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Gapstow

Platform guide — Marketing & Advertising

Automation optimizes toward the goal you give it.

Bid automation is very good at moving spend toward whatever metric it's told to maximize. If that metric is last-click ROAS and your margins vary by product, it will optimize you into unprofitable growth with excellent-looking reports.

Quartile operates in the paid media optimization layer, across search, social and retail media channels.

Explore Quartile

Partner disclosure: Gapstow may receive referral compensation if you sign up or purchase through links on this page. This does not change what you pay or how we evaluate the platform.

What it does

Quartile in one paragraph

Quartile applies machine learning to campaign structure, bidding and product-level targeting across channels including Google, Meta and Amazon, using product and performance data to allocate spend.

It sits alongside, not instead of, media strategy: the platform decides allocation within the rules and objectives it's given.

Notable capabilities

  • Cross-channel allocation

    Spend decisions made with visibility across channels rather than channel by channel in isolation.

  • Product-level optimization

    Bidding informed by individual SKU performance, which matters most with large or uneven catalogs.

  • Retail media coverage

    Marketplace advertising handled in the same system as search and social, relevant for brands with meaningful Amazon revenue.

  • Automated campaign structure

    Structure maintenance that would otherwise consume a specialist's week.

Where it fits

Every platform decision is a decision about who owns a stage.

Where Quartile sits across the eCommerce stack
  1. Storefront
  2. CustomerQuartile
  3. CRM & Retention
  4. Operations
  5. Fulfillment
  6. ReportingQuartile

Paid media sits before the storefront in the customer journey and after it in the data flow — it depends entirely on the quality of the product feed and conversion signal you send it.

Commonly touches

  • Product feed
  • Shopify
  • Analytics
  • Marketplaces
  • Finance and margin data

Judgment

Two lists, and the second one matters more.

When we’d look at it

  • Spend is large enough that a percentage point of efficiency is meaningful money.
  • Catalog size makes manual product-level management impractical.
  • You advertise across search, social and marketplaces and allocate between them by instinct.
  • Campaign management is consuming disproportionate internal or agency hours.
  • Product feed and conversion tracking are already reliable.

When we’d question it

  • Spend is modest. Automation overhead and platform cost need volume to justify themselves.
  • Conversion tracking is unreliable. Automation amplifies whatever signal it receives, including a wrong one.
  • Margin varies widely by product and you can't feed margin data in. Blended ROAS targets will quietly favor your worst products.
  • The real problem is creative, offer or landing experience. No bidding system fixes a weak proposition.
  • You're already at efficient scale with a capable team and the marginal gain doesn't cover the fee.

Before you implement

Questions to answer first

  1. 01

    What objective are we optimizing — revenue, blended ROAS, contribution margin, or new-customer acquisition?

  2. 02

    Is product-level margin available to the system, or are we optimizing revenue as a proxy?

  3. 03

    How reliable is our conversion tracking, and how is it affected by consent choices?

  4. 04

    Who owns creative and offer strategy while the platform owns allocation?

  5. 05

    How is incrementality assessed versus platform-reported attribution?

  6. 06

    What is the exit path — do we retain campaign structures and learnings if we leave?

Implementation

What tends to go wrong

  • Feed quality determines ceiling. Titles, attributes and availability accuracy do more for performance than bidding sophistication.
  • Consent-mode and tracking gaps change the data the model learns from; validate measurement before judging results.
  • Agree a learning period and a review checkpoint up front so the evaluation isn't a debate about timing.
  • Retail media and DTC have different economics. Blending them into one target obscures both.

Paid media automation is a leverage tool, and leverage works in both directions. Get the feed, the conversion signal and the margin picture right first — then automation is compounding rather than merely fast.

Vendor material

The software company publishes its own customer stories. We include one here because it’s a useful data point, clearly attributed and summarized rather than reproduced. It is not Gapstow work and we make no claim about the engagement.

Partner / vendor case study — published by Quartile, not Gapstow work

Discount Tackle's search and social performance

Quartile's own case study describes fishing supply retailer Discount Tackle using automated campaign management to exceed ROAS targets on Google Shopping and Facebook while reducing manual campaign work. Gapstow was not involved in this engagement.

  • Reported ROAS targets exceeded on search and social
  • Reduced manual campaign management time
  • Machine-learning bidding combined with vendor account support
Read the original on Quartile's site

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