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Product managers are expected to make decisions using data, but simply knowing terms like retention, conversion and churn is not enough. Recruiters want to see whether you can select the right metric, understand what changed and recommend a sensible product action.
That is where a KPI tracking project becomes valuable. It gives aspiring PMs a practical way to demonstrate product thinking, analytical ability and business understanding, even without previous product management experience.
This guide covers realistic KPI tracking project ideas for PMs, the metrics to include, tools to use and insights you can present in your portfolio.
What Is KPI Tracking in Product Management?
A Key Performance Indicator, or KPI, is a measurable value that shows whether a product is moving towards a defined objective.
For example, if a food delivery app wants more customers to order again, it may track 30-day repeat order rate. Supporting metrics could include delivery time, cancellation rate, customer complaints and order success rate.
KPI tracking involves more than displaying numbers on a dashboard. A product manager must understand what each number represents, why it changed and what the product team should do next.
Why KPI Tracking Matters for Product Managers
Product teams make dozens of decisions about features, pricing, onboarding, user experience and growth. Without relevant KPIs, they cannot tell whether those decisions are creating value.
Good KPI tracking helps product managers:
- Measure whether a feature is working
- Identify where users face difficulties
- Understand customer behaviour
- Monitor product and business health
- Prioritise problems using evidence
- Evaluate experiments and releases
- Communicate progress to stakeholders
The goal is not to track every available metric. The goal is to track the few metrics that support an important product decision.
Why Should Aspiring PMs Build KPI Projects?
Aspiring product managers often face a difficult situation. Companies want practical experience, but candidates cannot gain that experience without first getting a role.
A self-created KPI project helps reduce this gap. It gives you something concrete to present instead of only listing courses and certifications.
A well-structured project can demonstrate that you know how to:
- Define a product problem
- Map the user journey
- Choose a North Star Metric
- Build a measurement framework
- Analyse user behaviour
- Identify possible causes
- Prioritise improvements
- Measure the success of a solution
This is useful for Associate Product Manager, Product Analyst, Growth Product Manager and Product Operations interviews.
What Should a KPI Tracking Project Contain?
A good project should begin with a problem rather than a dashboard.
For example, do not start with, “I want to build an e-commerce dashboard.” Start with, “A large percentage of customers add products to their carts but do not complete their purchases.”
Your project should include the following sections.
1. Product overview: Explain what the product does, who uses it and how the company earns money.
2. Problem statement: Describe the user or business problem you want to investigate.
3. Product objective: State the desired outcome in measurable terms.
4. North Star Metric: Choose one metric that represents meaningful value delivered to users.
5. Supporting KPIs: Select metrics that explain changes in the primary outcome.
6. Data and assumptions: Explain where the data came from and whether any figures were simulated.
7. Dashboard and analysis: Show trends, comparisons, funnels, cohorts and user segments.
8. Recommendations: Explain which product changes you would prioritise and why.
9. Measurement plan: Describe how you would determine whether the proposed solution worked.
15 KPI Tracking Project Ideas for PMs
1. E-Commerce Conversion Funnel Project
An e-commerce conversion project tracks how users move from visiting a website to completing a purchase.
It is suitable for beginners because the customer journey is familiar. However, it still allows you to demonstrate funnel analysis, segmentation and revenue-focused decision-making.
Product problem
Customers are viewing products and adding them to their carts, but many are leaving before completing payment.
KPIs to track
- Product page view rate
- Add-to-cart rate
- Cart-to-checkout rate
- Checkout completion rate
- Purchase conversion rate
- Cart abandonment rate
- Average order value
- Revenue per visitor
- Repeat purchase rate
Analysis to perform
Compare the funnel across desktop and mobile users. You can also segment customers by acquisition channel, location, product category and new versus returning users.
If mobile users have a healthy add-to-cart rate but a weak payment completion rate, the issue may be connected to page speed, form length or payment options.
Tools: Excel, SQL, Power BI, Tableau, Google Analytics and Figma.
2. SaaS Onboarding and Activation Project
Activation happens when a new user experiences the product’s core value for the first time.
