Conversion Systems Agency · Dubai · UAE · KSA
Build the infrastructure that makes conversion compound.
One-off CRO wins don't compound. A landing page test that lifts CVR by 20% is worth 20% — once. A conversion system generates the next hypothesis from the data the last test produced, runs it against a tracking layer that gets richer every week, and applies each winner to a compounding revenue-per-visitor model. The same media spend. A higher revenue-per-visitor floor every time a winner is implemented.
6×
Improvement in test win rate when conversion system is tracking-led from day one
+124%
Median revenue-per-visitor improvement in month 6 vs. month 1 of the system
3
Conversion system layers required before a test pipeline produces compounding results
02 / Why Growth Breaks
Three ways brands scale spend without scaling revenue.
Conversion system failures are not CRO failures — they are infrastructure failures. The testing is sound. The hypothesis was reasonable. The A/B tool is installed. What is missing is the tracking layer that generates the hypothesis, the pipeline that compounds the result, and the architecture that connects acquisition to conversion as one system.
1 in 10
A/B tests win when run without a behavioral tracking foundation
20%
CVR improvement that does not compound — because there is no next test queued from the data
3.2×
More revenue per visitor when acquisition and conversion are designed as one system
CRO Without Infrastructure
Running A/B tests without a complete tracking layer is not CRO — it is guessing with a result. Without step-level behavioral data, micro-conversion events, and server-side signal quality, every hypothesis is generated from convention or competitor observation. The test might win or lose. Either way, the result does not tell you why — so the next hypothesis is equally uninformed.
Test win rates of 1 in 10. Six months of CRO budget producing three statistically insignificant results and one marginal win. The programme is cancelled not because CRO does not work, but because it was run without the behavioral data infrastructure that makes it work. The tracking gap is never identified because there is no tracking to identify it.
One-Off Testing Without a Pipeline
A landing page is redesigned and tested. It wins. The CVR improvement is real and documented. Then the programme ends — because the brief was to 'improve the landing page', not to build a system that continuously generates the next test from the behavioral evidence the last test produced. The win is real but permanent in isolation. Nothing follows it.
A 20% CVR improvement compounded over 6 cycles produces a 2.9× revenue-per-visitor lift. The same 20% improvement without a pipeline produces a 20% improvement — and then decay, as competitors iterate and audience behaviour shifts. One-off CRO wins the battle. The pipeline wins the economic war.
Acquisition-Conversion Misalignment
Paid media spend scales. CPMs rise. The media team optimises toward volume. Meanwhile, the landing page has a 78% bounce rate, the cart abandonment rate is 82%, and the checkout has never been A/B tested. The acquisition system and the conversion system have never been designed to operate together — so each scales independently, and the misalignment compounds.
Brands spending AED 200,000 per month on paid media with a 1.4% funnel CVR are generating the revenue of a brand spending AED 60,000 per month on a 4.2% CVR funnel. The media budget is not the constraint. The conversion system is. And it is invisible because the acquisition team and the CRO team do not share a measurement framework.
03 / The Conversion Systems Framework
Four layers. One compounding conversion system.
A conversion system is not a landing page audit or an A/B test. It is a four-layer infrastructure: a measurement foundation that generates clean signal, a behavioral intelligence layer that generates evidence-led hypotheses, an experimentation engine that converts hypotheses into statistically valid wins, and a performance compounding model that reinvests every win into the next test cycle. Each layer depends on the one below it.
Why it compounds
The system compounds because every test win improves the baseline from which the next test starts. A landing page that converts at 4.2% instead of 2.1% generates twice the behavioral data per traffic unit — which generates better hypotheses — which generates faster test wins. The compounding effect accelerates from month 4 onwards as the behavioral dataset reaches the depth required for consistently specific hypothesis generation.
- 01
System Architecture
The conversion system is designed before any test is run. This means mapping the full acquisition-to-conversion flow: which paid channels feed which landing pages, which landing pages feed which funnel steps, which funnel steps have step-level event tracking, and where the behavioral data gaps are. The architecture phase produces the blueprint — what to track, what to test, and in what order.
