Quartile

• What it is: AI retail-media ad automation across Amazon, Walmart, Instacart, Criteo, and more.
• The tech: six patented ML models doing hourly bid adjustments — not daily like most rivals.
• Scale: 5,300+ customers, $2B+ managed annual ad spend, 32+ countries.
• Pricing: quote-based (custom / contact sales) — scales with ad spend and channels.
• Best for: Amazon brands spending $20K+/mo and omnichannel retailers.

How Quartile's AI actually runs your ads

The core idea is simple to state and hard to execute: retail-media auctions move faster than any human bid manager can react to. Quartile's answer is to put six patented machine-learning models on the problem and let them adjust bids hourly across every channel at once, then layer reporting and listing recommendations on top so the optimisation compounds rather than churning the same keywords.

1. Connect your channels

   Link Amazon Sponsored Ads and DSP, plus Walmart Connect, Instacart, Criteo, Google, Meta, and Microsoft — whichever you run — into one platform.

2. Let the models learn

   The ML ingests campaign history. Expect a ramp of a few weeks while it learns your catalog, margins, and conversion patterns before it hits peak ROAS.

3. Bid hourly, per SKU

   Bids and placements adjust hourly (vs daily on most rivals), with per-ASIN / per-SKU granularity so mixed-margin catalogs are optimized individually.

4. Automate the levers

   Dayparting, geo-targeting, and budget pacing run automatically, plus listing-optimization recommendations to lift the conversion side of the equation.

5. Report across channels

   One dashboard rolls up cross-channel ROAS, so you stop reconciling three separate ad consoles by hand.

It is worth being concrete about why hourly, ML-driven bidding beats the manual alternative, because "AI optimization" is a phrase every ad tool now uses. Retail-media auctions — especially on Amazon — are second-price auctions running continuously, with demand and competitor bids shifting throughout the day. A human bid manager, even a great one, reviews campaigns daily at best and adjusts a few hundred keywords by gut and last-week's report. The opportunity cost of that latency is invisible but constant: bids left too high during low-conversion hours, bids left too low during the windows when your shoppers actually buy. Quartile's models close that gap by re-evaluating placements and bids hourly, per SKU, against live performance data. Multiply that across a catalog of hundreds of ASINs and several marketplaces and the difference is no longer a rounding error — it is the ~41% average ROAS lift the case studies report.

The omnichannel coverage is the second pillar, and it matters more every year as retail media fragments. A brand that sells on Amazon increasingly also sells on Walmart Connect, runs Instacart ads for grocery SKUs, and buys Criteo and Google to capture demand off-platform. Run those as four separate consoles and you get four sets of bidding rules, four reporting formats, and no unified view of where a marginal dollar earns the most. Quartile pulls Amazon, Walmart, Instacart, Criteo, Google, Meta, and Microsoft into one platform with consolidated ROAS reporting — so optimisation decisions are made across the whole portfolio rather than within seven silos. Most rivals are Amazon-only; that breadth is genuinely Quartile's differentiator, not a checkbox.

What's under the hood

Quartile vs Helium 10 vs Perpetua

Three tools get compared here, but they are not the same category. Helium 10 is a broad Amazon-seller toolkit whose PPC module is a fraction of what Quartile does. Perpetua is the more direct competitor — strong on Amazon — but Quartile's patented multi-channel ML and omnichannel coverage are its differentiation. The practical way to read the table below: if your need is product research, keyword discovery, and lighter campaign management on Amazon, Helium 10 is excellent and far cheaper. If you are an Amazon-focused ad team wanting goal-based automation, Perpetua is a strong, more directly comparable choice. Quartile earns its premium when the problem is bigger than Amazon alone — when you are optimising serious spend across several retail-media networks at once and want one set of ML models and one reporting surface doing it.

Who Quartile is for

Setting expectations: the ramp and the fit

Two things temper the pitch, and both are about fit rather than flaws. First, the ML needs data to work. The models perform best after they have a few weeks of campaign history to learn your catalog, margins, and conversion patterns from — so the first 60–90 days are a ramp, not an instant switch-on. Brands that expect a step-change in week one will be disappointed; brands that give the system a quarter to learn are the ones whose case studies show the headline gains. Plan the onboarding window into your expectations and your internal reporting.

Second, Quartile is built for volume, and that shapes who should bother. There is no self-serve signup and no published rate card — pricing is quote-based and scales with managed ad spend and the channels you activate, set on a demo call after scoping your account. A single-product seller spending a few hundred dollars a month is simply not the target customer and will get more from a lighter, cheaper PPC tool. The brands that see the clearest payoff are spending $20K+/mo on Amazon ads, selling across multiple marketplaces, or running an agency managing several seller accounts where consolidated reporting and consistent optimisation across clients is itself the win. If that describes you, the quote-based model is a feature, not a barrier — it means pricing is matched to the spend the platform is optimising.