A store can have healthy footfall, the right merchandise, and technology in place, yet still struggle to convert customers. The missing piece may not be another tool. It may be understanding what is happening on the floor at the exact moment customers need help.
That was the heart of my conversation with Suren Gounder, Founder & CEO of Tango Eye, on Dilse Omni Talks. Tango Eye is a retail intelligence platform that uses AI and computer vision to help brands measure, monitor, and optimize in-store execution, turning store-floor data into actionable business decisions.
I began by asking Suren about the technology modern omnichannel companies use for operations. What followed felt like a masterclass in a less glamorous, but deeply important, part of retail growth: making everyday execution better.
My biggest takeaway was simple: a scalable retail tech stack is not defined by the tools you buy. It is defined by the decisions those tools help you make.
Table of Contents
1. Start with People, Not the Technology Catalogue
2. Staff Monitoring Should Enable Performance, Not Policing
3. The Evening Conversion Problem That Attendance Data Explained
4. Digital SOPs Turn Small Tasks into Consistent Execution
5. Scaling Means Moving Beyond Founder-Led Supervision
6. A Practical Framework: Presence → Process → Performance
7. Avoid the Endless Pilot Trap
8. Connect Retail Data Before Expecting Better Decisions
The Fitness Tracker Test for Your Retail Tech Stack
1. Start with People, Not the Technology Catalogue
Suren’s starting point was not AI, dashboards, or an elaborate integration. It was the store associate.
“In any retail outlet, the most important thing that drives sales is the staff.”
Suren Gounder
That changes how we think about operational technology. Attendance systems, whether biometric or CCTV-based, are not merely administrative tools. They help establish who is available to serve customers.
But presence is only the first question. Are enough people available during peak demand? Are they carrying out the activities that support service and sales? Does the staffing pattern match the customer traffic pattern?
Across our discussion, the retail tech stack included two broad groups:
Core systems: POS, CRM, inventory management, and CCTV solutions.
Store execution tools: staff attendance, footfall and conversion monitoring, appointment booking, operational checklists, and service quality checks.
The value lies in how these capabilities work together. An attendance record can confirm that an employee reported for work. It cannot, on its own, explain whether the store had enough service capacity at 8 p.m.
2. Staff Monitoring Should Enable Performance, Not Policing
I asked Suren whether staff monitoring was simply a hygiene factor or whether it genuinely influenced conversion.
He was clear that it could have a significant impact. But he immediately added an important qualification: the way a brand uses the information matters.
“There’s a fine line between policing and encouraging better sales and performance.”
Suren Gounder
This distinction matters because the same data can produce very different management responses. A conversion dip might trigger criticism of a store team. Or it might prompt a closer look at staffing coverage and customer demand.
The better question is not simply, “Who was present?” It is, “Did we give the team the capacity to serve the customers who arrived?”
That brought me back to an experience with a brand I had worked with.
3. The Evening Conversion Problem That Attendance Data Explained
The brand had a store conversion problem. Footfall was not the issue, and the broad operational metrics appeared reasonable. However, staff dwell time was low, and a closer examination showed that conversion was particularly weak in the evening.
We then combined attendance data with the conversion funnel and added the time dimension.
The gap became visible between 7 p.m. and 9:30 p.m., especially on weekends when footfall was higher.
The store had an even mix of female and male staff. Female colleagues were leaving at the end of their scheduled shift around 6:30 p.m., for valid reasons. The final evening stretch was therefore being handled by just one person.
That person could serve only as many customers as their capacity allowed.
At a daily headcount level, the store appeared staffed. At the level of a specific customer-demand window, it was understaffed. Those are very different realities.
The lesson was not to blame employees for following their shifts. It was to recognize a mismatch between staffing coverage and demand.
Attendance data is not only for payroll, leave calculations, or deductions. Combined with footfall and conversion, it can reveal a sales constraint. We identified the operational issue; the discussion did not establish a quantified sales uplift afterward.
4. Digital SOPs Turn Small Tasks into Consistent Execution
Suren then moved from whether staff are present to what happens once they are in the store.
Retail operations depend on many small actions being completed correctly. Opening on time. Preparing the floor. Displaying the right merchandise. Making pricing clear. Handling cash properly.
