PHPMem v2.0.1
Version
1.6.45
Uptime
18 days 8 minutes 57 seconds
Memory
Total
512MB
Used
12,72MB (2.48%)
Free
499,28MB
Keys
Current
14 060
Total (since start)
40 994
Evictions
0
Reclaimed
760
Expired Unfetched
0
Evicted Unfetched
0
Connections
Current
2 / 1 024 max
Total
242 740
Rejected
0
llm:220441484fb4caf64b6657ac4651e4dc01e0127cc567bb1af405ea3ce6af2e0f
Edit
Here are the three questions I'd pick. Each one is backed by a query I already ran, and each comes with a data caveat that affects how far you can trust the answer.
## 1. Is funding activity accelerating or slowing, and is the deal mix shifting from many small bets to fewer large ones?
This is the timing question for founders and investors.
- **Deal count falls steadily.** Deals drop from 935 in 2015 to 993 in 2016, then 687 in 2017, 310 in 2018 and 111 in 2019. The 2020 figure is only 7, which is a partial period (data ends 2020-01-13).
- **Median ticket size rises.** The median disclosed amount goes from $1.5M in 2015 to $1.0M in 2016, $2.25M in 2017, $4.0M in 2018, $12.0M in 2019 and $9.0M in 2020.
- **Disclosed totals are not a steady trend.** Totals are about $8.6B in 2015, $10.4B in 2017, $5.1B in 2018 and $9.7B in 2019. Fewer, larger deals are holding the total up.
- **Caveat:** only 2,066 of the 3,044 deals have a numeric amount. The 2016 total came back as `nan` because of at least one malformed amount value, so I can't state it. The 2016 median is still usable. A few dates are also malformed (for example `0015-07-01`, which should be 2015).
## 2. Which cities and verticals concentrate deal flow, and do they command different ticket sizes?
This is the question for choosing where to launch, open an office or source deals.
- **Bangalore leads on count.** The city table shows Bangalore with 700 deals, then Mumbai with 567, New Delhi with 421 and Gurgaon with 287.
- **Median tickets differ by city.** Medians are $2M in Bangalore, Mumbai and Gurgaon, and $1M in New Delhi and Hyderabad. Kolkata is much lower at about $157.5K, though on only 10 disclosed deals.
- **The city field is fragmented, so rankings are understated.** "Bangalore" (700) and "Bengaluru" (141) are the same place, as are "Gurgaon" (287) and "Gurugram" (50), and likely "New Delhi" and "Delhi". Merged, Bangalore is clearly even more dominant.
- **Industry Vertical has 822 distinct values**, and many are near-duplicates, so it needs grouping before a vertical ranking means anything.
## 3. What does a "typical" round look like by sector and city, and who is investing in it?
This is the benchmarking question: how much to raise, what round type to expect, and which investors to approach.
- **Coverage looks sufficient.** Every one of the 3,044 deals has an investors field, and there are 57 distinct round types and 2,459 distinct startups.
- **Cross-tabs are possible.** Round type × vertical × city, plus investor co-investment patterns, can show typical ticket size and the most active backers.
- **Cleanup is needed first.** Round-type labels have spelling variants, and the investor field holds multiple names in one cell, which must be split.
- **Repeat funding is visible.** There are fewer distinct startups (2,459) than deals (3,044), so you can trace how follow-on rounds progress and how much time passes between them.
## Data-quality notes that apply to all three
- About a third of deals have no usable amount.
- Date strings are inconsistent.
- City and vertical labels have many duplicate spellings.
- Standardise these before drawing conclusions, especially for the city and vertical comparisons.