Output: Conversion system blueprint: channel map, page inventory, tracking gap list, and test priority matrix - 02
Infrastructure Build
The tracking layer is installed before any test launches. Behavioral tools (heatmap and session recording coverage across all key pages), step-level GA4 events, server-side CAPI events on all active paid platforms, and micro-conversion event mapping are deployed as a single infrastructure build. Quick-win interventions — friction points with behavioral evidence strong enough to implement without an A/B test — are resolved in this phase.
Output: Full tracking stack active, behavioral data flowing, quick wins implemented, A/B test tooling configured - 03
Hypothesis Pipeline
The hypothesis queue is built from behavioral evidence — not from convention or preference. Heatmap analysis, session recording review, GA4 funnel drop-off data, and scroll-depth segmentation generate a ranked hypothesis backlog. Each hypothesis has a stated behavioral evidence basis, a predicted lift range, a target stage, and an evidence confidence score. The highest-scoring hypothesis runs first. Always.
Output: Ranked hypothesis backlog with evidence basis, predicted lift range, and test sequence schedule - 04
Compounding Iteration
Each validated winner becomes the new control. The behavioral data generated by the improved page refines the next hypothesis. The test win rate improves as the hypothesis quality improves. Month 6 tests run from a behavioral dataset 6 months deeper than month 1. Revenue per visitor compounds across every cycle — not because we got lucky, but because the system is designed to get smarter.
Output: Compounding revenue-per-visitor improvement — measured monthly against the system baseline established at launch
Want to see how this applies to your funnel?
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04 / System Architecture
Four layers. One conversion system.
A conversion system is not a test tool and a landing page. It is four integrated layers: the architecture that determines what visitors encounter, the intelligence layer that generates data about how they behave, the test pipeline that uses that data to run evidence-led experiments, and the compounding model that measures the cumulative output as revenue per visitor — not as individual test results in isolation.
Foundation layer
Conversion Architecture
The structural layer of the conversion system: page architecture, funnel sequence, offer framing, message continuity from ad to final conversion event, and CTA hierarchy at each stage. This layer determines what the visitor encounters and in what order. Without a deliberate architecture, the tracking layer has nothing coherent to measure and the test pipeline has no informed baseline to improve from.
Intelligence layer
Behavioral Intelligence
The data layer of the conversion system: heatmap coverage, session recording collection, scroll-depth events, micro-conversion events, GA4 funnel data, and server-side CAPI signals on all active platforms. This layer generates the behavioral evidence that makes every hypothesis specific, every prediction calibrated, and every test result interpretable beyond its binary win/lose outcome.
Engine layer
A/B Test Pipeline
The iteration engine of the conversion system: a continuously-refreshed hypothesis backlog ranked by evidence weight, a test execution process with pre-calculated statistical power, a 95% confidence threshold before winner declaration, and a winner propagation process that applies validated improvements to the architecture layer and refreshes the hypothesis queue with the behavioral data the test generated.
Output layer
Revenue Compounding Model
The measurement framework of the conversion system: revenue-per-visitor tracked monthly against the baseline established at system launch, compound CVR improvement modelled across the full funnel (not at individual pages in isolation), and a monthly system review that recalibrates the hypothesis queue against the updated behavioral dataset. The output of the system is not a test result — it is a compounding improvement in the economics of every media dollar spent.
Creative Systems is the upstream input to every conversion system
The ad creative is step 0. The promise made in the creative determines what the landing page must validate and what message continuity must hold through the funnel. Conversion systems built in isolation from the creative brief always have a message-match gap at the most trafficked entry point.
05 / Behavioral Intelligence
Every hypothesis earns its position in the queue.
The quality of the hypothesis determines the probability of the test win. A hypothesis generated from three independent behavioral signals — a rage-click heatmap, a high exit rate on session recordings, and a GA4 drop-off event at the same page element — is not the same as a hypothesis generated from a best-practice checklist. The behavioral intelligence layer is what makes the difference.
Quantitative signals
GA4 funnel data, event counts, and CVR segmentation
Quantitative signals tell you where visitors exit and at what rate. GA4 funnel reports with step-level events reveal the drop-off magnitude at each stage. CVR segmented by traffic source, device, and page variant reveals which audience segments are converting and which are not. These signals define the priority ranking of the hypothesis backlog — the highest drop-off stage with the highest traffic volume gets the first test.
