Grow Smart: The ROI-Driven Marketing Playbook [Part 1]

Article #1

Introduction: The Fundamentals of Performance Marketing ROI

This is the first article in a five-part series about performance marketing ROI, authored by Karlon Group. The series is meant for professionals in the digital consumer space that want to better understand how to measure ad spend ROI.

If you’re a CEO, CFO, or CMO in e-commerce or consumer tech, chances are you spend a significant amount of money on digital marketing. After all, it’s typically your main driver of growth and the one you have the most control over. But it’s also complicated. You’re not sure if you’re spending the right amount in the right channels. You want to improve your capabilities but you’re not exactly sure where to start and what to prioritize. You recognize it’s a blend of art and science but you’re unsure how to strike the right balance.

You may be asking yourself:

• What is the best way to measure digital ad spend ROI given my data analytics stack today?

• What investments should I be making in analytics tools and data infrastructure to improve my measurement capabilities?

• What are the practical limitations I’m up against in measuring ad spend ROI? How scientific can I get?

• How can I overcome these limitations and what does the “end state” of a fully optimized performance marketing function look like?

Over the next several weeks, I’ll be releasing a series of articles that walk through the four phases of performance marketing in e-commerce or consumer tech. These articles will give you a practical roadmap for how to build and improve upon your performance marketing capabilities no matter the stage of your company. I’ll cover the concepts that are at the foundation of ad spend ROI, the analytics tools and data infrastructure you’ll need to build a world-class performance marketing function, and the inherent limitations of performance marketing. I’ll also incorporate real-world examples of how these capabilities translated into insights and cost savings from our friends at Hawke Media.

We look forward to getting your feedback along the way.

At Karlon Group, we help empower our clients to make “the right decisions at the right time” when it comes to finance. We also like to encourage a healthy dose of humility and caution in any digital marketing efforts.

We’re going to begin our journey by looking at the ideal future state of performance marketing. What does a world-class performance marketing function look like? What KPIs should you measure and how often? What data do you need to collect? What’s the formula for optimal ad spend ROI?

While we recognize these two industries are different in many ways, they share a common focus on end consumers and tend to rely on similar marketing channels.

Let’s start with a few important concepts

Let’s start with a few important concepts and talk about what that ideal future state should look like for each of them.

ROI
There are many ways to measure ROI, or Return on Investment, in performance marketing. Let’s start by thinking about the concept of ROI and what it really means. In performance marketing, the term ROI refers to the “return” you get, in terms of profit, on the “investment” you made, in terms of ad spend.

For a single customer, the “return” is the profit you generate on that customer’s purchase (or purchases), and the “investment” is the money you spent on ads that the customer consumed before making the purchase. That’s the simple definition of ROI in the context of performance marketing.
[We’ll get into some of the nuances in this article.]

Attribution
Attribution is the process of connecting each customer’s purchase back to the marketing channels or campaigns that influenced it. This helps companies understand how their ad spend drives revenue and profit. Attribution is challenging because customer journeys are rarely linear – people often encounter multiple ads across different platforms over days, weeks, or even months before converting.

For example, someone might click a Facebook ad, later Google the brand and click a search ad, and finally make a purchase after receiving an email promotion. Which channel gets the credit? That’s where attribution models come in.

Basic models like first-touch or last-touch give full credit to the first or last channel the customer interacted with. More advanced companies use multi-touch attribution, which distributes credit across multiple touchpoints.

For instance, if a customer saw a TikTok ad, then clicked a Google search ad, and finally purchased after visiting the website directly, a multi-touch model might assign 30 % of the credit to TikTok, 50 % to Google, and 20 % to direct traffic.

In practice, this means that if the customer made a $100 purchase, TikTok would be credited with $30 of revenue, Google with $50, and direct traffic with $20. These revenue “credits” help marketers calculate Return on Ad Spend (ROAS) by channel – for example, if $500 was spent on TikTok ads that collectively drove $1,500 in attributed revenue, the ROAS would be 3×.

