Benne BI Dashboard
Industry
Hospitality
Timeline
Jul 2025 - Sept 2025
Organization
Benne - Heritage Bangalore Dosa
My Role
Business Intelligence Lead
Team
Chief Growth Officer, Head of Operations, 1 intern
Type
Live project
Description
In the fast-paced F&B industry, margins are thin & customer expectations evolve rapidly, analyzing business data is essential for identifying revenue opportunities, optimizing operations & reducing waste. This project was driven by the need to help leadership move beyond intuition & make informed strategic decisions through real-time performance insights.
I designed and developed a comprehensive BI dashboard for Benne, collaborating closely with the Chief Growth Officer and Head of Operations. The dashboard consolidated key metrics across operations, sales, marketing & finance into a single source of truth, enabling faster, data-driven decision-making.
Note: All data displayed is dummy data & for demonstration purposes only.
Problem Statement
Restaurants rarely have the resources or infrastructure set up to analyze the data they generate & make data-driver decisions. I learned this bit about the industry while pursuing my Master, from my professors who worked in food technology. POS systems do provide some analytics, but it is mostly boilerplate. This means, they might not be aware about where they are spending most money, or about their wastage, or which items are making them the most money, among many other things. This gap in the actual & guesswork called for a better system for restaurants to visualize their data in a way where they could understand insights into their operations better.
My Research Process
Research started with talking to people. I worked with Benne's Head of Operations to understand their current operations deeply- how ingredients flow from the kitchen to stores, how are orders coming in through different channels, how much of what are we selling. For more business-related requirements like outgoing rent, revenue targets & store growth, I worked with the Chief Growth Officer.
I understood the current operations & processes at Benne. Some data was already being captured, but on an individual level by certain departments. Which meant spread-out data, no insights & no centralization.
Discussed problems individually with stakeholders & understood where the gaps were. For eg. Operations had a raw material wastage problem, but there was no way to figure out how to reduce the difference between recorded & actual wastage.
Started linking problems to respective fundamental datapoints, then building metrics & KPIs out of them. Along with it came the backend work of figuring out how to capture, store & process the data. Challenge was to make it centralized but also not dependent on any single person to use.
Zeroing in on the metrics
After talking with the stakeholders & a bit of help from AI, I was off narrowing down on metrics in each function that could be most useful in getting us insights. Although, on paper we had a ton of metrics we'd have liked to track, but feasibility took precedence:
Are we even capturing this data?
If not, how can we capture it?
Who/what system will capture this?
Where will it be stored?
How will it be processed?
How will it flow into our centralized system?
Thus, keeping these factors in mind, we cut down to the best metrics which we could feasibly get the data for. For some metrics, we decided to be future-ready- building the template storage, data processing & data viz right now, & putting in real data when we were able to get the it in the future.
Execution
The system had to be accessible & operable even without me, so the best way to store data was using a dynamic & sharable tool like Google Sheets. I setup a bunch of sheets with all calculations required to process the data, accessible through Google Drive.
The sheets were connected to the data visualization platform Google Looker Studio. Next step was setting up all the visualizations in Looker Studio- connecting data, creating visualizations, creating filters, custom views, instructions & documentation.
Now members of any team could just update these sheets, just like they were updating their separate excel sheets & find the data compiled in a single comprehensive dashboard in Looker. This made data-driven decision-making very easy for the executives.
Following are some examples of the metrics created for the dashboard:
Note: All data displayed is dummy data & for demonstration purposes only.
Improving customer experience
Apart from the BI dashboard, there was a particular requirement from the Operations Head that was challenging but very interesting to me. They needed to see a condensed version of what people were feeling & saying about their experience at Benne. We decided to take up this challenge.
Google reviews represented a rich source of customer feedback for the brand, but Google itself didn't provide much to analyze the reviews, so we decided to build it ourselves. Using an AI automation tool- n8n & a publicly available API, we created a workflow that fetched all the reviews from Google, ran them through an AI engine & churned out analysis. It gave the team ready-made insights into the customer sentiment & friction points down to every store & every month, displayed directly in the same dashboard. This served as a huge relief for the team, as combing through thousands of reviews online would've been impossible otherwise.
The system consolidated & made visible thousands of google reviews ie. customer feedback points in a single place.
It analyzed the reviews to find patterns of customer experience, whether positive or negative.
Suggested operational improvements that could lead to a better customer experience. For eg. customers are repeatedly complaining about a menu item not tasting good, we could know its severity & act on changing the recipe or delivery for it.
Impact that we had…
Multiple functions like sales, marketing, finance & operations consolidated under a single comprehensive dashboard.
Translated raw data into actionable insights that improved decision-making for CXOs and internal stakeholders.
Increased visibility into particular data points that traditional POS systems could not provide.
Helped executives get a better view of customer experience by analyzing all online reviews through one central engine, giving them a chance to improve processes provide a better experience.