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If your CV says things like "responsible for improving system performance" or "worked on cross-functional projects," you're describing your job — not your impact. And in a competitive global job market, that's not enough.
The XYZ Formula is a simple framework that fixes this. It's used internally at Google, and it's the single most effective way to turn a forgettable CV bullet into something a recruiter actually remembers.
What Is the XYZ Formula?
The formula works like this:
Accomplished [X] as measured by [Y], by doing [Z].
X — What you achieved (the outcome or improvement)
Y — How you measured it (a number, percentage, time saved, etc.)
Z — How you did it (the specific action, tool, or method)
Here's a quick example:
Before: Responsible for improving API response times.
After (XYZ): Reduced API response times by 42% by optimising database queries and adding Redis caching.
Same role. Completely different impression.
Why Does This Matter for Non-Native Speakers?
If English isn't your first language, you're already working harder than native speakers to communicate clearly. A vague CV makes that even harder — it gives recruiters nothing concrete to hold onto.
The XYZ Formula solves this by doing the work for you. It gives your bullet points a clear structure, so your impact is obvious even if your phrasing isn't perfect. Recruiters scan CVs in seconds. A number or a result catches the eye immediately.
It also helps you prepare for interviews. The same structure — achievement, metric, method — maps directly onto the STAR framework you'll use when answering behavioural questions. Write your CV in XYZ, and your interview stories are already half-built.
What If You Don't Have Exact Numbers?
This is the most common reason people avoid writing results-focused bullets — and it's the wrong reason to hold back.
You don't need precise data. You can use:
Estimates: "reduced manual effort by approximately 3 hours per week"
Magnitude: "cut processing time from days to minutes"
Scale: "deployed across a team of 40+ engineers"
Qualitative with context: "improved onboarding clarity, reducing first-week support tickets"
Something is always better than nothing. If you're not sure of the exact figure, think back to feedback you received, dashboards you saw, or team updates you were part of. You know more than you think.
Learn more on this in our Behavioural Interviews module.
XYZ Examples by Role
Software Engineer
Before | After (XYZ) |
|---|---|
Wrote unit tests for backend services. | Increased test coverage from 45% to 87% by writing unit and integration tests for core backend services, reducing production bugs by 30%. |
Improved page load speed. | Reduced average page load time by 60% by implementing lazy loading and optimising image delivery via a CDN. |
Worked on API development. | Designed and shipped a RESTful API used by 3 internal teams, cutting integration time for new features by an estimated 2 weeks per release. |
Fixed bugs reported by the QA team. | Resolved 120+ critical bugs across two quarters, reducing open issue backlog by 40% and improving release stability. |
Refactored legacy code. | Refactored a 5-year-old monolithic service into modular components, reducing average deployment time from 45 minutes to under 10. |
Contributed to system design discussions. | Co-designed a microservices architecture for a high-traffic payments feature, supporting a 3x increase in transaction volume without downtime. |
Mentored junior engineers. | Mentored 2 junior engineers over 6 months through weekly code reviews and pair programming sessions, both of whom were promoted to mid-level within the year. |
Set up CI/CD pipelines. | Built a CI/CD pipeline from scratch using GitHub Actions, reducing manual deployment steps by 80% and cutting release cycle time from 2 days to 4 hours. |
Product Manager / Product Owner
Before | After (XYZ) |
|---|---|
Led a product redesign. | Led a redesign of the mobile onboarding flow, increasing 7-day retention by 18% and reducing drop-off at the first key action by 25%. |
Worked with engineering and design teams. | Coordinated a cross-functional team of 8 (engineering, design, data) to deliver a major feature 2 weeks ahead of schedule. |
Managed the product roadmap. | Defined and prioritised a 6-month roadmap across 3 product areas, aligning 4 stakeholder teams and reducing scope conflict by introducing a structured prioritisation framework. |
Ran user interviews. | Conducted 20+ user interviews that surfaced 3 critical pain points, directly informing a product pivot that improved NPS from 32 to 51 over two quarters. |
