Illia T.
Offer Illia work on your next project.
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Khmelnitskyi,
Ukraine
1 month 12 days back
available for hire
on the service 4 months 6 days
Rating
Successful projects
Average rating
Language proficiency level
Українська: fluent
Русский: fluent
English: pre-intermediate
CV
🔹 PPC / Google Ads specialist for e-commerce
🔹 Data-driven approach, analytics, system thinking
I specialize in PPC and Google Ads for e-commerce businesses where advertising is part of a broader business system, not just a traffic channel.
My approach is shaped by hands-on experience working inside e-commerce: understanding unit economics, product, conversion, and operational constraints. That’s why I don’t focus only on “nice-looking campaigns”, but treat paid traffic as a profit-driving tool.
I work in a data-driven way: reliable analytics, clear cause-and-effect logic, and decisions that can be scaled without breaking performance. No “magic” inside ad accounts and no unrealistic promises.
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🔧 What I work with:
✔️ Google Ads (Search, Shopping, Performance Max)
✔️ PPC for e-commerce
✔️ Product feeds, including large and complex catalogs
✔️ Analytics: GA4, GTM, proper conversion tracking
✔️ Optimization with margin and scalability in mind
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⚙️ How I work:
– start with data and current account analysis
– verify analytics and conversion tracking accuracy
– evaluate advertising from a unit economics perspective
– suggest solutions focused on long-term results
I work transparently and systematically, with a strong focus on business outcomes.
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🤝 Cooperation formats:
– audit and consultation
– PPC / Google Ads setup
– long-term campaign management
– partnership-focused collaboration
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👥 Best fit for:
– e-commerce businesses already running paid traffic
– teams looking to fix analytics and decision-making
– founders and managers who want to understand what works and why
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🏅 Additional:
– Google Partner status
– Active Google Ads certifications
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📩 Open to discussing your project and goals. Feel free to message me and see if we’re a good fit.
Skills and abilities
Promotion
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Contextual Advertising
from 16 USD for hour
- Lead Generation & Sales
Portfolio
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Setting up Google conversion tracking for e-commerce
Contextual Advertising
Case: Bringing the e-commerce conversion system in line with Google standards
Work format: Setting up and restoring a correct conversion system
Stack: Google Tag Manager, Google Analytics 4, Google Ads, Data Layer
… Project type: e-commerce + lead generation
Client request
The client approached with a typical problem for e-commerce projects:
there was complete chaos with conversions in the advertising account, which prevented Google Ads from optimizing correctly.
Additionally, it was discovered that:
— conversions were custom set up by a previous specialist
— events did not meet Google’s e-commerce standards
— purchase was either not recorded or transmitted incorrectly
— value was assigned to secondary actions (phone clicks, micro-conversions)
As a result, the advertising was not learning from real sales.
Initial situation
— Standard e-commerce events were missing
— Data Layer did not comply with Google documentation
— Conversions in Google Ads were blurred
— Purchase was not used as the main optimization goal
— Algorithms were optimized for secondary actions
Decision made
It was decided not to fix the analytics on top of incorrect logic,
but to first bring the technical foundation in line with Google standards.
To do this:
— a technical task was prepared for the developer
— all events were brought to Google’s standard e-commerce model
Implementation
Preparation of the technical task for the developer
The client was recommended to first involve a programmer.
