The Challenge
Bayut and Dubizzle are two of the UAE’s leading online marketplaces — Bayut in property and Dubizzle across cars and general classifieds. Both serve buyers, renters and sellers across Arabic and English, with audiences that behave very differently depending on locale, intent and the category they land in.
At that scale, the commercial goal is twofold. The business needs to keep ad listings flowing from sellers and dealers, and it needs the demand side to convert into qualified leads for those listings. A property enquiry in Dubai is not the same signal as a car enquiry in another emirate, and a first-time visitor is not the same as a returning one. Generic messaging treats all of those the same, which leaves both listing volume and lead quality on the table.
The task was to make the early relationship with each user work harder: guide new arrivals toward relevant inventory, and turn interest into contact across every language and locale the marketplaces support, without adding friction to the browsing experience.
Our Approach
We treated personalization as a full-funnel problem rather than a single campaign, then sequenced the work so that each stage earned the next.
Crawl phase. We started by mapping how users move through Bayut and Dubizzle and where intent actually forms, then connected the behavioral and listing signals that already existed inside the business. This phase was about establishing a reliable read on who a user is, what they are looking for, and which locale and language they should be spoken to in, so that later automation acted on sound inputs rather than assumptions.
Walk phase. With that foundation in place, we built the lifecycle machinery across several coordinated workstreams:
- Internal ML recommendation engines. We used the group’s own machine-learning models to generate property and car recommendations tuned to each user’s demonstrated intent, so the inventory shown reflected what someone was likely to enquire on next.
- Dynamic segmentation. Audiences were grouped and continually re-grouped on live behavior rather than fixed lists, letting each user’s messaging track their real position in the buying or selling journey.
- Automated property and car recommendations. The model outputs were placed into automated experiences that surfaced relevant listings across both marketplaces, connecting the demand side to the ad inventory sellers had posted.
- Multi-locale, multi-language lifecycle automation. The welcome and lifecycle journeys ran across the locales and languages the marketplaces serve, so Arabic and English audiences each received communications built for them rather than a translated afterthought.
Each workstream fed the others: segmentation shaped which recommendations were relevant, and the recommendations gave the lifecycle journeys something worth sending.
The Outcomes
The engagement moved personalization from a set of one-off tactics to a repeatable, automated part of how new users are welcomed and guided across both marketplaces.
- A 35% welcome-journey conversion rate, showing that early, relevant lifecycle communication turned new arrivals into engaged users.
- Automated property and car recommendations running across multiple locales and languages, powered by the group’s internal ML engines.
- Dynamic segmentation that keeps audiences aligned to real behavior, so messaging stays relevant as intent shifts.
- A full-funnel foundation connecting the demand side to ad listings, supporting both listing activity and higher-quality lead generation across Bayut and Dubizzle.