Understanding Ecommerce KPIs

Is ROAS the right metric for RMNs? Retail Media Networks (RMNs) have outgrown their early days when untapped demand meant every dollar spent was both high-ROAS and high-incrementality. Today, focusing solely on ROAS incentivizes behaviors that may appear efficient but harm long-term profitability and growth. Here’s how ROAS can be gamed—and why it’s problematic: 1️⃣ Over-spending on Retargeting or Brand Keywords. These tactics drive high ROAS but focus on customers who were likely to convert anyway, resulting in low incremental growth. 2️⃣ Discount-Driven Sales. Discounting boosts ROAS by generating short-term revenue but lowers margins, attracts low-LTV customers, and conditions buyers to expect promotions. 3️⃣ Cutting Spend on High-Incrementality Campaigns. Investing in new customer acquisition or brand building may have lower ROAS but drives long-term growth and quality customer cohorts. These behaviors lead to: ⛔️ Shrinking new customer cohorts. ⛔️ Increased reliance on discounts, reducing margins. ⛔️ Lower customer lifetime value (LTV) and diminished profitability over time. In essence, chasing ROAS at all costs leads to slower growth and declining margins—a losing combination for any business. Efficiency metrics like ROAS are necessary but must be balanced with an effectiveness metric that focuses on long-term outcomes. For example: ✅ 180-Day Contribution LTV: Measure the total revenue contribution from full-price customers acquired over six months. ✅ Incremental Revenue from Non-Brand Keywords: Track revenue generated from truly new demand sources. ROAS is an excellent efficiency metric but a poor north star. Striking the right balance between efficiency and effectiveness will ensure your business scales sustainably while maintaining margins. Keen to hear what other metrics are used for RMNs #advertising #media #tech

On the 10 year journey to Chubbies’ IPO, the realization that changed how we invest marketing resources was this --> Increasing ROAS * decreased * our growth. btw, I was the world’s largest ROAS (AKA Return on Ad Spend) fanboy for embarrassingly too long, but hey, my loss is your gain, so here's: 1. Three counterintuitive things I learned about ROAS 2. Two new ways to think about it 3. Three things you can do about this right now let's do it. ** Three counterintuitive things I learned about ROAS ** 1. “ROAS has been presented as a growth metric, when it’s actually anything but. In fact, ROAS is precision-engineered to keep brands small,” says Tom Roach. Chasing ROAS chases easy sales, not growth. Brand growth comes from light buyers, but focusing on high ROAS can lead to you targeting heavy buyers, therefore limiting growth. 2. ROAS is not actually a measure of *effectiveness* but how *efficiently* you achieved it. As Les Binet says: “Effectiveness first, efficiency second.” 3. Simply put, ROAS is the opposite of incrementality. ** Two new ways to think about it ** 1. It's like hiring an employee to stand just inside the entrance of your shop and tap shoppers on the back as they enter. A week later, the employee demand a raise, claiming credit for all the customers they’ve “enticed” to come in. 2. Imagine a soccer coach believing their forward is entirely responsible for every goal. As a result, in their infinite wisdom, they ditch their defense and midfield, only keeping their center forward. They end up losing every future game, but their “Goals Per Player” (the ROAS of this example) is higher than ever! ** Three things you can do about it right now ** 1. Vanity VS Value: Understand the negative externalities of the metrics we goal our teams on. For example, because many of us are seeing headwinds, brands either cut marketing spend or increase the ‘accountability’ of the dollars spent. The negative externality is that we're over-harvesting our existing customers in order to hit our numbers. ROAS and revenue from returning customers may be up (vanity metrics), but contribution dollars, share of search, and new customer revenue from unpaid sources (real business metrics) are likely down. 2. Party & Ponder: Spend half a day with your team and deeply consider the metrics you want to optimize your team’s efforts around in 2024. The whole team needs to take ownership of the metrics that matter AND have a deep understanding of the negative externalities of vanity metrics like ROAS. This is a super high-leverage use of time 3. Cultivate Creativity Completely (the 3C's of winning): Since marketing works by influencing future buyers, think about developing creative that gets noticed and gets remembered. Give your team permission to be bold, put on a show and have a little fun. As John Dawes of the Ehrenberg-Bass Institute says, “The brand that gets remembered is the brand that gets bought." Enjoy

