{"id":9498,"date":"2026-01-20T17:40:09","date_gmt":"2026-01-20T14:40:09","guid":{"rendered":"https:\/\/blog.mondiad.com\/?p=9498"},"modified":"2026-05-11T17:21:51","modified_gmt":"2026-05-11T14:21:51","slug":"case-study-lowercr","status":"publish","type":"post","link":"https:\/\/mondiad.com\/blog\/case-study-lowercr\/","title":{"rendered":"Mondiad Case Study: Cutting Costs with Traffic Segmentation"},"content":{"rendered":"\n

In this Mondiad case study, we can’t stress this enough: Data is your best friend<\/strong>! The more data, the better.<\/p>\n\n\n\n

This exact principle became the winning formula behind this Mondiad case study. By leveraging performance data combined with the support from our dedicated account managers, our advertiser was able to identify audience hotspots, refine targeting, and ultimately scale the campaign profitably.<\/p>\n\n\n\n

\n\n\n\n

Starting out<\/h2>\n\n\n\n

Our success story begins when our advertiser, a solo affiliate marketer, reached out to our team for a second opinion on an active affiliate offer he was promoting.<\/p>\n\n\n\n

This campaign focused on China-targeted adult traffic to drive user sign-ups for a Chinese dating platform.<\/strong> While the offer showed potential, performance wasn\u2019t fully optimised yet… and that\u2019s where data-driven insights made all the difference.<\/p>\n\n\n\n

\n\n\n\n

Assessing the campaign<\/strong><\/h2>\n\n\n\n

At the beginning of this challenge, our ad campaign looked like this:<\/p>\n\n\n\n

Offer type<\/strong><\/td>Dating\/Adult<\/td><\/tr>
Ad format(s)<\/strong><\/td>Banner<\/td><\/tr>
Ad flow & Creatives<\/strong><\/td>8 creatives, 300\u00d7250 + Adult LP<\/td><\/tr>
Targeting<\/strong><\/td>China<\/td><\/tr>
Capping<\/strong><\/td>2 impressions per 24 hours per creative<\/td><\/tr>
Bidding model<\/strong><\/td>CPM<\/td><\/tr>
Budget<\/strong><\/td>Initial deposit<\/strong> $1000
Daily budget:<\/strong> $500<\/td><\/tr>
Campaign started<\/strong><\/td>01.10.2025<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

At first glance, it was easy to spot a <\/span>broad traffic targeting<\/b>, mobile & desktop, Android & iOS, 3G & WiFi, all available sources in China.  <\/span><\/p>\n\n\n\n

This can be problematic because our goal is to scale results and optimise costs. Broad targeting is risky, as low-quality traffic consumes the budget first, and simply increasing spend doesn\u2019t guarantee more conversions; it often just wastes money on unprofitable users.<\/span><\/p>\n\n\n\n

Naturally, the second effect of broad targeting was facing <\/span>high acquisition costs<\/b>(~$15) compared to his target cost per conversion(~$4-5).<\/span><\/p>\n\n\n\n

As concluded, the advertiser needed a strategy to improve ROI and scale their campaigns effectively.<\/span><\/p>\n\n\n\n

\n\n\n\n

Intervention 1: Implementing Postback URL<\/strong><\/h2>\n\n\n\n

The client agreed on implementing<\/span> our postback URL, allowing our team to analyse conversion patterns and optimise bids effectively.<\/span><\/p>\n\n\n

\n
\"ad<\/a>
Mondiad case study campaign report<\/figcaption><\/figure>\n<\/div>\n\n\n

As you can see, initially the cost\/conversion was around $15 while his target is to get less than $4-5 per conversion.<\/span><\/p>\n\n\n\n

\n\n\n\n

Intervention 2: Split-Testing Campaign Targeting<\/strong><\/h2>\n\n\n\n
When all traffic is lumped together, winning patterns get diluted, so we decided to run granular tests on our targeting.<\/span>
  1. Conducted granular tests across different targeting parameters (OS, browsers, connection type).Observed significant differences in conversion rates between 3G and WiFi traffic.<\/span><\/li>Decision: Split the campaign into separate campaigns for 3G and WiFi to optimise
  2. performance per segment.<\/span><\/li><\/ol><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

    Testing our offer more granularly allowed us to discove<\/span>r some winning patterns to help us optimise results further. <\/span><\/p>\n\n\n

    \n
    \"ad<\/a>
    Mondiad case study campaign report – connection type<\/figcaption><\/figure>\n<\/div>\n\n\n

    After the first week of gathering conversion data, we noticed that <\/span>winrates and conversion rates were very different between 3G and Wifi traffic <\/b>(screenshot), so we decided to have separate campaigns for these targeting segments.<\/span><\/p>\n\n\n\n

    \n\n\n\n

    Intervention 3: Killing underperforming segments<\/strong><\/h2>\n\n\n\n
    1. Analyzed zoneIDs and subIDs with low conversion rates.Implemented blacklisting or lower custom bids on underperforming sources.Focused on increasing custom bids on high-performing subIDs while monitoring win rates and cost per conversion.<\/span><\/li><\/ol><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

