Tapping Telecom Data for Audience Accuracy Beyond Google Analytics
myGaru powered analytics solution, Nexinsight, revealed significant inaccuracies in Google Analytics’ gender split and enabled high-income audience segmentation for tailored offers, resulting in improved CTR for fashion e-commerce platform Kasta.
Name
Kasta
Headquarters
Kyiv, Ukraine
Founded
2010 (originally as ModnaKasta)
Industry
E-commerce / Online Fashion & Multi‑Category Marketplace
Platform
Transitioned from a members-only fashion club to a multi-category marketplace offering 2+ million SKUs from 5,000+ merchants
Challenge
Kasta, Ukraine’s leading fashion marketplace, ran direct deals with news platform Ukr.net to reach women, its core customer base. According to Google Analytics, 60% of Ukr.net’s traffic was female, guiding media spend and creative direction. However, campaign performance remained below expectations, prompting doubts about audience quality.
Solution
Ukr.net integrated Nexinsight — a telecom-based analytics tool built on myGaru’s technology. The Nexinsight analysis, driven by telecom data available within myGaru, revealed a striking mismatch: women actually made up only 30% of Ukr.net’s audience. Follow-up verification using SimilarWeb data confirmed myGaru’s telecom-driven insights.
In addition, Nexinsight, powered by myGaru data clean room connected telecom data, provided over 40 behavioural attributes on Ukr.net’s audience, including purchasing power, tech affinity, and consumer intent, enabling a far more granular and actionable understanding of the audience. As a result, Kasta shifted its creative strategy, tailoring messaging to different audiences and also offering higher-ticket items based on new income insights. This led to a noticeable uplift in CTR.
“Thanks to Nexinsight and their technology partner, myGaru, we discovered significantly more male users on certain platforms, prompting us to tailor our value proposition to better match those audiences, which in turn increased CTR. Telecom-driven tracking reveals the purchasing power of users across ad placements, something Google Analytics lacks. We saw that the audience had above-average buying capacity, so we included higher-priced items in our promotions. This delivered a positive performance outcome.”
GA gender split bias identified
01
CTR uplift post-correction
02
Income-tiered offer delivery
03
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