Designed the online enrolment for a gold savings scheme
Reliance Jewels is a jewellery retail chain. It runs a savings scheme where you pay a set amount every month and later swap the pot for jewellery. Until this project the only way to join was to walk into a shop. I put it online, then built the dashboard that caught me out. The scheme now brings in ₹80 Cr a year, and the first thing the dashboard showed me was 91% of people quitting on a screen I had drawn.
- What it is
- A monthly savings plan you later spend on jewellery
- What I owned
- The enrolment flow and the dashboard, sole designer
- Who it was for
- Shoppers on their phones, then leadership and the support team
- What changed
- A channel that did not exist now earns ₹80 Cr a year
- Problem
- The scheme only existed inside stores. If you could not get to one on a working day it may as well not have existed. And once it was online and earning, nobody could say how it was doing: the numbers lived in four different systems.
- My move
- Built the online enrolment from nothing, ordering the steps around what a person worries about rather than what the systems wanted. Then turned the 7 questions leadership kept asking into the 7 cards of a dashboard, instead of shipping a data dump.
- Outcome
- A channel that did not exist now does ₹80 Cr a year, and 4 fragmented sources became one view. Three other internal tools now reuse the chart components I built along the way.
- Learning
- The dashboard caught my own worst screen a launch too late. Test the step you think is easy, and instrument the measurement tool itself from day one.
The quieter of the two ways the business earns
A single purchase is a transaction. A savings scheme is a relationship with a diary entry in it.
Reliance Jewels earns two ways. The website sells jewellery outright: high ticket, one purchase at a time. The Gold Savings Scheme is the quieter engine. A customer pays an amount they can actually afford each month, and later walks out with jewellery worth the whole pot. It lowers the price of getting started and it brings the same person back to the counter, month after month.
That is the side worth protecting, and it is the side that had the least built around it.
How Reliance Jewels earns
E-commerce
High-ticket jewellery, bought outright. One purchase, one customer, one day.
Gold Savings Scheme
This caseMonthly instalments redeemed for jewellery later. Recurring revenue, repeat relationships.
You could only join by walking into a shop
There was nothing to redesign. A person now had to be talked into a savings product by screens, not a salesperson.
To join the scheme you went to a Reliance Jewels store. A salesperson took you through the durations, you filled in a form, you paid the first instalment at the counter. Then you came back and paid again, month after month, until the scheme matured.
That works if there is a store near you and if you can reach it on a working day. For everybody else the scheme may as well not have existed. My brief was to build the online version, and there was nothing to redesign, because online there was nothing.
Which is a harder problem than it sounds. Someone who had never met the salesperson now had to understand a savings product, prove who they are to the government's satisfaction, sign a legal document and hand over money. On a phone. On their own.
In the shop the salesperson carries the trust. A person in front of you says the scheme is real, answers the awkward question about what happens if you miss a month, and watches you sign. Online there is nobody, so the screens have to do that job. I ordered the flow the way a person's worries actually arrive, which is not the order the backend systems would have picked.
Two of the five steps were never going to be mine
A savings product in India has to check your identity against a government ID, and it has to take a legal signature. Ours does the first through DigiLocker, the government's own document wallet, and the second through JioSign. Both are somebody else's page, a different shade of blue to ours, and neither is a screen I could design. What I could do was stop them feeling like the site had broken. Before each jump the screen names who you are about to be sent to, shows their logo and says what to keep handy, so the unfamiliar page arrives as a step you were warned about rather than a redirect you did not ask for.
It went live, and the channel now brings in ₹80 Cr a year. End to end, 7.1% of everyone who tapped Enrol Now came out the other side having paid.
On its own 7.1% reads like a broken funnel, so it needs the rest of the sentence. There was no online enrolment before this, so the baseline is zero, not some earlier number I improved on. And this is not a one-tap purchase. Somewhere in the middle a person is asked for their Aadhaar, India's national ID, and a signature on a commitment that runs for months. 7.1% of them finished anyway.
Then the question changed. It stopped being can people do this on a phone and became how is this actually doing. Nobody could answer that one.
Nobody lacked data. They lacked the same view of it.
The realisation that set the direction
The data existed. The shared view did not.
Four teams held four half-pictures, so decisions went to whoever spoke last.
Every number about the scheme already existed somewhere. Product saw part of it in Google Analytics, the backend systems held the payments, marketing tracked its own campaigns and customer support dealt with missed instalments in a spreadsheet of their own. Four teams, four half-pictures, no shared one.
So roadmap discussions ran on whoever remembered the loudest anecdote, drop-offs turned up weeks late and money quietly leaked through failed payments nobody was watching.
Before · four places to look
Google Analytics
funnels, traffic
Backend systems
payments, schemes
Marketing
campaign numbers
Customer support
missed payments
One view · the 7 questions answered
After · one place to decide
The spec was a list of questions
I started from the questions leadership kept asking, not the metrics that happened to be available.
