Find the barriers to qualified conversions, build improvements and test what the evidence supports. The engagement connects investigation to implementation, with a baseline, reviewed changes and clear acceptance criteria.
What’s included.
Three steps.
Understand
Agree the task, baseline, source systems and delivery boundary.
Build
Develop and review the code, configuration and integrations in scope.
Verify
Test representative cases, compare with the baseline and hand over.
Recognise the challenge?
Start with the problem you see. Explore what we can change, then open the implementation notes for the detail.
01Abandonment analysis and revenue-leak diagnosis
What you may be seeing
- We do not know where we are leaking sales or enquiries.
- Many customers start checkout but never finish.
- A form works for staff but visitors keep leaving it.
- Mobile abandonment is much higher than desktop.
- Heatmaps show activity, but we still do not know what to fix.
We investigate where customers leave and combine behaviour data with research to choose what to improve.
Implementation & verification
We join verified funnel transitions with error, field and device evidence to isolate the specific failure patterns. We use recordings with appropriate masking and observed customer tasks to understand why those patterns occur. We produce an implementation backlog tied to concrete blockers and coordinate code or interface repairs with the relevant group. We validate the repaired path and evaluate completed outcomes, using controlled experiments for uncertain design hypotheses when feasible.
How we verify the work
- Segment the highest-loss steps by device, errors and relevant acquisition/intent groups.
- Connect recordings to the affected event pattern instead of watching an arbitrary sample.
- Repair reproducible failures directly; experiment on competing explanations or designs.
- Measure qualified completion and business-system outcomes, with cancellation/quality checks.
Tools considered for this work
Delivery: analytics tools + SQL (the language used to query a database) analysis; frontend/backend fixes with Groups 1, 4 or 5. Journey evidence: GA4 (Google Analytics 4) or PostHog funnels and order/CRM (a system for managing customer relationships) records. Behavior: PostHog replay or Microsoft Clarity with appropriate masking. Errors: existing APM/error tracking and field-level diagnostic events. Validation: Playwright; GrowthBook/VWO for testable alternatives.
02A/B testing and improvement experimentation
What you may be seeing
- We change pages but cannot tell whether the changes actually helped.
- Team members argue over which design or message is better.
- Conversion increased after a campaign, but traffic also changed.
- Our testing tool and order reports disagree about the winner.
- Our site has too little traffic to get a useful test result.
We build controlled comparisons of alternative experiences and agree in advance what the evidence must show.
Implementation & verification
We translate a researched problem into an experiment with a defined eligible population, randomization unit, primary metric, worthwhile effect and decision rule. We code stable A/B assignment and exposure logging through an SDK/feature flag for application changes, or configure a web-testing platform for suitable page variations. We build the variant, verify both experiences and reconcile outcomes with orders or CRM (a system for managing customer relationships) records while checking assignment balance, duplicate events and rendering errors. We run concurrent versions for the planned sample/observation window and evaluate uncertainty plus revenue, quality and performance guardrails using the selected statistical method. When traffic cannot support a useful result, we use observed-task validation and monitored implementation with explicit causal limits instead of claiming an experimental win.
How we verify the work
- Set eligibility, randomization, sample/duration and stop rules before launch; avoid arbitrary daily winner selection.
- Verify exposure recording, sticky assignment, expected split and valid business outcome capture; investigate sample-ratio mismatch.
- Use the planned fixed-horizon, valid sequential or Bayesian procedure; handle multiple comparisons and account-level clustering when applicable.
- Roll out only a supported decision; document inconclusive results and independently evaluate claimed revenue effects.
Tools considered for this work
Delivery: experimentation tools + frontend/backend variant and tracking development. SDK/warehouse option: GrowthBook, sticky identity and exposure callback; server-side assignment for backend changes. Web option: VWO visual/code editor or split-URL testing for suitable pages. Alternative: PostHog experiments/flags when it fits the existing analytics stack. Measurement: orders/CRM (a system for managing customer relationships) plus GA4 (Google Analytics 4)/warehouse; GA4 (Google Analytics 4) records outcomes and does not deliver variants.