For project-management software, activation might mean creating a project, adding tasks and inviting a teammate. For an email-marketing tool, it might mean successfully sending the first campaign.
Product problem
Many users create free accounts but do not complete the actions required to understand the product’s value.
KPIs to track
- Registration completion rate
- Onboarding completion rate
- Activation rate
- Time to first value
- Core feature adoption
- Team invitation rate
- Seven-day retention
- Trial-to-paid conversion rate
Analysis to perform
Compare activated and non-activated users. Identify which early actions are most commonly completed by customers who later become active or paid users.
Do not assume that every onboarding step is equally important. Some steps may delay users without increasing their probability of success.
Tools: Mixpanel, Amplitude, Excel, SQL, Power BI, Figma and Miro.
3. Mobile App Retention Dashboard
A retention dashboard tracks whether people continue using an app after downloading or registering.
This project is useful because many apps can generate downloads through advertising but struggle to build lasting user habits.
Product problem
New users open the app during the first week but most stop returning within 30 days.
KPIs to track
- Day 1 retention
- Day 7 retention
- Day 30 retention
- Daily active users
- Monthly active users
- DAU-to-MAU ratio
- Session frequency
- Average session duration
- Feature adoption rate
- User churn rate
Analysis to perform
Create retention cohorts based on each user’s registration week. Compare retention across acquisition channels, devices, locations and onboarding paths.
A cohort table can show whether a product change improved long-term behaviour or only produced a temporary increase in activity.
Tools: Firebase Analytics, Mixpanel, Amplitude, SQL, Excel, Power BI and Tableau.
4. Food Delivery Performance Dashboard
Food delivery platforms must coordinate customers, restaurants and delivery partners. A problem affecting one group can quickly damage the experience for the other two.
This makes food delivery a strong project for demonstrating marketplace and operational thinking.
Product problem
Order cancellations and delivery delays are increasing during peak hours.
KPIs to track
- Order acceptance rate
- Restaurant preparation time
- Delivery partner assignment time
- Average delivery time
- On-time delivery rate
- Cancellation rate
- Failed delivery rate
- Complaint rate
- Repeat order rate
- Customer satisfaction score
Analysis to perform
Compare performance by restaurant, location, delivery distance, weekday and time slot.
For example, cancellation rates may be highest in locations where delivery partners need to travel long distances to collect orders.
Tools: SQL, Excel, Power BI, Tableau, mapping tools and Figma.
5. Subscription Churn Tracking Project
Subscription businesses depend on customers continuing to pay over time. Strong acquisition cannot compensate for poor retention forever.
This project works well for streaming platforms, learning applications, fitness apps and SaaS companies.
Product problem
A growing percentage of customers are cancelling during the first three months of their subscription.
KPIs to track
- Customer churn rate
- Revenue churn rate
- Renewal rate
- Monthly recurring revenue
- Annual recurring revenue
- Customer lifetime value
- Customer acquisition cost
- Trial-to-paid conversion
- Failed payment rate
- Cancellation reasons
Analysis to perform
Study churn by pricing plan, customer tenure, feature usage, acquisition channel and payment method.
You may find that customers who do not use a key feature during their first month are more likely to cancel. That would make feature discovery a possible retention driver.
Tools: Excel, SQL, Power BI, Tableau, Mixpanel and subscription analytics tools.
6. Fintech KYC and First-Transaction Funnel
Fintech onboarding usually includes account creation, identity verification, bank linking and the first financial transaction.
A strong project should balance user convenience with security, risk and regulatory requirements.
Product problem
Users begin the registration process but leave before completing KYC or making their first transaction.
KPIs to track
- Registration completion rate
- KYC initiation rate
- KYC completion rate
- KYC rejection rate
- Average verification time
- Bank-linking success rate
- First transaction rate
- Time to first transaction
- Transaction failure rate
- Support contact rate
Analysis to perform
Build a funnel from registration to the first successful transaction. Compare drop-offs by device, verification method, document type, bank and acquisition source.
A poor KYC completion rate cannot simply be solved by removing necessary checks. The product manager must improve clarity without weakening compliance.