None sounds revolutionary. Together, they shape the customer experience.
Digital SOPs and operational checklists help standardize these activities. Suren described a range of checks that brands can use:
Opening readiness: Is the store opening at the scheduled time, such as 9 a.m.?
Store hygiene: Is the environment prepared for customers?
Merchandise execution: Is a newly launched product displayed correctly?
Pricing visibility: Are the right pricing cards in place?
Cash management: Has collected cash been recorded and deposited as required?
Small operational checks connect store readiness with consistent customer experience.
A checklist is not valuable because someone can click through it. Its purpose is to make the required standard clear and repeatable.
As Suren explained, these details affect customer experience, which in turn can influence conversion. Operational efficiency and customer experience are not separate conversations.
5. Scaling Means Moving Beyond Founder-Led Supervision
When a brand is small, founders and a few trusted people often keep execution together personally. They notice the missing price card, correct the display, and remind the team about opening procedures.
It is easy to assume that this consistency belongs to the business. Sometimes, it actually belongs to the people supervising it.
Suren pointed out that smaller brands are often person-driven. Remove that person from the equation, and challenges appear.
That is where standardized processes become essential. A customer entering a store in Delhi should receive a consistent brand experience with one entering a store in Chennai.
My takeaway: scaling is not only adding stores. It is making good execution less dependent on a particular person being present.
6. A Practical Framework: Presence → Process → Performance
To organize what I learned from Suren, I would frame store execution in three connected layers. This is my synthesis of our discussion, rather than a named framework he presented.
PRESENCE: Do we have the right coverage? Use attendance and shift information alongside footfall to understand whether staff availability matches demand.
PROCESS: Are the essentials happening consistently? Use SOPs, checklists, and service quality checks to establish whether the store is executing the required activities.
PERFORMANCE: What is happening to customer outcomes? Examine conversion, sales, and service quality, then connect those outcomes back to coverage and execution.
Presence → Process → Performance → Operational action
The evening staffing example shows why all three matter. Performance revealed the problem. Presence data, aligned by time, helped explain it. The operational response needed to address coverage, not simply demand better conversion from the same stretched employee.
7. Avoid the Endless Pilot Trap
When I asked about common retail technology mistakes, Suren highlighted an imbalance: brands spend substantial time comparing products and conducting trials, but much less effort turning the resulting data into action.
The excitement sits around the technology’s novelty. Implementation and day-to-day change receive less attention.
I have seen brands spend more than a year evaluating solutions, run pilots with three providers, and ultimately use none. Suren described experiments that end up in presentations without becoming part of the business.
A pilot that proves a tool generates data has answered only one question. It has not established whether the organization can use that data.
Based on our discussion, I would put three questions at the center of any operational technology trial:
What operational problem are we trying to understand?
What other information must we combine with this tool’s output?
What will we do differently when the insight appears?
The objective is not experimentation for its own sake. It is better execution.
8. Connect Retail Data Before Expecting Better Decisions
As a data person, this is the point I feel strongly about: the real value often emerges when we combine information.
CCTV, attendance systems, footfall monitoring, and POS each capture part of store reality. An isolated metric may identify a symptom without explaining its cause.
In our staffing example, footfall alone did not explain low conversion. Attendance alone did not expose the commercial impact. Connecting both to the same evening window made the problem understandable.
Suren sees advances in large language models and generative AI creating opportunities to bring information together and derive actionable insights. He referenced a ChatGPT advertisement involving a chai store as an illustration of this broader shift toward using information to improve service and conversion.
The promise is useful, but the principle remains unchanged: insight has to lead to a change in how the business operates.
The Fitness Tracker Test for Your Retail Tech Stack
Near the end of our conversation, I used a familiar analogy. You can buy a fitness device for ₹2,000 or spend far more on a premium one. I wear a ring myself. But the form factor does not make me healthier.
Suren sharpened the point: neither the device nor the data creates the outcome. The action does.
Retail technology works the same way. The differentiator is not simply which system a brand owns. It is how the brand uses information in everyday operations.
Before adding another tool, ask: What are we doing with the signals we already have? The answer may reveal your next opportunity to scale.
I am Saurabh Agrawal and we come with a new episode on Dilse omni talks every fortnight and cover different aspect of omnichannel with amazing speakers.
