Nobody owned what happened after a customer clicked Buy. Bad tech was leading to a disastrous consumer experience that was eating up the EBITDA.
Six systems. None of them talking. Every answer trapped inside.
The Experiment
Order confirmed! Delivery between Aug 14 – Aug 18 🎊
a five-day "window" — be home on all of them, any day could be the day
Update: your order will arrive on Aug 19 (outside the window)
Track your order here → bit.ly/track-id
opens a page with a different date entirely — and no real tracking
…called support. "I'll email you back." (they didn't)
Engagement
12 msgs
Window
5 days
Accuracy
0%
Order confirmed — arriving Thursday, by 9 PM
Out for delivery — arriving by 6 PM today
Delivered — a day early
Optimized workflows
Precision
1 date
Efficiency
Early
Friction
0 calls
By eliminating the "anxiety window" and providing deterministic updates, we reduce support ticket volume by 42% and increase repeat purchase intent by 2.4x. This isn't just shipping; it's trust engineering.
The Reports
The dashboards celebrated; the customers kept calling. I saw vanity metrics live and at scale.
Logistics reported
98% delivery adherence
Actually
3,000 open tickets nobody was counting
Support reported
90% CSAT
Actually
3.5 calls per unit sold — surveys only reached the happy endings
Leadership heard
NPS through the roof
Actually
Nobody owned post-purchase — a black box, end to end
That black box became my job.
The First Move
I researched and understood every action, the effect, the cause — and documented it for the first time in the company's history. That map led to where the money was leaking, which two numbers mattered, and the roadmap required.
North Star · Customer
Get this one number down, and everything downstream — cost, trust, CSAT — follows it.
North Star · Business
Headcount was the biggest cost driver. Every process redesigned to need fewer hands — then, wherever possible, none.
The Roadmap
AI on chaos is just faster chaos.
Unified data, defined flows, click-button automations.
Product Support, APMs, product analysts
If a human agent can read the right data and act on it, an AI agent can too.
The Build
Each layer created the data that revealed the next problem. Click a layer to explore it — built bottom-up, like any OS.
Layer 01 · Foundation
Order data lived in four systems that didn't talk. I unified their webhooks and APIs into one master database — a single source of truth that everything else would stand on.
↑ auto-playing — click any layer to pause and explore
The Range
🎯Strategy & Roadmap
Found the problems, proved they mattered, owned the prioritisation.
📄Every PRD
Wrote every requirement doc, end to end.
✏️Every Design
Drew every screen and flow myself.
🔄Scrum Master + PO
Ran the sprints and owned the backlog.
🧪QA
Tested every release before it shipped.
🎓Trainer of 1,000+
800 store staff + 200 call agents, trained personally.
🛡️Risk & Fraud
Found the fraud gaps, closed them, dropped the policies that didn't serve us.
🚀Then: the team
Built the Product Support team, APMs, and analysts who run it now.
I was the first person on this team — so everything it needed was mine to do, until I built the people it could be handed to.
The Impact
total post-purchase
cost saved
Calls per unit sold
Time to process a return
Open tickets (exception cases only)
Returns-to-origin (RTO)
CSAT — real this time, not the vanity number
Accuracy of the delivery dates we promise
Two years of work · Still compounding
I joined to fix post-purchase. I ended up building the operating system it runs on — and the team that keeps it running. All of it started with one bad delivery experience I couldn't stop thinking about.
Sharanya Chaturvedi · Senior Product Manager · The Sleep Company
I consult D2C brands on how to reduce cost while improving customer experience.