Marketing dashboard showed 4.2X ROAS. Finance team said we were losing money. Both were right. Spent three days digging through every transaction and found the hidden culprit: Our top-performing ad segment had a 72% return rate. Most marketers track acquisition metrics obsessively while completely overlooking what happens after purchase. The true journey: ⤵︎ Customers saw our high-quality images ⤵︎ They impulsively purchased multiple sizes ⤵︎ Kept one item, returned two others ⤵︎ Return processing cost us ₹420 per order Five immediate changes we made: ↗︎ Added detailed size guides with actual measurements ↗︎ Modified creative to show realistic fits on diverse body types ↗︎ Built separate campaigns for repeat customers ↗︎ Integrated CRM data with ad targeting to block serial returners ↗︎ Reworked attribution to account for net revenue after returns Our new dashboard shows actual profit per ad campaign. The first week was sobering - half our "winning" campaigns were actually money losers. But now? Our decisions are based on reality, not wishful thinking. What post-purchase metrics are you ignoring?

ROAS = Really Over-estimating Ad Success Here's how to fix it: It doesn't matter how much return your ads make if the sales were going to happen anyway. You need to look at the incremental return on ad spend (iROAS). The 'i' really matters. It means you're trying to measure sales caused by advertising, (using test <> control methodology) rather than just counting sales regardless of causality. Given how much is spent in performance this is probably the single biggest step the advertising industry can take in 2025 to improve effectiveness. Analytic Partners found 35 cents from every $1 spent on advertising is wasted due to optimizing exclusively to last click metrics like ROAS. That wastage is advertisers paying for existing sales. When you're next shown ROAS results ask where's the 'i'? iROAS is just the start. Consider the full picture: - What's the value of incremental sales you're driving? - What's the cost factoring non-media costs (creative production etc)? - What's the cost of goods sold (production, returns etc)? - How much incremental profit are you driving? - What's the long term incremental impact? Incrementality should be non-negotiable in 2025.

I wasted $50,000 on ads before learning this one thing Two years into running paid ads for my first brand. $50,000 spent. Revenue was flat. I blamed the platforms. The real problem took me six months to see. I was measuring ROAS. I wasn't measuring profit. At a 3.2x ROAS with 22% gross margins and $8 average shipping cost, I was losing money on every order and scaling the loss. The math no one showed me: Revenue: $320 COGS: $250 Shipping: $8 Ad spend: $100 Net: -$38 per order I thought 3.2x was strong. It was a slow bleed. What I track now before touching any budget: → Gross margin per product → Contribution margin after all variable costs → Break-even ROAS (hint: it's almost never 2x) Break-even ROAS = 1 / gross margin % At 30% margins, you need 3.3x just to cover product costs. Add shipping, fees, and returns — your real floor is higher. Know your number before you scale anything. #PerformanceMarketing #PaidAds #D2CMarketing #GrowthMarketing #AdStrategy

Incrementality (and your ROAS) decreases as you scale. Ever wonder why pumping 10x more into Facebook ads might only get you 5x more customers? It's not a glitch. It's not a platform issue. It's just mathematics working exactly as designed. Marketing follows a predictable diminishing returns curve that many don't account for. Every marketing channel follows an S-shaped response curve with three distinct phases: - First dollars capture the low-hanging fruit, customers ready to convert - Mid-level spending shows strong but linear returns - Higher spending inevitably hits diminishing returns Ad platforms amplify this curve. They're algo's optimize to show ads to the most likely converters first. --- 𝗛𝗲𝗿𝗲'𝘀 𝗮 𝘀𝗲𝗮𝗿𝗰𝗵 𝗲𝘅𝗮𝗺𝗽𝗹𝗲. 1. Your first $10K captures people literally searching for your exact product name. You get a $50k return ROAS = $50k / $10k = 5.0x 2. Your next $40K targets adjacent terms You get an additional $160k return ROAS = ($160k + $50k) / ($40k + $10k) = 4.2x 3. Your final $50K goes to broad match terms where intent is minimal. You get an additional $100k return ROAS = ($100k +$160k + $50k) / ($50k + $40k + $10k) = 3.1x --- 𝗠𝗮𝗿𝗴𝗶𝗻𝗮𝗹 𝗥𝗢𝗜 𝗶𝘀 𝘄𝗵𝗮𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀. Let's look at the example above. - First $10K: 5.0x marginal ROAS - Next $40K: 4.0x marginal ROAS - Next $50K: 2.0x marginal ROAS If you're scaling, you should be looking at what the "next dollar" will bring you. Brands commonly measure average performance, but use that metric to make marginal spending decisions. "We're getting 3.1x ROAS, let's double down!" without realizing their next dollar might only return 2.0x. I've seen CMOs nearly lose their jobs when $5M budgets produced worse efficiency than $1M budgets. Nothing is broken, they just failed to understand (or communicate) response curves. You shouldn't avoid growth. Instead focus on where your next dollar works hardest. - Map incremental returns across spending tiers for each channel. You can also use geo-tests to test the spend increases in a smaller population. - Redistribute budget based on marginal performance, not average. - Open new channels before pushing existing ones past efficiency thresholds. - Stop viewing "declining efficiency" as a failure. It's just math working as designed. Are you measuring your marginal channel ROAS? #incrementality #marginalroas #measurement

