Evaluating the Effectiveness of Ecommerce Ads

Incrementality testing is crucial for evaluating the effectiveness of marketing campaigns because it helps marketers determine the true impact of their efforts. Without this testing, it's difficult to know whether observed changes in user behavior or sales were actually caused by the marketing campaign or if they would have occurred naturally. By measuring incrementality, marketers can attribute changes in key metrics directly to their campaign actions and optimize future strategies based on concrete data. In this blog written by the data scientist team from Expedia Group, a detailed guide is shared on how to measure marketing campaign incrementality through geo-testing. Geo-testing allows marketers to split regions into control and treatment groups to observe the true impact of a campaign. The guide breaks the process down into three main stages: - The first stage is pre-testing, where the team determines the appropriate geographical granularity—whether to use states, Designated Market Areas (DMAs), or zip codes. They then strategically select a subset of available regions and assign them to control and treatment groups. It's crucial to validate these selections using statistical tests to ensure that the regions are comparable and the split is sound. - The second stage is the test itself, where the marketing intervention is applied to the treatment group. During this phase, the team must closely monitor business performance, collect data, and address any issues that may arise.  - The third stage is post-test analysis. Rather than immediately measuring the campaign's lift, the team recommends waiting for a "cooldown" period to capture any delayed effects. This waiting period also allows for control and treatment groups to converge again, confirming that the campaign's impact has ended and ensuring the model hasn’t decayed. This structure helps calculate Incremental Return on Advertising spending, answering questions like “How do we measure the sales directly driven by our marketing efforts?” and “Where should we allocate future marketing spend?” The blog serves as a valuable reference for those looking for more technical insights, including software tools used in this process. #datascience #marketing #measurement #incrementality #analysis #experimentation – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gWKzX8X2 

I’ve spent $15M+ on Meta Ads for ecommerce brands. Here’s the hard truth: Most brands fixate on fixing targeting… while ignoring the REAL leak in their funnel. After analyzing 37 accounts, I noticed a pattern: → Ads with great CTR often had terrible ROAS. → “Optimized” campaigns kept attracting bargain hunters. → Scaling spend made performance worse, not better. The invisible problem? You’re accidentally teaching Meta’s algorithm to find clickers, not buyers. Let me explain: 1. Algorithm Misdirection What’s happening: Optimizing for clicks/conversions (vs purchases) tells Meta to target action-takers, not spenders. Result: Cheap traffic that never buys. 2. Creative Mismatch What’s happening: Urgent discounts (“70% OFF!”) attract deal-seekers. Result: 2x CTR… and 0.5x ROAS. 3. Conversion Friction What’s happening: Landing pages cater to your creative’s audience (price-sensitive visitors). Result: Mismatched messaging kills trust. Meta gives you what you signal you want, not what you say you want. Last month, a skincare brand cut their CPA by 62% overnight. How? → Switched campaign objective from “Conversions” to “Purchase”. → Tested non-discount hooks (“Experts agree this serum…”). → Added a “Why invest in quality skincare?” section to their product page. Want the full playbook? I break down these exact strategies in my free course: https://www.fixmetaads.com Do this today: 1. Audit campaigns optimized for anything except purchases. 2. Match your ad hooks to your highest-value customer’s mindset. 3. Add a “premium” section to your landing page, even if you sell budget products. Your targeting isn’t broken, you’re just fishing with the wrong bait. Ever scaled an ad campaign that suddenly tanked? What tipped you off? 👇 P.S. If you want to dive deeper, I share my full Meta Ads audit process (with templates) in my Meta Mastery Blueprint Course. No fluff, just what actually works.

