RTB House: everything you need to know
Best for: retailers with meaningful site traffic who want deep-learning-powered retargeting (and full-funnel programmatic beyond it) to convert the visitors their other channels already paid to acquire.
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| Fast | Longer |
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| Discovery | Purchase |
RTB House is a global demand-side platform built on one technical bet: bidding and personalisation decisions made entirely by deep learning rather than classic machine learning. Born as a display-retargeting specialist and now a full-funnel programmatic partner with over a thousand specialists worldwide, it shows your visitors personalised product ads across the open web: the sofa someone configured, the sneakers they abandoned, the category they browsed, assembled dynamically from your product feed.
The 2026 reason to look at the channel is privacy engineering. RTB House has been one of the most active testers of Google’s Privacy Sandbox since its announcement, running cookieless retargeting experiments at the scale of millions of ads across 50 countries, and has built first-party-data advertising and Topics API integrations for a world where third-party cookies are a user choice rather than a default. Retargeting is the discipline cookie deprecation threatens most, and this is one of the vendors that engineered for the aftermath early.
Through Lengow, retailers prepare the product feed those personalised ads are assembled from, following the standard channel workflow: attribute and category mapping, image fields, deep links, product identifiers kept consistent with on-site tracking, and scheduled updates so the ad a visitor sees tonight shows the price they’ll find tomorrow.
Key figures
Sources: RTB House (company-published figures and client results, 2024-2026); RTB House promo-periods research; RTB House Privacy Sandbox testing reports. Client results are vendor-published and vary by vertical, traffic volume and campaign setup.
Channel positioning
Who will see your ads?
First and foremost: your own visitors. Retargeting works on the audience your other channels already delivered, the cart abandoners, product viewers and category browsers who left without buying. That’s what puts the channel late in the funnel: it doesn’t create demand, it recovers demand you paid for elsewhere, which is also why its conversion rates flatter it and why incrementality deserves honest measurement.
Personalisation is the mechanism: shoppers are far more likely to buy from brands whose ads reflect what they actually looked at, and dynamic creative built from the product feed is how that happens at catalogue scale, on publisher sites across the open web rather than inside one walled garden.
Beyond retargeting, the full-funnel campaigns extend reach to new customers who resemble existing buyers, which shifts the audience question from “who visited” to “who the model predicts will convert”, still fed by the same catalogue.
High-traffic retailers
Large visitor pools give the algorithm the behavioural signal it learns from; the more browsing data, the sharper the personalisation.
Large, varied catalogues
Fashion, beauty, electronics, home: verticals where product-level personalisation beats generic banners, and where the feed gives the engine choices.
Considered-purchase journeys
Products researched over days (travel, furniture, premium goods) leave the browsing trails retargeting converts best.
A weaker fit: thin traffic, unmeasured incrementality
Small visitor pools starve a learning algorithm, and retargeting’s flattering last-click numbers deserve scrutiny: without incrementality testing, you may pay to convert buyers who would have returned anyway.
What you need to know before launching
The launch has three moving parts: the commercial setup with RTB House, the tagging on your site, and the feed. The first two involve their team; the third is yours, and it’s what the ads are made of.
Advertiser profile
Retailers with an e-commerce site, meaningful traffic volumes for the algorithm to learn from, and a catalogue worth personalising. Campaigns are set up and managed with RTB House’s team.
Cost model & budget
CPC or CPM depending on campaign goal, with custom bidding strategies steering toward your CPA or ROAS targets. Judge spend against incremental revenue, not just attributed revenue.
Countries
RTB House runs campaigns globally, with cookieless tests alone spanning 50 countries. Multi-market advertisers run per-market feeds with local currency and language.
Technical setup
RTB House tags on your site capturing product views, carts and purchases, plus a Lengow product feed delivered to the channel following the standard workflow, with scheduled updates.
Feed requirements
Complete product data for dynamic creative: identifiers matching your on-site tracking, titles, categories, prices, availability, deep links and banner-quality images, with variants structured and parent products excluded per the standard workflow.
Measurement
Campaign reporting against CPA and ROAS goals from RTB House, cross-checked in your analytics. The mature move is periodic incrementality testing, and a post-cookie plan agreed with their team.
How to advertise with RTB House and Lengow
Advertising with RTB House through Lengow starts from the catalogue: a segment defines which products enter the retargeting pool, attributes and categories are mapped to the channel’s structure, and the feed ships on a schedule matched to your price and stock rhythm. Every personalised banner the engine assembles inherits that data, which makes the feed the creative brief.
RTB House follows Lengow’s standard channel workflow. Check out Lengow’s advertising channel setup guide.
| Channel challenge | What successful advertisers do | What Lengow automates |
|---|---|---|
| ID consistency | Keep feed product IDs identical to the IDs fired by on-site tags, since retargeting matches the product a visitor viewed to the product in the feed. | Identifier fields are mapped from the source catalogue, the same reference the site uses. |
| Feed-as-creative | Treat titles, prices and images as banner components, because dynamic creative is assembled from them per user. | Rules restructure titles and fields per channel without touching the source catalogue. |
| Banner-quality images | Provide clean, high-resolution product images that survive being composed into display formats. | Image fields are structured from catalogue assets; AI Image Compliance helps at scale. |
| Price & stock truth | Keep the feed current, since showing yesterday’s price to someone who saw today’s on your site kills the click. | Scheduled feed updates keep prices and availability aligned with the site. |
| Retargeting-pool hygiene | Pull out-of-stock and end-of-life products fast, because retargeting someone toward a dead product page pays for the worst possible click. | Automation rules disable out-of-stock products; exclusions remove discontinued lines. |
| Margin-aware selection | Decide which products deserve paid re-engagement, rather than retargeting the whole catalogue by default. | Segments and filters control which products enter the feed, by margin, category or performance. |
| Promotion signals | Carry sale prices and promo data so the engine can show the discount that finishes an abandoned purchase. | Sale-price and promotion fields are managed as feed data. |
| Variant clarity | Structure variants so the banner shows the colour and size the visitor actually viewed, not a random sibling. | Variation handling structures parent-child data, with parent products excluded. |
| Multi-market campaigns | Run per-market feeds with local currency, language and pricing for campaigns across countries. | Per-country feeds adapt currency, language and content from one master catalogue. |
| Feed health | Catch feed errors before the campaign does, since a broken feed silently shrinks the retargeting pool. | Feed monitoring and channel reports surface errors early. |
With Lengow AI Studio, AI is embedded directly into the feed workflow: AI Category Mapping structures the catalogue, AI Content Localisation adapts product content per market, and AI Image Compliance prepares visuals for dynamic creative at scale, the raw material a deep-learning engine turns into personalised ads.
Is RTB House right for your stack?
Retargeting earns its place when it recovers demand at a cost your margin accepts, and RTB House’s pitch is that a deep-learning engine recovers more of it per euro. The strategic argument is newer: the vendor spent years engineering for cookieless retargeting while much of the market waited, which matters for any advertiser whose display performance still leans on third-party cookies.
Retargeting is the discipline cookie deprecation threatens most, and the vendors who engineered for the aftermath early are the safer bet. Whatever the engine, the ads are assembled from your feed: the personalisation is only as good as the data.
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