Improved conversion rates. | Increased free-to-paid conversion by 14% by identifying friction in the upgrade flow and working with design to simplify the pricing page. |
Launched a new feature. | Launched a real-time notifications feature to 500,000+ users, achieving a 40% adoption rate within the first 30 days. |
Wrote product specifications. | Wrote detailed PRDs for 12 features across two quarters, reducing back-and-forth between engineering and design by an estimated 30% per sprint. |
Analysed product metrics. | Used Mixpanel and SQL to identify a 35% drop-off at checkout step 3, leading to a targeted fix that recovered an estimated £80,000 in monthly revenue. |
Data Engineer / Data Analyst
Before | After (XYZ) |
|---|---|
Built data pipelines. | Designed and deployed an end-to-end ETL pipeline processing 5M+ records daily, reducing data latency from 6 hours to under 30 minutes. |
Improved reporting processes. | Automated 8 weekly reports using Python and Airflow, saving the analytics team approximately 12 hours of manual work per week. |
Worked with large datasets. | Processed and cleaned a dataset of 50M+ rows using PySpark, improving downstream model accuracy by 22%. |
Created dashboards for stakeholders. | Built a self-serve Tableau dashboard used by 6 business teams, reducing ad-hoc data requests to the engineering team by 60%. |
Optimised SQL queries. | Refactored 15 slow-running SQL queries, reducing average query execution time from 4 minutes to under 20 seconds. |
Supported data modelling work. | Designed a dimensional data model for a new product area, enabling the business to run revenue attribution analysis for the first time. |
Monitored data quality. | Implemented data quality checks across 3 critical pipelines using Great Expectations, catching 95% of anomalies before they reached production. |
Collaborated with data scientists. | Partnered with 2 data scientists to productionise an ML model, reducing inference time by 70% through feature engineering optimisations. |
Team Lead / Engineering Manager
Before | After (XYZ) |
|---|---|
Managed a team of engineers. | Led a team of 6 backend engineers across 2 time zones, maintaining a consistent sprint velocity and delivering all Q3 milestones on time. |
Improved team processes. | Introduced structured sprint retrospectives and async documentation practices, reducing repeated blockers by 40% over two quarters. |
Grew the engineering team. | Hired and onboarded 4 engineers in 3 months, reducing time-to-productivity for new joiners from 6 weeks to under 3. |
Delivered a major project. | Led delivery of a platform migration from a monolith to microservices, completed on schedule with zero downtime and a 50% improvement in system reliability. |
Supported team development. | Ran bi-weekly 1:1s and created individual development plans for all 5 direct reports; 3 were promoted within 12 months. |
Reduced production incidents. | Implemented an on-call rotation and post-incident review process, reducing P1 incidents by 55% over two quarters. |
Worked with product and design. | Facilitated weekly cross-functional syncs between engineering, product, and design, improving sprint planning accuracy and reducing mid-sprint scope changes by 30%. |
Managed stakeholder expectations. | Established a monthly engineering update for senior leadership, improving visibility of technical progress and reducing unplanned escalations. |
How to Apply This to Your Own CV
Go through each bullet point on your CV and ask yourself three questions:
What actually changed because of what I did? (X)
Can I put a number on it — or at least a scale? (Y)
What specifically did I do to make that happen? (Z)
If you can't answer all three, start with what you know and work backwards. Think about the feedback you received, the metrics your team tracked, or the problem that existed before you arrived. The result is almost always there — it just needs to be made visible.
One More Thing: This Works in Interviews Too
The XYZ structure isn't just for your CV. When you're answering behavioural questions in an English-language interview — "Tell me about a time you improved a process" or "Give an example of a technical challenge you solved" — the same logic applies.
Interviewers remember impact. Specific numbers and clear outcomes make your stories easier to follow, easier to remember, and much more convincing. If you've already written your CV using XYZ, your interview answers are already halfway there.
Ready to put this into practice? The Mockly English course walks you through how to structure your CV, build your story bank, and deliver strong answers in English — step by step.
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