We build performance marketing around your business.Webuild productspend allocationalgorithms.make profitthe bidding signal.tailor your feedto every channel.put every productin one clear view.scale your creativeacross the catalog.turn product ideasinto creative to test.

Product feeds connect catalogs to Google and Meta. This fictional example follows 10,000 products in one Google Ads campaign with a 4× revenue return-on-ad-spend target. The target applies to the combined result; the platform still adjusts bids for individual auctions.

Zoom in on two illustrative products. Both sit under the shared 4× campaign target, but Product A returns 6× and Product B returns 2×. With equal ad spend, their combined ROAS is (6 + 2) ÷ 2 = 4×. The target is met even though performance differs. In a real catalog, combined ROAS is total revenue divided by total ad spend — it is spend-weighted, not a simple average of product ratios.

Platform ROAS is the first row, not the whole decision. We add the percentage of sizes available, sales velocity, on-site conversion and gross margin. In this fictional comparison, A has its full size range, rising sales, stronger conversion and high margin. B has a limited size range, falling sales, weaker conversion and thin margin.

The algorithm combines these inputs with the business’s agreed definitions and rules. A strong platform return can still hide poor contribution. A product with healthy margin and demand may have room to acquire more customers. We use enough evidence to support the decision and leave uncertain products in a test or review group.

The illustrative groups use the team’s own definitions: Stars have high sales and high margin; Cash cows sell in volume with thinner margin; Phase out captures products whose sales leave negative contribution. Grow, Test and Maintain are other possible groups. These are adapted commercial categories, with a reason attached to each assignment. No private margin thresholds are shown.

In the fictional Google plan, Stars receive a 3× revenue target, giving higher-margin products more room to acquire customers. Cash cows receive a 6× target, asking thinner-margin products to earn more revenue per unit spent. Phase out products are held from paid exposure under approved rules. Targets are settings, not promised results. We measure contribution across the catalog and review the groups as evidence changes; people approve the targets and exclusions.

We write group labels back to the product feed. They can organize Google campaign groups and Meta product sets, with separate targets, budgets or actions appropriate to the platform and campaign setup. The platform continues to place auction-time bids; our system changes the product groupings and commercial instructions it works with.

A backpack sells for €120. Standard revenue-based measurement sends the sale value and product information to the ad platform. Bidding sees a €120 purchase.

We receive the same order on your server, including the product, sale price and selected shipping method. We connect those details to your product records and fulfilment rules.

The order identifies the backpack, its €120 sale price and the selected home-delivery service. A new-or-returning customer flag adds context for your acquisition strategy.

Start with €120. Subtract €60 for the product: €60 remains. Picking and packing costs €8, leaving €52. Home delivery costs €12, leaving €40. Payment processing costs €4, leaving €36. The expected return cost is €6, leaving €30.

The five costs total €90. The €120 sale therefore contributes €30 towards advertising and the rest of the business. This is the product contribution we send for bidding.

The calculation closes back into the purchase journey. The server sends the €30 contribution value to Google or Meta as the sale happens. Your team can reconcile the same calculation in commercial reporting.

Bidding can now work towards what the sale contributes, instead of its revenue alone. We turn each purchase into a live profit signal so you can direct spend towards stronger economics and make every advertising euro work harder.

An ordinary product feed can send the same in-stock catalog to advertising and email platforms. That stock flag alone says little about whether the remaining variants suit the channel.

Our example trail shoe is in stock, but only size 41 remains out of ten sizes. Its size coverage is 10%. Many shoppers clicking an ad would not be able to buy their fit.

We connect variant availability to custom feed rules. For this example, the team chooses to hold this limited size range from paid feeds and include it in the email feed. The rule can use other product properties and channel requirements too.

The shoe is held from Google and Meta advertising feeds, while the email feed receives it for customers interested in the available size. This is a rule the team chose for the example, not a claim that every product with one size left should be excluded.

The email version turns the plain title “Trail shoe” into “Trail shoes for your next escape” and replaces a blunt description with “Leave the everyday behind. Find your next favorite trail and make more of every weekend outdoors.” Availability stays in its own field. We adapt titles, descriptions and other properties to the channel, using approved product facts and brand voice. The example shows the delivered product record, not a sent email.

Your team decides what goes where, approves the field mappings and handles commercial exceptions. We turn those choices into repeatable, checked outputs so every channel receives the products and information intended for it.

A store or marketing manager wants to understand a product before changing spend or stock. Marketing has visits and advertising spend; the store has orders and conversion; operations has stock and returns; finance has costs. Assembling the answer means asking people for separate reports.

We bring the departmental records into a shared model, match products and periods, and agree the calculations with the business. Costs, returns and advertising use consistent definitions, so the numbers describe the same product journey.

The connected model becomes a dashboard showing the journey from site visits to orders and returns, alongside marketing spend, available stock and contribution. The business P&L and EBITDA view can use the same agreed definitions; product contribution is not treated as company EBITDA.

Brand, category and product filters let the team move from the catalog to the item they are discussing. The example selects Footwear and a trail shoe, with all brands included and one matched 30-day period.

In the fictional product view, 12,000 visits produce 360 orders: a 3% conversion rate. Eighteen orders return: 5%. Advertising spend is up 8% against the previous period, 84 units are available, and contribution after advertising and returns is positive. The point is that all of this is visible together, not that any one metric proves the next action.

The dashboard is built on defined, accessible product data that can also be connected to AI tools. This is readiness for a connection, not a claim that a live AI system is already answering inside this example. People agree the definitions, access and decisions. The team gets one place to understand the product without rebuilding the report.

Two schematic cutouts are compared. A simple fill keeps the soft contact shadow; a model removes it and makes the product look pasted. The simple method wins on the image, not on complexity.

The product is composed on a brand ground with a relative offer index, original index struck through, rating, offer badge, brand position and disclaimer. These are fictional layout examples, not product claims.

The ad gets a fingerprint from the price, offer, rating, photograph and template. In the illustrative nightly run, 22,000 inputs are checked and 317 have changed. Only those 317 redraw. People own the template and review uncertain photographs before publication.

Four readings are rejected: clicks that count plays, add-to-cart counted six ways, a view-optimised hook rate, and a second advertiser in the account. A retention curve remains, and the chosen angle follows what people kept watching.

A schematic product stands for the real approved cutout. It is composed onto the brand ground and labeled frame 1. A brief counterexample morphs without the pin; the pinned geometry holds. In production we use the real reference.

The shot routes to a performance clip, a silent loop or a static card. Relative effort bars carry no cost claim. A loop plays forward and back; the illustrative seam measure falls from 33.6 to 0.6 RMS, below the 12/255 craft reference.

The whole-duration frame grid is checked. Three frames flag an unintended mark, a stray hand and a numeral. Audio, a watermark-free master and captions get a second check. A script-free transcription catches a dropped word; a scripted pass would repair it and hide the failure. The content must be corrected and rechecked before approval.

One approved clip fans out into six posts with different opening hooks and no further generation. In the supplied work, hook changes moved performance 50–200%, versus 20–30% for body changes; these are observed ranges, not promised uplift. The script, claims and final publishing decision remain human.