SaaS

End‑to‑End

0→1

+2k Users

Subscription management automation & bulk actions tools

Role:

Solo Product designer

Team:

2 developers

Duration:

2 months from concept to MVP

Platform:

Web Application (desktop only)

TL;DR

Context

Kurabu is a club management platform that supports the day‑to‑day administrative work of sports clubs.

This project tackles subscription management: the most time-consuming task for club admins and their top source of support requests.

What I did

Designed a rule‑based solution that adapts to each club’s pricing structure, mixing automation and bulk actions. Created so that a small, mainly junior dev team can build and maintain it.

Business

Users

Client type retention
Advantage against our competitors
More dev time = new features faster
Feature that weighs in big client acquisition

Less tedious work

-3 support requests per new client

-900 seasonal support requests

Target: -70% invoicing errors
Target: ~15 hrs saved per week

Solution’s impact

Discovery

Starting points

Survey
of club admins
- → who they are
- → how often subscriptions change
- → what works, what doesn't
- → impact on their members
Interview
of sales and dev teams
- → how admin friction affects them
- → extra user insights
- → technical feasibility, build time, reliability and maintenance
Pricing Analysis
across clubs
- → common patterns
- → edge cases
Competitor analysis
of our direct & indirect competitors
- → how they solve this issue
- → how other industries solve similar problems
Audit
of our current product
- → gaps
- → what we do well
- → dependencies
- → advantage a solution would bring against competitors

Insights

Club pain points turned out to be the dev team’s bottleneck

Club pain points result in repetitive sales requests like bulk member additions, setting plan end dates, or editing plan frequency, pulling the dev team away from building new features and slowing overall product development. At season start alone, this reached up to 900 update requests across all clubs.

What they say

“I spend days every month just making sure our teenage players are moved to the adult pricing tier.”

Clubs

“Most support requests involve plan management, typically bulk-adding members or setting end dates. It’s time-consuming and tedious work.”

dev team

Common patterns

100%

age-based pricing system

100% of clubs have a pricing change at age 18, making it a major pain point

65.5%

No exceptions

Predefined age groups are applied across all of the club’s sport categories

34.5%

with exceptions

Categories don’t always follow the same age group breakdowns

None of our competitors offers a satisfying solution.

Clubs’ most common pricing structure

Business

Users

Need more high value clients
Need a key advantage against our competitors
Dev team is drowning in support requests

Hours of

tedious work

subscriptions management is time consuming and error prone
Dependent on support requests for bulk updates
Scattered tools

Challenge

Automations had to be customisable

The challenge was to design a system flexible enough to support diverse club needs, while remaining realistic for a small engineering team to build and maintain.

Automating everything at once wasn't possible

There were many opportunities for automation, but automating everything at once would have taken too long. Clients and investors expected visible progress fast.

Decision

Key Decisions

→ Kept the MVP focused on age‑group rules
→ Added bulk actions for common tasks
→ Improved the subscription area first (“Plans” in the product), so it could support automation.

Choosing automations over AI

Pricing variations across clubs follow recognisable patterns that can be modelled as configurable rules.

AI would add unpredictability to a system where every outcome needs to be trustworthy and auditable. A rule‑based system with anomaly detection was the right fit, and far easier for a small, mainly junior development team to build and maintain.

Trustworthy & Auditable

Financial and subscription changes must be transparent and easily verifiable by club admins.

Scalable Architecture

The logic must be designed so new rule types (like document validation) can be added later without rewriting the core engine.

Solution & results

Preparing the product for automations

Before implementing the feature, I redesigned key parts of the subscription management area (“Plans” in the product). This prepared the UI for automation and gave the team the chance to rebuild those parts in React, a requirement for new features.

BeforeAfter

Designing a system that can scale

In close collaboration with engineering, leadership, and key client clubs, I defined how rules should behave, how edge cases are handled, and what could realistically be automated.

Before: Admins spent days every month moving members between plans by hand.

15 hrs

Saved per week

Targeted reduction in club administrative workload

-70%

Invoicing errors

Targeted reduction in billing mistakes due to manual entry

Preventing invalid rule creation through real-time setup validation

Real-time inline validation catches logical errors (like overlapping age limits) during creation so broken rules can't be created.

Handling member exceptions before and after rule runs

Pre-run checks for edge cases: The system flags members who don't fit the new criteria, letting admins resolve them manually or apply a suggested auto-fix before the rule goes live.

Automatic skipping during runs: If unexpected errors occur during execution, the system skips those members so the rest of the run succeeds, logging them in a post-run report for later review.

Protecting manual edits: Warns admins when a manual profile change affects active rules, making downstream impacts clear before anything is saved.

Before: Required manually cross-referencing Excel sheets.

Bulk actions for what automations can't cover

Reduce repetitive manual work or support requests by allowing admins to apply an action to all or selected members at once.

Before: Bulk changes ended up as support requests.

-900

Support requests

eliminated seasonal manual update tickets (shipped)

Enabling change of a subscription’s billing frequency before 1st invoice

New clubs often picked the wrong billing frequency during setup and needed a developer to fix it.

-3

Support requests per new client

One whole request type gone: wrong billing frequency at setup (shipped)

Validation

Validated with investors and clients, tracked by adoption and support request load

The feature was validated through investor pitches and client demos.

The projected impact was estimated by the sales team for investor and client pitches. Success would be tracked through:

Ratio of subscriptions managed via automation vs bulk action vs manually
Ratio of clubs using automation vs bulk actions only vs neither, across club sizes
Number of subscription management support requests, and time spent on them

80%

Target adoption

Clubs with 1,000+ members using automation or bulk actions

−70%

Target support request reduction

Subscription support requests and time spent on them