Qualitative signals
Session recordings, heatmaps, and rage-click analysis
Qualitative signals tell you why visitors exit. A session recording of a visitor who rage-clicks on a non-interactive element, scrolls past the CTA, and exits reveals a specific friction point that a GA4 drop-off number cannot. Heatmap data shows where attention concentrates and where it does not reach. The combination of quantitative (where/what) and qualitative (why) produces hypotheses that are specific, predictable, and testable.
Friction taxonomy
Classifying every friction point by type, stage, and revenue impact
Not all friction is equal. A navigation menu on a paid traffic landing page is visual friction. An 8-field lead form is form friction. A checkout with no BNPL option is trust friction specific to GCC markets. The friction taxonomy classifies every identified friction point by type (visual, cognitive, form, trust) and stage (entry, consideration, commitment, conversion) — so the hypothesis queue is structured, not just a list of gut-feel improvements.
Evidence-weighted hypothesis ranking
Every test hypothesis earns its position in the queue
A hypothesis earns its position in the test queue by accumulating evidence weight — the number of behavioral signals that independently point to the same intervention. A hypothesis supported by a rage-click heatmap, a high exit rate on the session recording, and a GA4 drop-off event at the same page element scores higher than a hypothesis supported by one observation. Evidence weight determines test sequence. Not opinion.
06 / Tracking & Signal Quality
The tracking layer is installed before the first hypothesis is queued.
A conversion system without a complete tracking stack is guessing. The behavioral layer generates the qualitative hypothesis evidence. The step-level event layer generates the quantitative evidence. The server-side CAPI layer ensures test windows do not corrupt platform signal quality. The CRM layer closes the loop to real revenue. All four are required before any hypothesis earns a position in the test queue.
The tracking gap
Why most CRO programmes fail to compound
Most CRO programmes are installed on top of an incomplete tracking stack. The landing page has GA4. The cart has no events. The checkout has a pixel. The CRM has no connection to the front end. Every hypothesis generated from this foundation is missing three of four evidence layers — and the test win rate reflects it.
6×
Higher A/B test win rate when hypotheses are generated from behavioral data vs. convention
94%
Average server-side event match rate under a complete conversion system tracking stack
3.2×
Revenue-per-visitor improvement achievable in 6 months vs. 1-off CRO engagements producing 1 win
Full-stack tracking implementation
The tracking layer is built and verified as the first phase of every conversion system engagement — before any hypothesis is generated.
Behavioral layer
Heatmaps, session recordings, and engagement events
Generates the qualitative hypothesis evidence — the why behind every quantitative drop-off.
Step-level event layer
GA4 micro-conversions across every funnel stage
Generates the quantitative hypothesis evidence — the where and what-rate behind every behavioral observation.
Server-side signal layer
CAPI on all active paid platforms
Ensures conversion signal quality during A/B test windows — so platform algorithms are not skewed by variant traffic differences.
Downstream signal layer
CRM integration for lead quality and revenue signals
Closes the loop between the conversion event and the revenue event — so the system optimises against real revenue, not proxy metrics.
07 / Testing Roadmap
The system reaches full power in month 4. It compounds from there.
A conversion system does not produce its full output in the first test. The first month installs the tracking layer and fixes the obvious frictions. Month 2 runs the first evidence-led A/B tests. Month 4 is when the behavioral dataset is deep enough that hypotheses are consistently specific, predictions are consistently accurate, and the test win rate measurably reflects the evidence quality behind it.
Diagnostic & Infrastructure
Weeks 1–3Full funnel mapping, behavioral tool installation, step-level event deployment, server-side CAPI setup, and baseline metric establishment. Heatmap and session recording coverage activated across all key pages. GA4 funnel events installed and verified. Quick-win friction points identified and resolved without testing — saving test budget for the hypotheses that need validation.
Architecture Phase
Weeks 4–6Highest-confidence architectural improvements are implemented: message-match corrections between active ad creative and landing page, above-fold hierarchy restructure on mobile, and stage-level offer framing alignment. These are behavioral evidence-led changes that do not require an A/B test to implement — they correct documented mismatches between what the visitor is shown and what the conversion architecture requires.
Active Test Cadence
Month 2 onwardOne to two A/B tests running simultaneously at different funnel stages — preventing test interaction effects while maximising iteration velocity. Each test targets the highest-ranked hypothesis by evidence weight. Tests run to 95% statistical confidence. Winners are implemented immediately. The behavioral data generated by each test refreshes the hypothesis backlog for the next cycle.