I spent over $3M on Meta ads in January. And I use 3 attribution models: Ad platforms are notorious for taking credit for view-through conversions they didn't drive. They do it to bait you into spending more. The issue is that your top 1-2% of ads should drive ~50% of your spend and revenue. If you're relying on bad attribution, you won’t be able to find them. This is why 8-9 figure brands (that NEED their tracking to be faultless), use 3 attribution models: 1. Multi-touch attribution (MTA) - for ad and campaign level optimization. This is your Triple Whale. Great for knowing which ads are performing best, which ones to scale, which to cut. Not as good for comparing channel to channel. It also will overcount total revenue, which you need to be careful about. To make sure your account is well optimized, plot CPA vs Spend on a scatter plot. The top ads should be in the low CPA, high spend zone. 2. Post-purchase survey - for channel level allocation. Get a 35%+ response rate, extrapolate to all new customers, and calculate your cost per new customer response per channel. This tells you which channel to push into. Click-based attribution overvalues lower-funnel performance by up to 250%. Post-purchase surveys catch what click attribution misses - top-of-funnel creative can drive 13X more incremental acquisitions than bottom-of-funnel. 3. Marketing Mix Model (MMM) - for validating direction. You can't use this daily, but it confirms your post-purchase survey is sending you the right way. Then you use post-purchase on a daily basis to optimize channel allocation. Some channels drive low-quality customers that look good on ROAS but don't stick around. MMM helps you optimize for 12-month profit as opposed to just immediate return. The other thing to know is that view-through attribution is poor signal. Make sure your attribution is set up for 7 or 14 day click, depending on your purchase funnel. One day view will overcount. Here's what this gives you: When performance drops, you know exactly where to pull budget to create the smallest impact on revenue while keeping the company profitable. When things are going well, you know exactly where to push budget to scale effectively. Bottom line: -> Use MTA for ads and campaigns.  -> Use post-purchase surveys for channel allocation.  -> Use MMM to validate you're heading the right direction. This is how 8-9 figure brands figure out where every dollar should go.

At my first ad tech job, I saw something that still haunts me: vendors celebrating record ROAS … while marketers watched customer growth flatline. The disconnect? Incrementality - understanding if your marketing actually changed customer behavior. Here's what I mean: Say you spend $1M on ads and track $5M in sales from customers who saw them. Impressive 5x ROAS, right? But what if $4M of those sales would have happened anyway? Your real return isn't 5x - it's 1x. And you might be optimizing toward the wrong channels. This is why brands are shifting to incrementality testing. By comparing exposed vs. unexposed audiences, you can: - Identify which channels actually drive NEW customers (vs. just claiming credit for existing ones) - Spot when high ROAS signals you're over-targeting existing buyers - Make smarter budget allocation decisions based on true incremental impact This transformation is happening right now.  One retail client discovered their highest ROAS channel was cannibalizing organic sales. So they shifted their budget and drove 40% more new customers *while spending the same amount.* With marketing budgets under more scrutiny than ever, we can't afford to chase the wrong metrics. The real question isn't "what happened after my ad?" but "what happened because of my ad?"

