Yes. The most natural segmentation is by funding stage. Month, location and sector also work as secondary cuts, but they need cleaning first. **1. Funding stage (best primary segment)** - Stage is the one classifier present in all five monthly tables, and it gives a meaningful split of the startups. - The largest groups are Seed (73 records), Series A (56), Pre-series A (37) and Series B (26). Series C, D, E and F follow with 11 to 19 each, and Series G, H and J have only 2 to 3 each. - The result is a natural maturity ladder: early stage (Pre-seed, Seed, Pre-series A), growth (Series A to C), and late stage (Series D and beyond). - 127 records have a blank stage and another 37 are "undisclosed". Together that is about 164 records with no usable stage, so this must be treated as its own "unknown" segment. - The stage labels also need standardising before use. Variants include "Preseed" and "Pre-seed", "Series-D" and "Series D", "$Series A" and "Series A", and "Undiclosed" (a misspelling of "undisclosed"). There are also stray values such as "Working Capital to SMEs" and an investor name sitting in the Stage column. **2. Reporting month (built-in segment)** - Each table is one month's announcements: January has 115 rows, March 98, February 96, April 95 and May 42. - This is a natural time-based grouping. The tables are not joinable and share no key, so any cross-month comparison has to be done by stacking them. - May is smaller and has misaligned or blank rows, so it is not directly comparable. **3. Location (geographic segment)** - Location is highly concentrated: Bangalore has 124 records, Mumbai 62, Gurgaon 48, New Delhi 31, Chennai 27, Noida 15, Hyderabad 14 and Pune 12. - It needs merging: "Bangalore" and "Bengaluru" (10 more) are the same city. There are also 20 blank locations. - A "top metros plus other" grouping would work well. **4. Sector (industry segment)** - Sector has roughly 20 or more distinct values per month, up to about 55 in March. It is fragmented and overlapping. - Financial Services leads with 59 records, plus 13 more under "Financial services" (differing only in case). Other large groups are Information Technology & Services (27), Computer Software (26), Health, Wellness & Fitness (15), E-learning (12), E-commerce (12) and Food & Beverages (11). - 19 records have no sector. - Sector would need to be rolled up into broader categories, such as fintech, software/IT, health, consumer and education, to be useful. **Recommendation:** Segment primarily by cleaned funding stage, grouped into early, growth and late stage with a separate "unknown" bucket. Cross it with a top-metro location grouping and rolled-up sector categories. Use reporting month for time comparison. Amount is stored as text, so it would need parsing before any segment-level funding totals.