      After creating separate campaigns for 3G and Wifi targeting and a few more days of gathering data, we started consistently looking out for zoneids and subids that show<\/span> low conversion rates,<\/b> to blacklist.<\/b><\/p>\n\n\n

      \n
      \"ad<\/a>
      Mondiad case study campaign report – SubID segmentation<\/figcaption><\/figure>\n<\/div>\n\n
      \n
      \"\"
      Mondiad custom bidding<\/figcaption><\/figure>\n<\/div>\n\n\n

      Similarly, we continued monitoring SubIDs and ZoneIDs to efficiently customise bids based on their performance.<\/p>\n\n\n\n

      \n\n\n\n

      Mondiad Case Study First Results<\/strong><\/h2>\n\n\n\n

      The combination of granular targeting, bid optimisation, and careful blacklisting led to dramatic improvements in just a few days.<\/span><\/p>\n\n\n\n

      Before optimization:<\/b> $300 spent \u2192 20 conversions <\/span><\/p>\n\n\n

      \n
      \"ad<\/a>
      Mondiad case study – campaign report before optimisations<\/figcaption><\/figure>\n<\/div>\n\n\n

      After optimization:<\/b> $300 spent \u2192 100 conversions<\/span><\/p>\n\n\n

      \n
      \"ad<\/a>
      Mondiad case study – campaign report after optimisations<\/figcaption><\/figure>\n<\/div>\n\n\n

      Cost per conversion before optimisation: <\/b>~$15 <\/span><\/p>\n\n\n

      \n
      \"ad<\/a>
      Mondiad case study – campaign report before optimisations<\/figcaption><\/figure>\n<\/div>\n\n\n

      Cost per conversion after optimisation:<\/b> ~$4\u20135<\/span><\/p>\n\n\n

      \n
      \"ad<\/a>
      Mondiad case study – campaign report after optimisations<\/figcaption><\/figure>\n<\/div>\n\n\n

      The campaign continues to scale with ongoing optimization and monitoring.<\/span><\/p>\n\n\n\n

      \n\n\n\n

      Mondiad Case Study Key Takeaways<\/strong><\/h2>\n\n\n\n
        \n
      1. Data-driven decisions matter: <\/b>Implementing postback URLs allows your account manager to see results and give accurate recommendations based on the extra data.<\/span><\/li>\n\n\n\n
      2. Segment traffic wisely: <\/b>Splitting campaigns based on connection type, devices, browsers, os versions, and other granular parameters can uncover high-performing niches.<\/span><\/li>\n\n\n\n
      3. Continuous optimisation is crucial: <\/b>Regularly reviewing zoneIDs and subIDs for performance allows smarter bidding and scaling.<\/span><\/li>\n\n\n\n
      4. Scalable strategy: <\/b>Consistently increasing bids on high-performing sources while monitoring cost\/conversion ensures sustainable growth.<\/span><\/li>\n<\/ol>\n\n\n\n
        \n\n\n\n

        Need a second opinion on your campaign?<\/strong><\/h2>\n\n\n\n

        This Mondiad case study proves one thing clearly: profitable scaling doesn\u2019t happen by chance.<\/strong> It\u2019s the result of data-driven decisions, continuous optimisation, and the right expertise backing every move.<\/p>\n\n\n\n

        \"Mondiad<\/figure>\n\n\n\n

        Speaking of expertise, we are here to help you.<\/strong><\/p>\n\n\n\n

        At ad network Mondiad<\/a>, you\u2019re not just launching ad campaigns, you\u2019re gaining a team of experienced account managers<\/strong> who are ready to share their knowledge, help you uncover hidden opportunities, and guide you toward sustainable growth. <\/p>\n\n\n\n

        Whether you\u2019re testing a new offer, struggling with a new GEO, or looking to scale profitably, we\u2019re here to help you make the most out of your goals.<\/strong><\/p>\n\n\n\n

        Don\u2019t hesitate to reach out.<\/p>\n\n\n\n

        <\/p>\n","protected":false},"excerpt":{"rendered":"

        In this Mondiad case study, we can’t stress this enough: Data is your best friend! The more data, the better. This exact principle became the winning formula behind this Mondiad case study. By leveraging performance data combined with the support from our dedicated account managers, our advertiser was able to identify audience hotspots, refine targeting,<\/p>\n","protected":false},"author":4,"featured_media":9553,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[80],"tags":[90,25,132,59,77,286],"class_list":["post-9498","post","type-post","status-publish","format-standard","has-post-thumbnail","category-success-stories","tag-adult","tag-advertiser","tag-affiliate","tag-banner-ads","tag-case-study","tag-dating"],"featured_image_src":"https:\/\/mondiad.com\/blog\/wp-content\/uploads\/2026\/01\/Mondiad-case-study-dec-2025.jpg","author_info":{"display_name":"Oana Vasarhelyi","author_link":"https:\/\/mondiad.com\/blog\/author\/oana\/"},"yoast_head":"\nMondiad Case Study: Cutting Costs with Traffic Segmentation<\/title>\n<meta name=\"description\" content=\"This Mondiad case study shows that 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