Yektanet
Scaling product systems across ad-tech, automation, experimentation, and 0→1 growth
Associate PM → Product Manager · 2019–2022
Worked across CRM automation, advertiser UX, retargeting optimization, and a 0→1 e-commerce product at scale.
Iran's largest ad network at 25M+ users had manual CRM, a leaky advertiser UX, rule-based retargeting, and an un-monetized creator tool.
There was no automated lifecycle, no structured way to convert support friction into product fixes, and no experimentation muscle around delivery.
Treat each phase as a distinct product surface — automation, UX, algorithm, then 0→1 — and prioritize what would actually move advertiser outcomes.
Designed lifecycle automation for 20,000+ accounts, shipped UX changes informed by ticket-pattern analysis, ran dynamic segmentation experiments to move retargeting from rules to algorithm, and built a 0→1 e-commerce motion on top of a creator tool.
~30% growth in average daily ad revenue, ~7% drop in key support ticket categories, ~10% lift in sign-up conversion, and ~1,000 customers converted before a strategic pivot.
Track downstream metrics — advertiser retention, renewal — from the start, not after the algorithm proves out short-term revenue.
Detailed file
Context
Yektanet is Iran's largest online advertising network — 25M+ unique users, 5 billion monthly impressions. Over nearly 3 years I moved across 4 distinct product areas.
Phase 1 — CRM & marketing automation
Problem
20,000+ advertiser accounts with no systematic way to onboard, nurture, or re-engage them. Communications were manual and inconsistent.
What I did
Built the company's marketing automation system from scratch. Defined automation rules across the full customer lifecycle — welcome sequences, activation nudges, re-engagement flows — covering 20,000+ business accounts via email and SMS.
Phase 2 — Advertiser panel UX
Problem
High volume of repetitive support tickets signalling friction in the advertiser self-serve experience.
What I did
Combined Google Analytics funnel data with support ticket analysis to locate friction points. Shipped UX improvements including a structured FAQ before ticket submission — reducing ticket volume for key complaint categories by ~7%.
Phase 3 — Retargeting & algorithmic optimization
Problem
The retargeting product was delivering ads using manual frequency and timing rules. Revenue was being left on the table.
What I did
As sole PM working with a data science team, designed experiments around ad frequency caps, timing windows, and impression pacing. Rather than fixed A/B splits, we segmented users dynamically across scenarios — shifting traffic allocation as performance signals emerged, similar to a multi-armed bandit approach. Transitioned delivery from manual rules to an algorithm, contributing to ~30% growth in average daily ad revenue.
Lesson: The shift from rule-based to algorithm-driven delivery is as much a stakeholder challenge as a technical one. Getting sales and ops to trust the model required showing them the experiment data, not just the outcome.
Phase 4 — 0→1 e-commerce platform
Problem
A link-in-bio tool for Instagram creators had thousands of users but no monetization path.
What I did
Built tracking dashboards to understand the conversion funnel. Improved sign-up conversion by ~10% through data-informed UX changes. Designed and executed the 0→1 e-commerce motion — converting ~1,000 users before a strategic pivot shut the project down.