the oddly blog
The oddly blog
Marketing intelligence insights for growing brands. Google Ads, Meta Ads, Shopify, SEO, and the operational habits that compound.
Looking for a specific topic? Browse every guide, comparison, and case study by topic in the resource hub.
What oddly catches in its own ad spend: the recursive marketing test.
Three concrete catches from 90 days of oddly's own paid spend: a conversion tracking gap on /pricing, a retargeting overlap on the Operator audience, and a content gap.
Read moreWhy oddly runs oddly: running our own brands as the only credible product marketing.
Three of the largest marketing analytics platforms have founders who do not visibly run brands. oddly runs three: Burrow and Be, MOE, and oddly itself.
Read moreCohort benchmarking without the privacy creep: how oddly compares your ROAS to your category.
Most benchmark tools expose individual merchant data or hide aggregation in a black box. Hashed at insertion, salted weekly, cohort floor at 5.
Read moreThe hidden cost of duplicate-purpose ads: how Meta and Google end up paying for the same customer twice.
Meta retargeting and branded Google search overlap more than either platform tells you. The deduplication math, with real numbers, and how to fix it.
Read moreContent gaps your competitors are eating: a SG SMB diagnostic.
A baby apparel brand ranks position 12 for organic baby clothes Singapore: 487 impressions, 18 clicks. Moving to top 5 captures 5 to 8x the volume. Here is the diagnostic.
Read moreCross-channel reallocation: the move Triple Whale and Polar don't show you.
Within-channel tuning is table stakes. Moving spend from Meta to Google when the math says so is the unexplored lever. How the classification works.
Read moreThe 14-day rule: when to kill an underperforming ad set.
Killing ad sets at 3 days is too early. At 45 days it is too late. The 14-day rule is the operator's framework, with thresholds and an example.
Read moreWhy your Meta ROAS is lying to you (and how to spot it in 5 minutes).
Meta-reported ROAS is typically inflated 20 to 40% over verified revenue. Here is why the gap exists and a five-minute audit to find your real number.
Read moreWhat we learned managing 4 Shopify brands solo for 3 years.
Running multiple Shopify brands as a solo operator surfaces problems that single-brand operators never see. Six lessons from three years of doing it badly.
Read moreWhy Marketing Ops is the missing category in your Shopify stack.
Marketing Ops sits between your ad platforms and your analytics tools. It is a real product category and your Shopify stack does not have one yet.
Read moreTrue ROAS vs reported ROAS: the conversion tracking pixel lie.
Reported ROAS in your ad platform is almost always wrong. Here is why, and how to calculate the version that actually matters for your business.
Read moreShopify merchants waste 18% of their ad budget on out-of-stock products. Here's the data.
Out-of-stock products still running ads is the single largest source of paid-spend leakage for Shopify merchants. We measured it across our portfolio.
Read moreThe Hidden Cost of Not Monitoring Your Google Ads Search Terms
15 to 30% of your Google Ads search traffic comes from irrelevant queries. Here's how to find the waste and stop it before it compounds.
Read moreBest Marketing Intelligence Tools for Shopify Stores in 2026
Triple Whale, Northbeam, Lifesight, Kepla, oddly, and Peel compared. Which marketing intelligence tool fits your Shopify store in 2026.
Read moreWhy Your Google Ads Conversion Tracking Is Probably Broken (And How to Check)
Five common ways Google Ads conversion tracking breaks for Shopify stores, plus a 10-minute diagnostic you can run right now.
Read moreHow to Stop Wasting Ad Spend on Out-of-Stock Products
Out-of-stock products quietly burn Google and Meta Ads budget on clicks that cannot convert. How much it costs and how to stop the bleed.
Read moreoddly vs Kepla: Which Marketing Intelligence Platform Is Right for Your Business?
oddly and Kepla solve different problems. Side-by-side comparison of features, pricing models, and where each platform fits.
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