Tools: SQL, Excel, Power BI, Tableau, Figma and product analytics tools.
7. EdTech Learner Progress Dashboard
An education platform should not measure success only through registrations and video views. Those metrics do not prove that students are learning.
A more useful project measures whether learners complete meaningful activities and achieve expected outcomes.
Product problem
A large number of students enrol in courses, but only a small percentage complete them.
KPIs to track
- Enrolment rate
- Lesson start rate
- Lesson completion rate
- Course completion rate
- Weekly active learners
- Quiz participation rate
- Assessment score
- Assignment submission rate
- Learning streak
- Certification rate
Analysis to perform
Compare completion across courses, modules, instructors and lesson formats. Identify the lessons where the largest drop-offs occur.
If students repeatedly leave during long theoretical modules, the team should investigate difficulty, presentation and relevance before removing the material.
Tools: Excel, SQL, Power BI, Tableau, Moodle reports and learning analytics tools.
8. Digital Payment Success Dashboard
Payment failures can reduce revenue and damage customer trust. They may occur because of bank issues, gateway performance, network problems or confusing user flows.
Product problem
Customers are starting transactions but a high percentage of payments are failing or being abandoned.
KPIs to track
- Payment initiation rate
- Payment success rate
- Payment failure rate
- Payment abandonment rate
- Retry rate
- Retry success rate
- Average processing time
- Refund initiation time
- Refund completion time
- Success rate by payment method
Analysis to perform
Compare performance across UPI, cards, net banking and wallets. You can also examine results by bank, gateway, device and time of day.
If a particular gateway performs poorly during peak periods, the business may need smarter transaction routing.
Tools: SQL, Excel, Power BI, Tableau and transaction-event data.
9. Customer Support KPI Dashboard
Support data can reveal product problems that may not appear in standard usage dashboards.
For example, a sudden increase in password-reset tickets may indicate that the recovery process is confusing. Increasing the support team may treat the symptom without solving the underlying product issue.
Product problem
Customers are waiting too long for resolutions, while the same issues continue generating repeated tickets.
KPIs to track
- Total ticket volume
- First-response time
- Average resolution time
- First-contact resolution rate
- Ticket reopen rate
- Escalation rate
- Customer satisfaction score
- Self-service resolution rate
- Tickets per active user
- Tickets by issue category
Analysis to perform
Identify which features or user journeys generate the most support requests. Compare ticket volume before and after product releases.
Tools: Zendesk, Freshdesk, Intercom, Excel, SQL, Power BI and Tableau.
10. New Feature Adoption Project
Launching a feature does not mean that customers are using it. A feature adoption dashboard shows whether eligible users discover, try and repeatedly use the feature.
Product problem
A newly launched feature has low usage despite being requested during customer research.
KPIs to track
- Feature visibility rate
- Feature discovery rate
- First-use rate
- Adoption rate
- Repeat-use rate
- Frequency of use
- Time to first use
- Task completion rate
- Retention among adopters
- Feature-related support tickets
Analysis to perform
Compare users who adopted the feature with eligible users who did not. Study usage by plan, customer segment and user experience level.
Be careful with causation. If feature users have better retention, it does not automatically mean the feature created that improvement. Highly engaged users may simply be more likely to find it.
Tools: Mixpanel, Amplitude, SQL, Power BI, Tableau and Figma.
11. Ride-Hailing Marketplace Dashboard
Ride-hailing platforms need enough drivers to meet passenger demand without leaving drivers inactive for long periods.
Improving only the passenger or driver experience can create problems for the other side of the marketplace.
Product problem
Passengers face long waiting times during peak hours, while ride cancellations are increasing.
KPIs to track
- Ride request volume
- Driver acceptance rate
- Driver cancellation rate
- Passenger cancellation rate
- Ride completion rate
- Estimated pickup time
- Actual pickup time
- Driver utilisation rate
- Requests per available driver
- Repeat rider rate
Analysis to perform
Compare supply and demand by area, day and hour. Identify locations where passenger demand exceeds the number of available drivers.
