A few months ago, a client came to us with a deceptively simple request: they needed a comprehensive map of every viable event venue across their target city. Not a rough list pulled from Google Maps in an afternoon — a structured, filterable, decision-ready dataset that their events team could actually act on. What followed was one of the more methodical research projects I've run at Helion 360, and Excel became the backbone of the entire operation.
Here's how I approached it, what I'd do differently, and the framework you can steal for your own city-wide venue research project.
Why Excel (and Not a Fancier Tool)
My first instinct was to reach for something more sophisticated — Airtable, Notion, even a lightweight CRM. But there's a reason I kept coming back to Excel: everyone on the client's team already used it, it handles large flat datasets exceptionally well, and the filtering, sorting, and conditional formatting capabilities are genuinely powerful when you know how to use them.
The goal of a city-wide venue research project isn't to impress anyone with your tech stack. It's to collect clean, consistent data that helps decision-makers move fast. Excel does that reliably.
Defining the Research Scope Before Touching a Spreadsheet
The most common mistake I see in venue research projects is jumping straight into data collection without agreeing on what you're collecting. Before I opened Excel, I spent time with the client defining three things:
- Venue categories: Were we looking at hotels, standalone event spaces, rooftop venues, restaurants with private dining, cultural institutions, or all of the above?
- Qualifying criteria: Minimum capacity, parking availability, AV capabilities, catering options, outdoor space — these became our filter columns later.
- Geographic boundaries: City-wide sounds simple, but does that include surrounding suburbs? We used specific postcodes to draw a hard boundary.
Locking in these parameters saved probably a week of wasted data entry. Every row in our final spreadsheet needed to answer the same questions, so the questions had to be defined first.
Building the Excel Data Collection Template
Once the scope was clear, I built the master template. The sheet had a single row per venue, with columns grouped into logical clusters. Here's the structure I used:
Venue Identification
- Venue Name
- Address
- Postcode / Borough
- Website URL
- Primary Contact Name
- Contact Email / Phone
Capacity & Spaces
- Maximum Seated Capacity
- Maximum Standing Capacity
- Number of Separate Rooms/Spaces
- Outdoor Space (Yes/No)
Facilities & Services
- In-House Catering (Yes / No / Approved List)
- AV Equipment Included
- Accessibility Rating (we used a simple 1–3 scale)
- Parking (On-site / Nearby / None)
- Accommodation On-site (Yes/No)
Commercial Details
- Day Hire Rate (Estimated)
- Evening Hire Rate (Estimated)
- Minimum Spend
- Exclusivity Available (Yes/No)
Research & Scoring
- Data Source (Google, Direct Call, Site Visit, Referral)
- Last Verified Date
- Overall Suitability Score (1–5)
- Internal Notes
That last section is easy to overlook, but it's critical. Knowing when data was collected and how it was sourced tells you how much to trust it six months later.
The Data Collection Process Itself
With the template locked in, I split the collection work into three phases.
Phase 1: Desk Research
We started with Google Maps, venue directories like Tagvenue and Hire Space, local tourism board listings, and a few industry-specific databases. The rule was simple: if a venue had a web presence and met the basic criteria, it went into the sheet. We weren't qualifying deeply at this stage — we were casting wide. This phase gave us around 180 raw venue entries across the city.
Phase 2: Outreach and Verification
Raw entries from the internet are often outdated. Venues close, rebrand, change capacity, or stop taking private hire bookings. I had a team member work through each entry with a standardised outreach script — a short email followed by a phone call if no response within five business days. The goal was to verify the key commercial and capacity details and fill gaps in the spreadsheet.
We used a simple colour-coding system with conditional formatting: green for fully verified, amber for partially verified, red for unresponsive or unverified. At a glance, you could see the confidence level of the entire dataset.
Phase 3: Scoring and Shortlisting
Once the data was clean, I added the suitability scoring. Rather than scoring on gut feel, I built a simple weighted scoring formula in Excel. The client had told us that capacity and catering flexibility were their top priorities, so those columns carried more weight in the formula. Every venue got a calculated score out of 100, and we filtered the sheet down to the top 40 for deeper review.
Making the Data Usable
A spreadsheet full of data is only as useful as the analysis layer on top of it. I added three additional tabs to the master file:
- Summary Dashboard: A pivot table view showing venue counts by borough, average capacities, and how many venues hit the minimum suitability score threshold.
- Shortlist View: A filtered view of the top 40 venues with key details pulled into a cleaner layout for sharing with stakeholders who didn't need the full dataset.
- Change Log: A simple tab tracking any updates made after initial delivery — because venue data goes stale faster than most people expect.
What I'd Do Differently Next Time
Honestly, the process worked well, but there are two things I'd change. First, I'd build the outreach tracker directly into Excel from day one rather than running it separately in a different sheet. The context-switching slowed us down. Second, I'd agree upfront on who owns data maintenance after handoff. We delivered a beautifully clean dataset, and six months later the client admitted nobody had updated it. Build a review cadence into the project from the start.
The Bottom Line
A city-wide venue research project sounds like a big, unwieldy task — and it can be if you don't structure it properly. But when you invest time in defining scope, building a consistent Excel template, and running a disciplined three-phase collection process, you end up with something genuinely valuable: a living document that helps people make faster, better-informed decisions. That's the kind of strategic research work we do every day at Helion 360, and it starts with getting the fundamentals right.