We’ll get more into ROAS later in this series.

Incrementality

Attribution tells you which ads a customer interacted with. Incrementality asks a deeper question: Did the ad actually influence the purchase? In other words, would the customer have purchased anyway, even if they hadn’t seen the ad?

This concept is critical. If your ad spend isn’t driving incremental purchases, that spend isn’t driving profit, which means you’re probably not getting a good ad spend ROI. Incrementality measures the true lift that your ads generate — the additional purchases driven by your ad spend that wouldn’t have happened without it.

In a perfect world, you’d know exactly which customers were influenced by an ad and which were not. In reality, the only way to get close is by running holdout tests. These experiments split your audience into two groups: one group sees the ad (the test group), and the other doesn’t (the holdout group). You then compare the conversion rates between the two.

Let’s say you run a campaign and find that 5% of the test group made a purchase, while 3% of the holdout group purchased without seeing the ad. The incremental lift is 2 percentage points — meaning only 40% of the observed purchases in the test group (2 out of 5) were truly driven by the ad. That means only 40% of the associated revenue should be attributed to your ad spend when calculating ROI. The other 60% would have happened anyway.

These organic purchases — sales that happen without paid marketing — are a crucial part of any business. They can come from word of mouth, strong brand equity, or social proof (for example, seeing someone else use the product on social media).

Understanding how much of your business is organic versus driven by paid marketing helps you avoid over-investing in channels that aren’t delivering incremental value.

Sophisticated performance marketers pair attribution models with incrementality testing to avoid false signals. Just because a channel touches a lot of conversions doesn’t mean it’s driving them. Incrementality testing helps you separate correlation from causation — and spend more efficiently as a result.

Some ad platforms, like Meta and Google, offer their own incrementality estimates using internal models and experiments, such as Meta’s Conversion Lift tool or Google’s Geo Experiments. These tools use randomized control groups or geographic holdouts to simulate the effect of your ads. While helpful, these platform-provided results are often limited to that platform’s data and may not account for the full customer journey across multiple channels. That’s why many sophisticated marketers run their own incrementality tests or use third-party measurement tools to validate and cross-check platform-reported lift.

Purchase Cycle

How long does it take a customer to make a purchase decision? It depends on the product. Some products, like soda and chapstick, have short purchase cycles because they’re cheap, frequently used, and don’t require much research.

Other products, like furniture and laptops, have long purchase cycles because they’re expensive, long-lasting, and often involve comparison shopping. There’s a spectrum here and many products — digital and physical — fall somewhere in the middle.

Likewise, many products have different purchase cycles depending on the season, with shorter purchase cycles during highly promotional periods like Black Friday or Cyber Monday.

Understanding your product’s purchase cycle is essential for attribution. In a perfect world, you’d know exactly how long each customer takes to make a decision — and could map every ad they saw from first touch to final purchase. That way, you’d better understand how long your marketing efforts take to convert, and how to allocate spend accordingly.

Marginal ROI

It’s one thing to know the ROI of a campaign after you’ve run it — but what if you want to spend more (or less)? Will your return improve, stay flat, or decline?

This is where understanding marginal ROI is important. Marginal ROI looks at the return on the next dollar spent, not just the average return on all dollars spent. Without insight into marginal ROI, you’re flying blind when it comes to scaling your budget.

A campaign that looks profitable at $10,000 in spend may become less efficient at $50,000 — or vice versa.

In a perfect world, you’d know your ROI curve for each channel, showing how your marginal profit changes as you increase or decrease ad spend. This would help you allocate budget more intelligently, pulling back on channels with diminishing returns and doubling down on those with untapped potential.

Continue this article and look out for the rest of the series on the Karlon Group blog.

About the Author:

Karsten Loose is co-founder and Managing Partner at Karlon Group, a fractional finance and accounting firm that helps companies build, scale, and optimize their finance and accounting functions. Karlon Group works with companies across SaaS, consumer, manufacturing and technology, offering a full suite of finance and accounting support tailored to each client’s changing needs.