Compounding Iteration
Month 4 onwardThe system enters its compounding phase: the behavioral dataset is 3–4 months deep, hypothesis quality has improved from accumulated insight, test win rate is measurably higher than month 1, and revenue-per-visitor is tracked monthly against the system baseline. Each cycle makes the next test more predictable. The system is now operating as designed — not as a series of experiments, but as a performance infrastructure.
08 / GCC Conversion Systems
Conversion systems engineered for UAE and KSA — not adapted from Western conversion architectures.
GCC conversion systems are not Western systems localised with Arabic copy. Trust signal architecture, payment method integration, seasonal conversion strategy, and bilingual audience segmentation are structural decisions — not cosmetic adaptations. A conversion system built for GCC markets is designed from the ground up around the specific trust, payment, and behavioural patterns of UAE and KSA audiences.
Regional credibility signals
GCC Trust Architecture
Trust signals that convert a European consumer often fail to convert a GCC consumer making the same purchase decision. GCC audiences in UAE and KSA have specific trust requirements: local payment methods, delivery promise to their country, Arabic customer service availability, and brand presence signals (UAE trade licence, physical address, WhatsApp contact). A conversion system built for GCC audiences integrates these signals at every decision stage — not as afterthoughts.
- Tabby, Tamara, Mada, and Apple Pay at every checkout
- UAE/KSA delivery promise above the fold — not in footer
- WhatsApp CTA as primary trust and support signal
- Local presence signal (UAE trade licence, office address) for high-consideration categories
Language-specific conversion factors
Arabic Conversion Psychology
Arabic-language conversion is not a translation exercise. Gulf Arabic carries different tone registers for different product categories — formal Arabic for financial services, colloquial Gulf Arabic for consumer brands. RTL visual hierarchy requires rebuilt CTA placement, not mirrored layout. Arabic social proof (testimonials named with UAE/KSA location context) outperforms translated English testimonials by a consistent margin in GCC-audience A/B tests.
- Gulf Arabic dialect calibration per product category and market
- RTL visual hierarchy — rebuilt per page, not auto-mirrored
- Arabic-native social proof with local name and city context
- Arabic-language form validation and error messages
Seasonal conversion architecture
Ramadan System Strategy
Ramadan is not a campaign — it is a system event. CPMs rise 40–80% across all paid platforms simultaneously. Evening traffic patterns shift to 8pm–2am. Purchase intent category mix changes. A conversion system without a Ramadan strategy runs the same pages into a transformed audience and wonders why CVR falls despite rising spend. The system adaptation covers landing page messaging, offer framing, checkout trust signal recalibration, and post-Ramadan Eid funnel preparation.
- Ramadan landing page and funnel variant activated 2 weeks before start
- Evening traffic peak alignment with test scheduling
- Eid gift-giving offer architecture for relevant categories
- Post-Ramadan re-entry funnel for cart abandonment recovery
Expat vs. national conversion architecture
UAE Market Segmentation
UAE's 88% expatriate population creates a segmentation challenge that no other market replicates at scale. English-language conversion systems for expat-facing categories and Arabic-primary systems for national-facing categories are not the same system run in parallel — they have different message architectures, different trust signal stacks, and different payment preference hierarchies. The conversion system maps each segment and allocates test budget to the highest-volume audience.
- English-primary conversion architecture for UAE expat-facing categories
- Arabic-primary conversion architecture for UAE national-facing categories
- Language detection for personalized entry to bilingual funnel paths
- Separate test queues per segment — hypotheses do not transfer across audiences
09 / Systems We Build
Ecommerce, SaaS, lead generation, and multi-channel. One system framework.
The conversion system framework is the same across all business models — tracking layer, behavioral intelligence, A/B test pipeline, compounding model. The architecture, the hypothesis set, and the revenue metric differ by model. An ecommerce system optimises revenue per visitor. A SaaS system optimises trial-to-paid activation. A lead generation system optimises cost per qualified lead. Each is built for its specific conversion economics.
Ecommerce
Ecommerce Conversion System
Objective: Revenue per visitor across the full purchase path
An ecommerce conversion system connects paid traffic channels to purchase conversion through a structured architecture: channel-specific landing pages, product page proof density, cart abandonment intervention, checkout friction removal, and a test pipeline running at each stage simultaneously. Revenue-per-visitor is the compound metric. ROAS improvement is the outcome that makes paid media scalable.