I prepared a detailed technical task, in which I specified:
— which e-commerce events should be transmitted to the Data Layer
— the structure and parameters of the events
— mandatory events:
view_item
add_to_cart
begin_checkout
purchase
Control of Data Layer implementation
During the developer's work, I:
— constantly tested events
— checked the correctness of the Data Layer
— monitored duplication
— controlled the sequence and logic of triggering
Final check after development
After the work was completed:
— the Data Layer was fully re-checked
— the correctness of all e-commerce events was confirmed
Setting up Google Tag Manager
Next, I:
— set up Google Tag Manager
— created triggers for Data Layer events
— transmitted events to Google Analytics 4
Setting up analytics and advertising
— events in Google Analytics were marked as key
— key events were transmitted to Google Ads
— Purchase was designated as the main conversion
— value was removed from secondary conversions
— phone clicks and micro-actions were excluded from optimization
Setting up lead forms
Since the business also works with applications:
— all forms on the site began to transmit the Form Submitted event to the Data Layer
— through Google Tag Manager, the event was transmitted to Analytics as Generate Lead
— Generate Lead was transmitted to Google Ads as a separate conversion
Result
— Google Ads began to see the Purchase conversion for the first time in a long period
— algorithms began to learn correctly from real sales
— advertising is optimized for business results
— the conflict between micro-conversions and purchases disappeared
— the conversion system became transparent and manageable
Summary for the client
— Correct e-commerce conversion system
— Full compliance with Google standards and documentation
— Clear logic for advertising optimization
— Reliable foundation for scaling without data distortion
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1000 USD Case: scaling Google Ads for e-commerce auto parts
Contextual Advertising
Project Context
The client approached with a task to scale Google Ads advertising in the auto parts niche — one of the most complex in terms of account structure and the functioning of Google algorithms. Prior to this request, the client had collaborated with several agencies. Despite the agencies' experience in Google Ads, the lack of deep expertise specifically in auto parts prevented achieving stable and predictable results.
… Initial Situation
The project had a number of systemic difficulties:
— a large product feed
— dozens of categories
— thousands of parts with varying applicability
— a wide price range even within a single category
— different margins and the impact of products on the average check
After the audit, a key limitation was identified:
contractors launched one or several campaigns for the entire feed, resulting in:
— Google effectively showing only 5–10% of products
— the majority of the assortment not receiving any impressions at all
— algorithms "getting stuck" on a narrow group of products
Key Issues
Feed impression limitations
Google could not learn effectively from a large array of products within a single campaign, so only the most "convenient" positions for the algorithm made it to the auction.
Bias towards ROAS
The algorithm sold only those products that fit within the specified ROAS, ignoring positions that:
— were profitable for the business
— brought customers to adjacent categories
— increased LTV and repeat sales
Lack of connection between average check and conversion price
The conversion price was assessed equally for all products, although the average check and margin differed fundamentally.
Decision Made
I abandoned the standard logic of "one feed — one campaign" and developed a personalized account structure with deep segmentation.
Implementation
Segmentation by price segments
The entire assortment was divided into price segments. For each segment:
— a specific acceptable conversion price was set
— the average check was taken into account
— the economics of the specific group of products was considered
This allowed:
— to eliminate inadequate order costs
— to equalize effectiveness among different price categories
— to stop "squeezing" profitable but more expensive products
Working with a large feed through additional feeds
To address the issue of slow product entry into impressions, a fragmentation strategy was applied:
— a segment of approximately 100,000 products was divided into 8–10 sub-feeds
— separate campaigns were launched for each sub-feed
— during the learning phase, products with the best performance were selected
Next, products were reassembled based on the logic of:
— "stars"
— "cash cows"
— products with growth potential
— underperformers
Each group received its own advertising logic.
Result
— scaling limitations were removed
— advertising stopped hitting algorithmic limits
— Google began to work evenly with the assortment
— advertising generates exactly the number of leads needed by the business
Important point:
scaling became manageable. If necessary, the volume of leads can be increased at any moment — without destroying the structure and losing effectiveness.
Summary for the Client
— a stable and controllable advertising system
— utilization of the real potential of the assortment
— turnover growth without bias towards individual products
— account structure tailored to the business, not to agency templates
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Audit of Google Merchant Center and restoration of access to advertising
Consulting
Case: audit of Google Merchant Center and restoration of advertising access
Work format: audit of Google Merchant Center, website, and product feed
Client request: Google Merchant Center account is blocked, products are not allowed for advertising, Google does not accept the store. The client does not understand the reason for the blockage and does not know what needs to be corrected.