Why was this brand paying 42% more for customers they already had in reach? When I audited their account, the founder assumed pricing was the issue. CAC was climbing, margins were thin and they were ready to test discounts. But the numbers told a different story. The problem wasn’t price, it was structure. Here’s what we fixed: 1. Audience re-segmentation Instead of running broad cold, warm and customer buckets, we broke them into intent-based layers. Warm traffic was divided into “cart abandoners,” “repeat site visitors,” and “social engagers.” Each group got ads tailored to its stage of readiness instead of generic messaging. 2. Funnel sequencing Previously, retargeting was hitting cold leads too early, wasting spend on people who weren’t ready. We re-mapped the sequence: cold campaigns to spark awareness, mid-funnel ads to build education and trust and retargeting focused solely on proof and urgency for high-intent visitors. 3. Creative alignment All their ads looked the same, polished product features. We rebuilt the creative to fit funnel stages: problem/solution ads for cold traffic, story-driven testimonials for mid-funnel and offer reinforcement for retargeting. This way, buyers saw a journey, not repetition. The impact? → CAC dropped 42% in 60 days. → Average revenue per customer stayed intact. → Profitability grew without touching price or product. The real win wasn’t “better ads.” It was creating a system where every stage of the funnel worked together. ↪ Running e-commerce ads but still seeing CAC creep higher (even when top-line ROAS looks fine)? ↪ I’m offering a quick 15-minute funnel audit (link in comments) to uncover the 1–2 misalignments inflating your CAC and show you how to fix them.

Every VP of Ecommerce should study how Orolay unlocked a 13.5% purchase rate boost using AMC. (You’ll want to bookmark this!) BACKGROUND: Orolay had some clear goals: - Understand their customer journey across multiple devices and platforms. - Pinpoint how ad exposure timing impacted conversions. - Reallocate budgets to maximize ROI during peak seasons. So they leveraged AMC and Xmars to do a deep analysis and take strategic action. 3 PART AMC ANALYSIS: (1) Leveraged AMC to analyze SP and DSP campaigns, focusing on customer consideration and conversion stages across multiple devices and inventory sources. (2) Used Xmars’ proprietary modeling to identify key touchpoints, interaction sequences, and media combinations driving conversions, revealing that multiple touchpoints increased purchase likelihood by 23x. (3) Studied the timing of ad exposures, finding a significant drop in performance after 9 p.m. and correlations between exposure times and conversion rates. 4 ACTIONS TAKEN: (1) Deployed a strategy combining DSP and SP to maximize customer touchpoints and drive higher conversion rates. (2) Recalibrated the funnel to emphasize upper-funnel tactics early in the customer journey, followed by SP ads, increasing purchase rates by 4x. (3) Adjusted ad schedules to focus on peak performance times, avoiding periods of low efficiency like after 9 p.m. (4) Shifted 16% of the budget from SP to DSP during the 2022 peak season to improve media efficiency and ROI. THE RESULTS: - 13.5% boost in purchase rates - 7% increase in new-to-brand purchases - 300% better Deal of the Day cost-per-view performance compared to the previous year For anyone working on better utilizing AMC, this is a great playbook to run. Props to Orolay and the team for showing what’s possible with AMC. #Ecommerce #Amazon #AMC #Xmars

Here’s how a customer we work withincreased ROAS 99% with a data-led approach And how you can do the same for your brand by cutting fluff & focusing on the metrics that move the needle. These are the exact 5 steps they used: ↳ Track the right metrics They used PenPath’s Purchase Intent Rate (PIR) dashboard as a guiding metric. Instead of relying solely on ROAS or CVR, they analyzed customer buying signals: - Adding to cart - Begin Checkout - Site searches - Email signups ↳ Clean up campaign data Set up clean campaign naming conventions to make data analysis easy & actionable. Specifically making things segmented by prospecting, retargeting, and by product category. ↳ Optimize by funnel stage Measured PIR by source, medium, and campaign to understand baselines for each stage of the funnel to measure interest for each traffic source and by product categories. ↳ Focus on what’s working For TOF effort with high PIR, they scaled or kept them even when ROAS was not performing and cut the rest. For BOF, they cut any campaign with low ROAS or PIR. This is an over simplification but that was the general approach. ↳ Scale high-intent audiences Lastly, they used purchase intent data to created improved retargeting audiences on Google and Meta. The Results? ✅️ ROAS skyrocketed from 1.35x to 2.69x (+99.555) in three months ✅️ Ad spend increased by 243% --- with no wasted dollars Pro Tip: Map your customer journey with intent-driven metrics. Focus on actions that align with each stage of your funnel (TOF, MOF, BOF) to uncover where customers drop off—and where to double down on winning strategies. If you’re an ecommerce decision maker, what data have you used to scale ROAS as quickly as possible? #Dataanalysis #Ecommercetips #Adspend #Ecommercesolutions