System metric: revenue per visitor — tracked monthly against baseline
SaaS
SaaS Conversion System
Objective: Trial sign-up to paid activation rate
A SaaS conversion system spans from paid ad to trial sign-up to onboarding completion to first meaningful value moment to paid conversion. Most SaaS CRO programmes optimize sign-up rate and ignore activation. The conversion system treats the trial-to-paid activation gap as the primary optimization target — because improving activation rate by 50% produces 50% more revenue from the same acquisition spend without changing a single ad.
System metric: trial-to-paid activation rate — tracked against cohort
Lead generation
Lead Generation Conversion System
Objective: Cost per qualified lead — not cost per form submit
A lead generation conversion system is measured at the CRM qualification event — not at the form submit. The system connects form architecture (field count, field type, offer framing) to lead quality (qualification rate, consultation rate, close rate). A form that submits 1,000 leads per month with a 12% qualification rate is outperformed by a form that submits 600 leads with a 40% qualification rate — at lower CPL and higher revenue.
System metric: cost per qualified lead — not cost per raw form submit
Multi-channel
Multi-Channel Conversion System
Objective: Blended revenue per visitor across all traffic sources
A multi-channel conversion system manages different landing page architectures and funnel paths for different traffic sources — because Google Search intent, Meta warm retargeting, and TikTok cold social all arrive with different belief states that require different conversion architectures. The system tracks blended revenue per visitor across all sources, allocates test budget to the highest-volume segments, and maintains message continuity between each channel's creative and its dedicated conversion path.
System metric: blended revenue per visitor — across all paid channels
Every conversion system is built around the specific traffic it serves. Systems receiving Google Ads search traffic, systems receiving Meta Ads social traffic, and systems receiving multi-channel paid media each have different entry-stage audience temperatures that require different landing page architecture and different hypothesis starting points. The system architecture brief begins with the traffic source.
10 / Results
One measure: did revenue per visitor compound as the conversion system matured?
Measured against revenue-per-visitor improvement across the full engagement, not against individual test results. Three conversion system engagements — UAE ecommerce, Dubai SaaS, KSA financial services — each judged on whether the compounding mechanism held as the behavioral dataset deepened.
- EcommerceUAE+186%
Revenue per visitor (M6 vs. M1)
Engaged with a conversion system build after 18 months of one-off landing page tests produced no sustained lift. System architecture phase identified: no step-level event tracking below the landing page, zero cart-stage data, and checkout never tested. Infrastructure build took 3 weeks. First 4 tests targeted cart and checkout — not the landing page. Revenue per visitor grew 186% over 6 months. Cost per purchase fell 44% against the same media spend.
Cost per purchase−44%Read the case study - SaaSDubai+2.8×
Monthly recurring revenue from paid traffic
Trial sign-up rate was 5.1% — acceptable. Trial-to-paid activation was 7.2% — meaning 93% of trial users never converted. The conversion system mapped the full trial lifecycle: sign-up, onboarding step completion, first feature use, and paid upgrade event. Hypothesis pipeline generated 9 evidence-ranked hypotheses across the trial journey. 6 months of systematic testing across sign-up, onboarding, and activation stages produced a 2.8× lift in MRR from the same paid media spend.
Cost per activated user−51%Read the case study - Financial ServicesKSA+3.4×
Qualified lead volume
Meta and Snapchat campaigns generating leads at a high CPL with a 9% CRM qualification rate. The conversion system audit found: 6-field Arabic form with LTR layout, no WhatsApp CTA, and a generic thank-you page. System build: 3-field RTL Arabic form, WhatsApp as primary contact channel, lead qualification integrated directly into the CRM flow. CPL fell 62%, qualified lead volume increased 3.4× over 90 days without increasing spend.
Cost per qualified lead−62%Read the case study
Results are reconstructed from server-side tracking and verified attribution. Figures are representative of typical engagements, not guarantees.
11 / Questions
What operators ask about conversion systems before engaging
Questions from ecommerce operators, SaaS founders, and lead generation businesses evaluating a conversion system engagement.