Tools: SQL, Excel, Power BI, Tableau and geospatial mapping tools.
12. Job Portal Application Funnel
A job platform must help candidates find suitable roles while helping employers receive relevant applications.
Measuring only application volume could encourage users to apply everywhere, reducing application quality for recruiters.
Product problem
Candidates view jobs but leave before completing their applications.
KPIs to track
- Search-to-job-view rate
- Job-detail view rate
- Apply-click rate
- Application completion rate
- Application abandonment rate
- Time to complete application
- Recruiter response rate
- Interview conversion rate
- Saved jobs per user
- Relevant application rate
Analysis to perform
Compare application completion by device, job category, number of form fields and application length.
You can also examine whether easier applications generate more recruiter responses or simply increase low-quality submissions.
Tools: SQL, Excel, Power BI, Tableau, Mixpanel and Figma.
13. OTT Content Engagement Dashboard
Streaming products must help users find content they enjoy without overwhelming them with too many options.
Watch time matters, but it should not be used alone. A customer who watches frequently but cancels next month is different from one who consistently discovers satisfying content.
Product problem
Users browse the content library for a long time but frequently leave without watching anything.
KPIs to track
- Browse-to-play conversion
- Search success rate
- Time to first play
- Content completion rate
- Watch time per user
- Number of active viewing days
- Recommendation click rate
- Subscription renewal rate
- Churn rate
- Content satisfaction score
Analysis to perform
Compare behaviour by content category, subscription plan, device and new versus returning users.
Tools: SQL, Python, Excel, Power BI, Tableau and product analytics platforms.
14. Healthcare Appointment Completion Project
Healthcare platforms must make booking convenient while protecting patient information and ensuring access to appropriate care.
A meaningful dashboard should measure completed consultations rather than only appointment bookings.
Product problem
Patients book appointments but frequently cancel or fail to attend them.
KPIs to track
- Search-to-booking conversion
- Appointment completion rate
- Patient cancellation rate
- Doctor cancellation rate
- No-show rate
- Average waiting time
- Rescheduling rate
- Repeat consultation rate
- Patient satisfaction score
- Support contact rate
Analysis to perform
Study cancellations by appointment type, booking lead time, time slot, speciality and reminder status.
Tools: Excel, SQL, Power BI, Tableau, Figma and scheduling data.
15. Online Grocery Fulfilment Dashboard
Online grocery platforms manage inventory availability, substitutions, delivery slots and time-sensitive fulfilment.
A customer may complete an order but still have a poor experience if important items are unavailable or replaced without permission.
Product problem
Orders are being delivered, but customers report missing items, unwanted substitutions and delays.
KPIs to track
- Order fulfilment rate
- Item availability rate
- Substitution rate
- Substitution acceptance rate
- Missing-item rate
- On-time delivery rate
- Refund rate
- Complaint rate
- Average basket value
- Repeat order rate
Analysis to perform
Compare fulfilment by store, product category, delivery slot and location. Identify whether specific inventory categories generate most substitutions.
Tools: SQL, Excel, Power BI, Tableau and inventory data.
How to Present the Project in Your PM Portfolio
Your project should read like a product case study, not a collection of screenshots.
Use this structure:
- Product overview
- Target users
- Problem statement
- Product objective
- North Star Metric
- Supporting KPIs
- Data source and assumptions
- Dashboard
- Key findings
- Root-cause hypotheses
- Product recommendations
- Prioritisation method
- Experiment plan
- Limitations
Keep detailed SQL queries, formulas and supporting calculations in an appendix. The main case study should focus on the decision-making process.
How KPI Projects Help in PM Interviews
A strong KPI project provides examples you can use while answering product management interview questions.
It can help you answer:
- How would you measure feature success?
- Which metric would you choose as a North Star?
- What would you do if retention declined?
- How would you investigate lower conversion?
- Which guardrail metrics would you use?
- How would you prioritise product improvements?
- How would you differentiate correlation from causation?
Interviewers may question your metric selection or assumptions. Be prepared to explain your reasoning and acknowledge missing information.
