I mystery-shopped 5 Lenskart stores. The demand is there; the leaks are in execution.

While interviewing for a Customer Excellence role at Lenskart, I was asked to study their stores and propose improvements. I didn't want to do it from a desk. So I ran a small mystery-shopping exercise: five stores, all visited after 6 PM, two in Gorakhpur (Tier-2) that I walked into myself, and three in Noida (Tier-1) covered by a second observer I briefed on what to look for. At one store I also spoke with the staff to understand what happens behind the counter.

This is what I found, and what I'd fix first. Five stores is a small sample, so read the findings as signals to test at scale, not verdicts.

The headline: demand isn't the problem

The business underneath is strong. At the Gorakhpur store where I spoke with staff:

• Footfall: 70–80 people on weekdays, 100–110 on weekends
• Sales: ₹60–70K on weekdays, ₹90K–1L on weekends
• Eye test to purchase: about 80%, when the test is actually completed
• Eye tests a day: 40–45 normally, down to 30 because one of the two testing rooms had been out of action for two weeks

People come in, and when the core experience works, they buy. The losses happen in between, and they are mostly about execution, not appetite.

What I saw across five stores

1. No one in charge. Four of five stores had no manager on the floor: transferred, at lunch, or no reason given. With no local owner, standards slip quietly.
2. No one says hello. None of the five stores greeted a customer within the first minute. The average was 5+ minutes of browsing alone, and twice we had to start the conversation ourselves.
3. The eye test breaks at the worst moment.
   • In Gorakhpur, one of two testing rooms had a dead screen for 2+ weeks, cutting capacity by about a third.
   • In one Noida store, a long wait with nothing to do: the observer left.
   • In another, the machine test was done, then the optometrist disappeared for 20 minutes: she left again.
   • In a third, a single-lens trial missed a real −0.75 prescription and recommended a blue-light lens instead. No sale.
4. Product knowledge gaps. Staff mixed up anti-glare with infrared-blocking coatings and couldn't explain lens options with confidence.
5. Technology on display, not in use. Virtual try-on was ignored ("customers prefer physical"), face scanning put some customers off over privacy, and a broken device sat behind an unresolved ticket.
6. Support that doesn't reach the store. Staff described inventory that didn't match the system, tickets closed without a fix, workarounds for broken QR check-in, and no access to customer data to follow up with unhappy buyers.

Where the journey leaks

Mapping the store visit step by step, against what should happen, shows where value is lost:

Stage	What should happen	What actually happened	Time
Entry	Greeted within 60 seconds, intent captured	No greeting, no check-in	5+ min
Frame discovery	Guided selection with try-on tools	Self-browse, tools unused	10–15 min
Waiting for the eye test	Pick frames while you wait	Nothing to do, so people leave	15–25 min
Eye test	Machine reading, manual check, prescription	Shortcuts and incomplete tests	10–20 min
Lens selection	Advice on options and pricing	Transactional, thin knowledge	5–10 min

A rough funnel from what I saw, to be validated with store data:

• Walk-in → engaged by staff: about 60–70%. The rest browse and leave.
• Engaged → asks for an eye test: about 40–50%.
• Test requested → completed: about 80–85%. Waits and gaps lose the rest.
• Test completed → purchase: about 80%.

That puts store conversion at roughly 25–30% of walk-ins. With the leaks fixed, 40–45% looks reachable.

Why it happens

• An accountability gap. No manager on the floor means nobody owns greeting, equipment or coaching in the moment.
• Knowing the SOP isn't following it. Staff had a month of onboarding, yet the basics were skipped.
• Tech that's deployed but not adopted, because of training gaps, privacy worries and unreliable hardware.
• Head office and stores out of sync: unresolved tickets, policy changes without training, no access to customer data.
• Inconsistent optometry, which puts the single most valuable step, the 80% test-to-purchase conversion, at risk.

What I'd fix, in order

I ranked each lever by impact on conversion, effort to implement, and whether it scales across 2,300 stores. The lift ranges are estimates to test in a pilot, not promises.

Priority	Lever	What it involves	Estimated lift
P0	Protect eye-test capacity	48-hour equipment repair SLA with auto-escalation, three-step test compliance checks, frame selection during the wait	+12–15%
P1	First 60 seconds	Mandatory greeting and intent check; a soft "free eye test while you browse?" for passive customers	+5–7%
P1	Manager accountability	Manager coverage SLA, a daily checklist, KPIs on equipment uptime, engagement and test completion	+6–9%
P2	Optometrist certification	Quarterly re-certification and mystery-shopping audits on test rigour	+3–5%
P2	Eye-health signage	"1 in 3 adults have an undiagnosed vision issue. Free 15-minute eye test."	+2–4%

The P0 lever comes first because it protects the highest-converting step. In Gorakhpur alone, the dead testing room was costing an estimated ₹35K a day. Manager accountability is the lever that makes the others stick.

If store conversion moved from 25–30% to 40–45%, that's roughly 10–25 more orders a day per store. At a ₹3,000 average ticket, that's ₹30–75K more revenue per store per day.

What I took away

The pattern is one I've seen in every business I've run: when demand is strong and results are uneven, the problem is usually operating discipline, not the market. The fixes are rarely glamorous. Ownership on the floor, service standards that are measured rather than just trained, and a support loop that closes tickets in hours instead of weeks.

It's also where AI now earns its place in operations. Equipment tickets that escalate themselves, conversion dashboards per store, and automated follow-ups with unhappy customers are exactly the systems I build today. Same operator instinct, more leverage.