CRO (conversion rate optimisation) is a discipline — the practice of improving conversion through testing and behavioral analysis. A conversion system is the infrastructure that makes CRO compound over time rather than produce isolated wins. It includes: a complete tracking layer (behavioral tools, step-level events, server-side CAPI), a conversion architecture (page and funnel structure built around the traffic source), a hypothesis pipeline (continuously refreshed from behavioral data), and a compounding iteration model (each winner builds the next baseline). Without the system, CRO is a series of one-off experiments. With it, every test makes the next one more valuable.
Landing Page CRO optimises one page — the entry point from paid traffic. Funnel Optimization maps and improves the multi-step journey from entry to conversion. A Conversion System connects both of these disciplines within a unified infrastructure: the tracking layer that generates data for both, the test pipeline that runs experiments at both the page and funnel level simultaneously, and the compounding model that measures revenue-per-visitor improvement across the full system — not at isolated pages. Landing Page CRO and Funnel Optimization are components of a Conversion System, not alternatives to it.
A complete conversion system tracking stack includes: GA4 with step-level conversion events across all funnel stages, scroll-depth and micro-conversion events at each key page, heatmap and session recording coverage across the full funnel, server-side CAPI events on all active paid platforms (Meta, TikTok, Snapchat, Google enhanced conversions), and where possible, CRM integration for downstream lead quality signals. We install the full tracking stack as the first phase of every conversion system engagement — before any hypothesis is generated or any test is queued.
The diagnostic and infrastructure phase takes 3 weeks. The architecture phase (implementing evidence-led improvements and preparing the first A/B tests) takes a further 2–3 weeks. The first A/B test results are typically available within 30–45 days of engagement start. Meaningful compounding improvement is visible by month 3. The system's highest-value output — a test pipeline that consistently generates statistically significant wins because the hypothesis quality has been refined by months of behavioral data — is visible from month 4 onward.
A conversion system is the direct efficiency multiplier for paid media spend. A 50% improvement in revenue-per-visitor across a funnel receiving 10,000 paid sessions per month produces the equivalent revenue of 5,000 additional paid sessions — at zero additional media spend. We build conversion systems as integrated workstreams alongside paid media: the landing page architecture is designed against the specific traffic source's audience temperature, the test pipeline runs experiments coordinated with creative refresh cycles, and CVR improvements feed directly into media buying decisions on blended ROAS.
A functional conversion system requires enough traffic to generate behavioral data and run statistically valid A/B tests. As a minimum: 1,000+ conversion events per month on the primary conversion goal (purchases, leads, or trial sign-ups) and 5,000+ sessions per month to generate meaningful heatmap and session recording data. Below these thresholds, we run a diagnostic-led optimisation programme that implements evidence-ranked improvements without concurrent A/B testing — until traffic volume supports formal testing. We will tell you your traffic threshold honestly in the first call.
Yes. GCC conversion systems require specific adaptations that go beyond translating English-market architectures: RTL layout continuity across all funnel stages (not just the landing page), GCC-specific trust signal integration (Tabby, Tamara, Mada, Apple Pay, WhatsApp support), Arabic-language behavioral data collection and hypothesis generation from Arabic-language session recordings, Ramadan seasonal system adaptation, and UAE bilingual segmentation (English-primary for expat-facing categories, Arabic-primary for national-facing). The system is calibrated to the GCC audience — not adapted from a Western template.
The ad creative is step 0 of every conversion system. The promise made in the ad determines what the landing page must validate, what the funnel must sustain, and what the checkout must resolve. A conversion system built without alignment to the creative brief will always have a message-match gap at step 1 — the most common and highest-impact source of landing page bounce. We build conversion systems in coordination with creative briefing: the landing page architecture is designed from the creative promise, and creative refresh cycles are timed to align with test windows so new creative does not contaminate active A/B tests.
Start with a conversion system audit
Your paid traffic deserves a conversion system, not a test.
A conversion audit maps your current tracking coverage, identifies where the absence of tracking infrastructure is limiting CRO output, and outlines the conversion system architecture required to compound revenue from your existing paid traffic. Written report delivered within five business days. Specific findings: where your tracking foundation has gaps, where CRO is running without a compounding pipeline, and what to build first. No pitch. No commitment beyond the audit.
- Senior conversion strategist on every engagement
- UAE · KSA · Global
- Full-stack: tracking, pages, funnel, testing