Lizay Pırlanta operates in fine jewellery, one of Turkey’s most competitive categories. High CPCs, strong brand players and intense competition were making new customer acquisition expensive.
When we took on the project, the bulk of sales was coming from brand searches, generic (non-brand) growth was limited and the cost per new user was rising. On top of that, the total media budget was not going to increase.
Our goal was to generate more revenue from the same budget and turn search into a scalable growth system for Lizay Pırlanta.
The Problem: Moving from Safe Brand Demand to New Demand
Brand awareness created a safe space in the short term, but on its own it did not generate new demand. Costs were high on competitive jewellery keywords, and the purchase motivations of men and women were completely different: male users mostly arrived with marriage and proposal intent, while female users were focused on self-purchase and everyday wear.
On top of all this, the growth expectation had to be met without any budget increase. What Lizay needed was not simply campaign management; it was an architecture that does not just capture demand but increases it.
The Strategy: A Full-Funnel Performance Architecture
The solution was not to manage search and social as separate performance areas, but to build a system that creates new demand, in which each channel feeds the other with data. We built that system on four strategic layers.
1. Creating new demand from generic searches
We focused on category-level searches such as “solitaire ring”, “gold necklace” and “diamond ring”. Our approach was not about volume, but about acquiring high-intent new users. We positioned generic search not as an upper-funnel visibility area but as the entry point to growth, bringing in users who had no connection with the brand yet but a strong connection with the category.
2. Turning brand search into a data engine
Users searching for the brand already knew Lizay but were not buying yet. Instead of treating this audience only as a “brand conversion” metric, we passed them into Meta custom audiences; we retargeted them with personalised messaging, brought them into assisted conversion journeys and completed the conversion path with cross-channel remarketing. Brand search therefore became not just a zone of trust, but a data source feeding performance.
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3. Accelerating the algorithm with micro conversions
By actively using actions such as “view_content” and “add_to_cart”, we helped the algorithms learn faster. These micro signals improved targeting quality, lowered conversion costs and supported a steady rise in ROAS.
4. Managing product performance like an investment portfolio
Not every product performed the same; some generated high revenue despite low impressions. Putting GA4 data at the centre, we analysed performance at product level, gave more visibility and budget to high-return products, and stepped back from low-performing areas in a controlled way. We managed performance with data rather than intuition.
Execution: A Self-Feeding System from Search to Conversion
We rebuilt the audience architecture around demographic segmentation by age and gender; product view, cart and checkout behaviours; and lookalike audiences. On Google we set up a multi-campaign model, and on Meta a funnel-based structure.
The Search → Remarketing → Conversion loop thus turned into a self-feeding system.
Timeline
March–April: We analysed the existing campaign structure in detail. We reviewed product and category performance; assessed seasonal demand dynamics such as weddings and special occasions; and redesigned the audience architecture. We prepared the infrastructure by integrating micro conversion signals.
May: We activated the full-funnel strategy at full scale. We made GA4-based budget shifts, intensified remarketing and accelerated generic growth. From May onwards, a clear and measurable jump in performance began.
Challenges Overcome
The first challenge was growing new customer acquisition in high-cost generic categories while keeping the budget fixed. We solved this by focusing on purchase intent rather than volume and by managing product-level return with GA4 data.
The second challenge was not squeezing different user motivations into a single campaign logic. By combining demographic and behavioural segmentation with the funnel structure, we delivered the right message at the right stage. Brand search, generic search and remarketing stopped being disconnected channels; they became data layers of the same growth system.
Results
From April to May, new user volume rose with no budget increase, cost per new user fell and total revenue increased. GA4 purchases grew 125%, Meta Ads purchases 102% and Google Ads purchases 64%.
Target / Metric | Result |
|---|---|
GA4 purchases | 125% increase |
Meta Ads purchases | 102% increase |
Google Ads purchases | 64% increase |
Meta Ads ROAS | +~9 |
Google Ads ROAS | +6 |
Media budget | Growth with no increase |
Takeaways for Brands
The Lizay Pırlanta project showed that growing on a fixed budget comes not from simply launching more campaigns, but from setting up the right flow of data between search and social channels.
With the right intent focus, generic searches can become the entry point for new customer acquisition.
Brand search does not only produce conversions; it creates a strong data layer for cross-channel remarketing.
Micro conversions can shorten the algorithms’ learning period, improving targeting quality and ROAS.
Tracking product-level return makes growth on a fixed budget possible by moving budget into the areas that produce the highest commercial value.
For Lizay, “search” is no longer just an advertising channel; it is a strategic growth infrastructure.