowers more precise and competitive budget allocation. As shown in the example, only advertisers who can track CLV and attribute it across all marketing campaigns can adjust their Google Ads bids accordingly and outperform their competitors.
This principle applies not just to Google Ads but to all biddable media channels — including Facebook, Instagram, Bing, most DSPs, Twitter, LinkedIn and retargeting platforms.
Funnel’s CLV attribution and Google Ads integration
Funnel’s attribution modeling is based on a holistic view of the customer journey, as shown in diagram 3. The holistic ROAS displayed in the Funnel dashboard allows for adjusting the budget allocation based on CLV attribution.
In addition to displaying these performance metrics in the Funnel dashboard, the CLVs attributed to Google Ads campaigns, based on Funnel’s attribution model, can be exported into Google Ads on a Google Click ID (Gclid) level. This allows for a direct bid adjustment in Google Ads on all possible levels, from keyword- to ad group- and campaign level, all based on CLV attribution.
Google Ads offers machine learning-based target ROAS bidding as part of the smart bidding strategies, which optimize for conversion value in each auction. This target ROAS bidding can work on the CLV attribution values imported into Google Ads from Funnel. This powerful combination enables fully automated, AI-driven CLV attribution and bid optimization.
CLV attribution requires cross-device tracking
Funnel integrates with mobile app tracking platforms (e.g., Adjust, AppsFlyer) and cross-device identity solutions (e.g., Tapad, Roqad). This enables user tracking across devices and accurately accounts for campaign performance, even across multi-device customer journeys.
As a result, CLVs can be assigned across the entire customer journey — including device switches — ensuring a more complete and accurate picture of performance. Without this capability, attribution becomes skewed.
For instance, imagine the second order in the earlier example happens on a device different from the one used for the initial touchpoints. Without cross-device tracking, those early marketing interactions wouldn’t be credited for their role in the conversion — reducing the model back to a simplistic, non-CLV attribution approach.
Final Thoughts
Short-term metrics like CPA or ROAS are easy to measure, but they don’t tell the whole story. If you’re serious about sustainable growth, CLV attribution isn’t optional — it’s essential.
By shifting focus from transactions to relationships, marketers can:
- Allocate budget more effectively
- Make smarter bidding decisions
- Gain a true competitive edge
Want to learn how CLV attribution can transform your marketing? Let’s talk!