Ofer Moskovich
Performance Marketing Manager
(PPC / UA / Growth… or whatever you call it)
“I like clear goals, clean structure, and a campaign that knows what it’s here to do.”
I run paid media for e-commerce and lead-gen brands, in Israel and abroad. Most of the budget goes through Google Ads and Meta - Search, Shopping and social - and I own the account, not just the campaigns: structure, budgets, product feed optimization and creative testing.
I measure with whatever the account gives me - GA4, Shopify reports, Merchant Center and the rest - and judge the work by the one number the client actually cares about, whether that is cost per lead or ROAS.
AI is not the magic fix some people expect it to be. It shortens processes, runs deep analysis fast, spots what made a winning creative work and drafts the brief for the next one - but it will not replace experience or human context, and it still will not make you a coffee.
I produce ad copy and visual creative with gen-AI tools - Gemini, Claude and others - mainly to cut the waiting and the back-and-forth with teams outside the media department.
- What goes in - brand language, ad-account data, the personas we are actually buying, and the format best practices of each platform.
- What comes out - variants ready to test, built in the shape the platform rewards.
- Why it helps - the person making the creative is the person reading the numbers, so less gets lost on the way.
Claude Code, platform APIs and Google Apps Script turn raw ad data into a creative report that runs on schedule, weekly or monthly.
- What it does - ranks the ads and points to the elements the winning ones have in common.
- What comes out - a brief for the next round of creative, ready to hand over.
- What it saves - hours of sitting with exports trying to work out what worked, why, and how to turn that into next month’s creative.
Shopping feeds are where a lot of budget quietly underperforms, and they are usually the last thing anyone opens.
- Build - attribute-rich feeds and ad-ready product titles, structured for Shopping campaigns on Google and on Meta.
- Upkeep - automated search-term analysis and scheduled feed updates, so the changes come from data rather than from a hunch.
- Where it shows - CPC, CTR and conversion rate.
When a number moves, I want the cause and not a theory - so I build root-cause trees that map the metrics of each platform and how they feed each other.
- Setup - a tree per ad platform with its own relevant metrics, connected by API so the data is live.
- Investigation - follow the branch that moved down to the level that actually changed.
- Why it matters - sometimes the cause is not in the media at all, and knowing that prevents changes to an account that was never the problem.
- Israel - fashion e-commerce, Google. Search and shopping optimization, advanced platform features tested one at a time, and better data fed back to the algorithm. No sweeping changes that reset its learning: if it is working, do not break it.
- US - jewellery e-commerce, Google, Meta and supporting channels. ROAS up while both budget and revenue grew, which is the harder version of the number. Deep work on the Shopping feed for Google and Meta, tests across ad formats, personas and messages, customer reviews pulled in from the retention stack, and behavioural data pulled from the Shopify API - so budget kept moving to what was winning, measured month over month.
Hands-on across ad platforms, analytics and AI tooling - deep in some, familiar with others.
- Before entering a platform - is the audience there, is it branding or selling, and can we produce the content it rewards?
- The rule I work by - if a new platform will not beat what you already run, do not rush budget into it. Surplus goes into what works.
- One source of truth - every channel is judged against the same numbers, not its own dashboard.