Before opening Figma I sat with the PM and made a list. Not of the metrics we could pull, but of the questions that kept coming up in reviews and never got a clean answer. What did we earn this month, and is that up or down? Where exactly do people abandon enrolment? Who has stopped paying, and why? Seven questions kept returning, so seven became the spec. Every card on the screen answers one of them, and anything that answers none of them stayed off.
How much did we grow month over month?
→ Total revenue and month-on-month growth
Where are customers dropping off?
→ Funnel drop-off analysis
Are instalments being paid on time?
→ Payment status tracker
Which categories are redemptions flowing into?
→ Redemption categories
Which scheme durations are most adopted?
→ Duration distribution
What is the ratio of new versus existing customers?
→ Retention ratio
Why are payments failing?
→ Failure reason logs
The goal was clarity, not completeness.
The calls that made it get used
Ship only what changes decisions, colour only what deserves attention, and give every number a next move.
Adoption over complexity
There were fancier options on the table: predictive models, cohort deep-dives, forecasting. We deferred all of it. Leadership opens a dashboard between two meetings, and a first screen that needs a tutorial stops getting opened. A dashboard nobody opens changes nobody's mind, so the first release shipped the seven answers and nothing else.
- Revenue, and whether it is up or down
- Where people abandon enrolment
- Who has stopped paying
- What people redeem, and for how long they save
- Predictive models
- Cohort deep-dives
- Revenue forecasting
Gold for data. Red only for bad news.
The brand's primary colour is red, and the obvious move was to chart in brand colours. On a money product red reads as loss before it reads as brand. So every chart runs on the monochrome gold secondary palette instead, and the dashboard stays calm at a glance. Red appears in one place only, the payment failure chart, where the news genuinely is bad. When something turns red on this screen, it has earned it.
Every number ends in an action
A stat that changes nobody's Tuesday is decoration. So each view is built around what a team does next. The funnel names the exact step where people abandon enrolment, which tells marketing where to intervene. Overdue instalments are grouped by how late they are, so the support team works the riskiest bucket first instead of scanning a flat list. And every bucket exports straight to a call sheet.
123- 1Overdue instalments grouped by how late they are, with the amount pending in each bucket.
- 2Status says where each payment stalled: the identity check, the signature or the payment itself.
- 3Export List turns the bucket into a call sheet for the day.
Time works the way leadership asks
Nobody in a review asks for the eighteenth of January to the tenth of February. They ask what last month looked like, or this quarter so far. The date picker leads with exactly those phrases, presets first, keeping the custom calendar one step behind for the rare precise pull.
The design system had no way to show data. So I built one.
- No chart of any kind
- No way to pick a date range
- Nothing that held a dense table
- Charts small enough to reuse anywhere
- On the type and colour already in the system
- Documented, so the next team takes them off the shelf
The funnel card's first finding was about my own screen
91% never got past the step I would have called the easy one.
The funnel card was the one I had argued hardest for. It was also the one that came back for me.
Of everyone who tapped Enrol Now, 91% never got past the first screen. Not the KYC, not the signature, not the payment. The step where you pick a duration and type an amount.
I had drawn that page with the salesperson still in my head. Pick a duration, pick an amount, easy. Standing in a shop with someone explaining it, it is easy. On a phone, on your own, it is a sum you have to do before you know whether you even want the thing, and you are being asked to do it by a company you have not decided to trust yet. Everything below it is worse: a nominee, and a question about whether you happen to be standing in a store right now.
So I went back into that one step and rebuilt it around what the funnel and the follow-up research showed. The rebuilt version has not had its second read yet.
1234- 1Four steps of commitment on show before you have chosen anything.
- 2Pick a duration. In the shop this is the bit the salesperson talks you through.
- 3Then type an amount, with nothing on screen doing the maths alongside you.
- 4Nominee details, asked before the person has committed to a thing.
A channel that did not exist, and one place to watch it
Three other internal tools adopted the chart components without being asked to. That's the proof I trust most.
- You joined by walking into a shop
- Four teams held four half-pictures
- Decisions went to whoever spoke last
- Anyone with a phone can join
- One view everybody argues from
- Missed payments surface the same week
From inside the team I watched roadmap debates start from the funnel view instead of opinion. I saw that shift, I did not measure it.
What I'd do differently
Test the step you think is easy, and instrument the measurement tool itself from day one.
Two things, and the first one stings more. The 91% was not a discovery. It had been sitting in the scheme-selection screen since the day it shipped, and it took a dashboard built later to make anyone go and look. That page should have been put in front of a few strangers with a phone before a line of it was built.
The second is smaller and more awkward for someone who builds analytics. This dashboard has no instrumentation of its own: which cards get opened, by whom, before which decision, none of it is recorded. Usage analytics on the analytics would have turned the shift I watched into a number I could show. It is the first thing I would wire in next time.