After 10+ years of analyzing marketing performance data, I've noticed a (very!) common optimization pitfall. Teams focus solely on Cost Per Acquisition (CPA) while missing the bigger revenue (ROAS) picture. 𝐖𝐡𝐲 𝐂𝐏𝐀 𝐈𝐬𝐧'𝐭 𝐄𝐧𝐨𝐮𝐠𝐡 👉 Different user segments show varying behaviors post-conversion (retention rates, seats per account, cancellation patterns, upselling potential) 👉 Low CPA campaigns might actually generate less revenue than higher CPA initiatives with better ARPU 👉 Subscription products have multiple revenue-generating actions beyond initial conversion 𝐓𝐡𝐞 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞 𝐰𝐢𝐭𝐡 𝐓𝐫𝐚𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥 𝐑𝐎𝐀𝐒 𝐓𝐫𝐚𝐜𝐤𝐢𝐧𝐠 👉 Multiple revenue events (renewals, plan changes, seat additions) can't be cleanly attributed to original campaigns 👉 Attribution windows often misassign later revenue events to organic or CRM campaigns 👉 Conversion events alone don't capture the full revenue story 𝐓𝐡𝐞 4-𝐒𝐭𝐞𝐩 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐀𝐜𝐜𝐮𝐫𝐚𝐭𝐞 𝐑𝐎𝐀𝐒 𝐌𝐞𝐚𝐬𝐮𝐫𝐞𝐦𝐞𝐧𝐭 1️⃣ 𝘚𝘵𝘰𝘳𝘦 𝘈𝘥 𝘗𝘭𝘢𝘵𝘧𝘰𝘳𝘮 𝘚𝘱𝘦𝘯𝘥 - Implement ETL tools (Fivetran, Funnel etc) to store spend data - Create unified view across platforms with daily campaign-level granularity 2️⃣ 𝘊𝘢𝘭𝘤𝘶𝘭𝘢𝘵𝘦 𝘙𝘦𝘷𝘦𝘯𝘶𝘦 𝘗𝘦𝘳 𝘜𝘴𝘦𝘳 - Aggregate all revenue events (subscriptions, renewals, upgrades) - Create comprehensive user lifetime value view - Store in same warehouse as ad spend data 3️⃣ 𝘛𝘳𝘢𝘤𝘬 𝘐𝘯𝘪𝘵𝘪𝘢𝘭 𝘊𝘰𝘯𝘷𝘦𝘳𝘴𝘪𝘰𝘯 - Ensure conversion events link to single touchpoint - Maintain consistent unique identifiers (user_id, campaign_id) - Connect conversion data to revenue tracking 4️⃣ 𝘑𝘰𝘪𝘯 𝘋𝘢𝘵𝘢 𝘚𝘦𝘵𝘴 𝘧𝘰𝘳 𝘈𝘯𝘢𝘭𝘺𝘴𝘪𝘴 - Combine spend, revenue, and conversion data - Create segmented views by market, strategy, audience, keyword - Enable granular ROAS calculation per campaign With this as basis, you can calculate granular ROAS and payback period for your individual campaigns, ads or keywords. Full detailed guide with implementation steps in comments.

I wasn’t going to write this post. But after seeing another brand celebrate a 300% ROAS improvement while missing its new revenue target by 20% I realized something: This isn’t a data problem. It’s a strategy problem. Here’s the core issue: Most companies don’t know how to translate a business goal into a marketing goal. And because of that, marketing teams hit all their platform metrics (because they know how to) but still miss the company’s bottom line. Let’s break this down. Your CEO says: “Our goal this quarter is $10 million in new revenue.” Marketing replies: “We have $4 million in ad budget. So our MER goal is 2.5.” So far, so good. But then marketing leadership says: “To hit that MER, we need a 3x ROAS on Google and a 2x on Meta.” And that’s where everything starts to fall apart. Because those ROAS targets are based on reported revenue, not incremental revenue. And unless you’ve measured how incremental each channel truly is your ROAS goals are completely unanchored from reality. Here’s what most teams miss: Not all revenue is created equal. If $6 million of that $10 million would have happened anyway and your ads only drove $4 million in causal lift then your spend wasn’t as efficient as you think. That’s why CAC and ROAS can look amazing while your business still underperforms. The solution isn’t more data or new creatives or a new agency. It’s better modeling and strategy. You need to: • Run controlled holdout tests • Measure iROAS (not just ROAS) • Calculate each channel’s incrementality factor • Then translate your MER goal into platform-specific ROAS goals The formula is simple: Target ROAS [should] = MER ÷ Incrementality Factor Here’s the part that’s hard to say out loud: Many marketing teams don’t do this because they don’t know how and they are scared to admit it or they’re afraid of what they’ll find. Some know their spend is inefficient but keep pushing dollars into channels anyway because if they don’t spend the budget this quarter they might lose it next year. That might work for a while but eventually, your results stop aligning with reality. Finance notices. Budgets get cut. Then your job might be on the line. This is not about blame or pointing fingers. (I literally see this all the time. CFO's I'm looking at you.) This is about fixing the system that sets marketing teams up to fail. Marketing metrics must be designed in service of business goals. That doesn’t happen by default. It happens when incrementality measurement is the foundation not an afterthought.