03Conversion Rate Optimization (CRO)
What you may be seeing
- We get visitors but too few purchases, bookings or useful enquiries.
- Customers do not understand our offer or the next step.
- We spend on traffic while existing visitors struggle to complete tasks.
- A higher click rate did not produce more customers.
- We keep making cosmetic changes without evidence of improvement.
We turn conversion research into design and development work, judging success by useful customer actions and business value.
Implementation & verification
We identify the largest verified completion barriers in a defined journey using funnel data, errors and customer-task evidence. We coordinate performance fixes, clearer offer/decision support and transactional or UI changes across the delivery groups. We code and verify the changes in the existing site, then test competing versions when traffic and the decision justify an experiment. We measure the proportion completing a qualified outcome and inspect revenue, errors, cancellations and lead quality before calling a change beneficial.
How we verify the work
- Separate broken-task repairs from unproven offer/interface hypotheses.
- Prioritize affected eligible users, outcome value and strength of evidence rather than a generic checklist.
- Use stable randomized assignment for experiments; monitor simpler rollouts with stated comparison limits.
- Judge qualified completed outcomes per eligible visitor, with business-quality guardrails.
Tools considered for this work
Delivery: analytics + UX/research tools + frontend/backend development. Diagnosis: GA4 (Google Analytics 4)/PostHog, session replay, CRM (a system for managing customer relationships)/order data and targeted task research. Implementation: existing CMS/commerce/app stack and Figma for changed screens. Experiment options: GrowthBook SDK/flags, VWO page testing or PostHog experiments. Verification: Playwright and reconciled business outcome metrics.
04Choose a test the evidence can support
What you may be seeing
- Our traffic is too low to get a useful answer from every split test.
- A higher conversion rate may hide lower margins or poorer leads.
We choose between research, prototype testing and controlled experiments according to the decision and available evidence. Success includes lead quality, margin or retention when those matter to the business.
Implementation & verification
We choose between research, prototype testing and controlled experiments according to the decision and available evidence. Success includes lead quality, margin or retention when those matter to the business.
How we verify the work
- Define the hypothesis, primary outcome, adverse effects to watch and decision rule before the test.
- Check whether the available traffic can distinguish a worthwhile change within an acceptable observation period.
- Use usability research for clear task failures when a quantitative test cannot answer the question well.
- For marketing contribution, distinguish attribution, which assigns credit, from incrementality, which estimates what changed because of the activity. Use controlled comparisons where practical.
Test the idea. Keep the learning.
Automation checks experiment events, allocation and unexpected failures. AI can help organise feedback and research themes; the test design and decision remain tied to the agreed evidence.
Explore the technical toolkit
Selected around your existing stack, licences, permissions and agreed scope.
Define what better means.
Trust
Reconciliation, event completeness and data freshness.
Decisions
Time to answer a defined operational or customer question.
Outcomes
Qualified conversions, retention and experiment uncertainty.
Targets follow the baseline. These are measurement areas, not promised results.
Engineering terms, explained
- Grounded AI / RAG
- An assistant retrieves relevant approved evidence before generating an answer. Access controls and evaluations still need to be engineered and tested.
- Idempotency
- Retrying the same operation does not create an extra order, payment or record.
- Controlled experiment
- A concurrent comparison of assigned variants, with a defined outcome, observation window and decision rule.
- Core Web Vitals
- Measures of main-content loading, responsiveness to interaction and visual stability.
Technical references: Web Vitals · Azure document access · Structured data guidelines.
Quick answers.
We already have analytics. What would you change?
We check whether it answers your actual business questions. That can mean repairing collection, reconciling reports or connecting activity to orders and qualified leads before adding another dashboard.
Can you run A/B tests on a low-traffic product?
Not every product has enough traffic for a useful result. We assess the sample and decision window first. Where a test is impractical, we use task research and monitored changes, with the limits of the comparison made clear.
Can staff ask questions in plain English?
Yes, within a defined set of data and permissions. We build the governed queries and evidence retrieval behind the answer, then check factual accuracy and access boundaries.