… Current situation
— Google Merchant Center account is blocked
— Products are rejected and not allowed for advertising
— Rechecks are not passing
— The client does not receive clear explanations from Google
— Advertising of products is impossible, sales are halted
Audit tasks
— Identify the reasons for the Merchant Center account blockage
— Check the website for compliance with Google requirements
— Analyze product data and feed structure
— Evaluate the settings and status of the Merchant Center account
— Prepare a clear correction plan for passing the recheck
Findings
Website issues
— Non-compliance of the website with Google Merchant Center requirements
— Insufficient transparency of information for users
— Errors in mandatory pages and trust elements
— Violations critical for passing moderation
Product data issues
— Errors in product attributes
— Discrepancies between the website and the feed
— Parameters that led to product rejections
Merchant Center account issues
— Incorrect account settings
— Violations of Google Merchant Center policies
— Lack of a systematic approach to checking requirements
Actions taken
Website audit
— Checking the website against the Google Merchant Center requirements checklist
— Identification of critical and minor violations
— Formation of a list of specific corrections
Product and feed audit
— Analysis of product attributes
— Verification of data compliance between the website and the feed
— Identification of reasons for product rejections
Audit of Google Merchant Center account
— Checking account settings
— Analysis of blockage and rejection history
— Identification of factors hindering the passing of checks
Results
— The client received a structured document with recommendations
— Clearly defined what and where needs to be corrected
— Corrections divided into critical and minor
— Clear logic of actions for resubmission for review
— Basis for restoring access to product advertising
Value for the client
— Understanding the real reasons for the blockage, not assumptions
— Time savings on chaotic attempts to pass moderation
— A clear action plan without experiments
— Opportunity to restore advertising and sales
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Google Ads Account Audit for UTAK
Contextual Advertising
Case: Google Ads Account Audit for YUTAK
Scope of work: Google Ads account audit
Client goal: obtain an independent evaluation of current advertising and understand
… — whether the account structure is correct
— whether the current specialist is performing effectively
— whether optimization or changes are required
Initial situation
— Google Ads was already launched and actively running
— Advertising was consuming budget with unstable results
— There was no confidence that algorithms were optimized for real business outcomes
— Account structure did not reflect actual customer behavior
Audit objectives
— Review the correctness of the account structure
— Evaluate current campaigns and bidding strategies
— Analyze conversion tracking and analytics setup
— Assess alignment between advertising and the real business model
— Provide clear, actionable recommendations
Key findings
Conversion tracking issues
— Conversions were configured incorrectly
— Google algorithms optimized toward non-target actions
— Campaign behavior was unpredictable
— Budget was spent without stable results
Inefficient campaign settings
— Settings increased spend without improving efficiency
— No traffic prioritization logic
— Campaigns were not adapted to different demand scenarios
Mixed B2C and B2B audiences
— The website attracts users with different behavioral patterns
— This was not reflected in the advertising account
— The same campaign logic was applied to fundamentally different customer types
Google Analytics issues
— Website data was collected incompletely
— Part of user actions was lost
— Analytics did not support confident decision-making
Google Merchant Center risks
— Potential non-compliance with Google policies was identified
— Risk of restrictions or suspensions existed
— Preventive actions were required instead of reactive fixes
Recommendations provided
Optimized account structure
— Campaign separation based on B2C and B2B audiences
— Clear and scalable account architecture
— Structure aligned with business objectives
Conversion system improvements
— Proper setup of key conversions
— Alignment between analytics and advertising goals
— Preparation for stable algorithm learning
Review of all active campaigns
— Individual analysis of each campaign
— Clear recommendations on what to pause, rework, or keep
Google Analytics audit
— Identification of data collection issues
— Recommendations to fix tracking logic
— Improved data reliability
Google Merchant Center recommendations
— Website compliance review
— Risk mitigation for suspensions
— Preparation for long-term stable advertising
Bonus: website conversion rate optimization
— Page analysis from a user behavior perspective
— Recommendations focused on decision-making logic
— Conversion improvements rather than visual redesign
Client outcome
— Clear understanding of the real state of the advertising account
— Separation of effective elements from budget-wasting ones
— Step-by-step improvement plan without assumptions
— Solid foundation for further optimization
Activity
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