A Plott Data research room

Every hour of household labour in India, counted.

We poll the country's home-services platforms every hour and record who can actually be booked, where, at what price, and by how many workers. This is what the dataset holds and the questions it settles.

See the questions → Data dictionary

Live slot board · location × hour polling 06:00

bookable constrained unserved

98M+

items processed

39M+

timestamped snapshots

cities polled

364

pincodes

Hourly

collection cadence

Indian platforms in the Plott Data stack

What we track directly

Three home-services platforms at full depth, alongside the quick-commerce and e-commerce coverage that lets you read this category against the rest of Indian retail.

Home services

Urban Company

Price and package structure by locality
Ratings and review stream
Instant and scheduled booking modes

Home services

Snabbit

Slot-level availability
Worker headcount behind each slot
Price ladder and promotions

Home services

Pronto

Service pricing and hub geography
Slot depth and surge
Recurring-booking rates

Quick commerce

Blinkit · Zepto · Instamart

Plus BigBasket, JioMart and Flipkart Minutes
Priced at dark-store level, not national averages
Search rank and sponsored placement

Food & gig delivery

Swiggy · Zomato · Rapido

Plus Porter and Dunzo
The gig-labour comparable set for this category
Fees, surge and rider-side economics

E-commerce

Amazon · Flipkart · Myntra

Plus Nykaa, AJIO and Meesho
Catalogue, seller and fulfilment data
Keyword rank and paid placement

Questions this settles

Grouped by the lines of enquiry that decide an underwriting case. Every question below is answerable from data already collected.

GMV

GMV & unit economics

Order value, take and worker payout — derived from inventory, not disclosed by anyone.

01What is monthly GMV by platform and by city, and how fast is it compounding?

02How many bookings complete per day, and at what average order value?

03What share of available worker-hours converts into paid work?

04What does a worker earn per shift, and what does the platform keep?

05What is revenue per worker per month, and is it improving or decaying?

06How does city GMV scale with workers deployed — linear, or is there density leverage?

07Who is winning share of category spend in each city, month by month?

08What does a city's GMV curve look like from launch, and how long to break density?

How GMV is derived no platform in this category discloses it

Capacity

Every bookable slot carries a worker count. Polled hourly, that is the category's advertised supply in worker-hours.

measured

Depletion

The same future slot is re-polled each hour. Worker-hours that disappear between polls are worker-hours that sold.

differenced

Value

Completed bookings are priced at the ladder captured for that city, duration and booking mode in the same hour.

measured

Calibration

Cross-checked against review velocity and public booking counters. Indexed GMV needs no anchor; absolute GMV states its assumption.

modelled

Pricing

Pricing & realisation

What a booking actually earns, once discount is stripped out.

09What does an hour of household help cost on each platform, and how has that moved month over month?

10Who is running a price experiment right now — in which cities, in which direction, and did it hold?

11How much of the headline price is discount rather than list, and what is the real realisation per booking?

12Does the same service cost the same in every city, or is there geographic price discrimination?

13What premium is charged for instant over scheduled booking — how is urgency monetised?

14How does price scale with duration, and who is cheapest at each basket size?

15Are promotions deepening or shallowing — is pricing discipline improving or eroding?

Supply

Supply & labour

The constraint that decides whether growth is real. Normally private.

16How many workers are actually on shift, by city and by hour?

17Is the worker base compounding, flat, or churning out?

18Which cities are supply-constrained, and which carry idle capacity?

19Does a platform build supply ahead of demand, or chase it?

20How long does a new city take to reach viable worker density after launch?

21What share of advertised capacity can actually be staffed?

Utilisation

Utilisation & demand

Whether operating leverage is arriving, or the model stays linear.

22What fraction of bookable capacity is actually consumed?

23Which hours and days carry the load, and which sit empty?

24Is utilisation improving over time, or is scale being added without density?

25Where does demand outrun supply badly enough to break fulfilment?

26Is there an unserved window a new entrant could take?

27How seasonal is the category, and how sharp are the weekly peaks?

Geography

Geography & expansion

Where the runway actually is, and what it costs to open it.

28Which cities is each platform live in, and when did each go live?

29How dense is coverage inside a city — how many localities are genuinely serviceable?

30What is the expansion cadence, measured in cities and pincodes per quarter?

31Where is the next launch, before it is announced?

32Has anyone quietly retreated from a city or thinned a cluster?

33How much addressable geography remains unentered?

Competition

Competitive dynamics

Who is setting terms, who is reacting, and how contested the map is.

34Who moved price first, and how quickly did the others follow?

35Is competition converging prices or dispersing them?

36In contested neighbourhoods, who holds the denser supply?

37How much of the map does each platform have to itself?

38Is anyone broadening or narrowing their catalogue, and where are they cutting scope?

39Are the platforms colliding head-on, or segmenting by city tier?

Quality

Quality & retention

Whether the service holds together as it scales — the durability question.

40How do ratings differ by platform, city and locality?

41What is review volume doing, as a directional read on booking volume?

42What do complaints cluster around, and where geographically?

43Does service quality hold as a platform scales a city, or degrade?

44Is quality diverging between the leaders and the challengers?

Data dictionary

Reference tables describe the world; snapshot tables record it repeatedly. Every snapshot row is immutable and timestamped, which is what makes change measurable rather than inferred.

Table	Type	Grain	Key fields
Urban Company
uc_cities	reference	City	`name` `city_key`
uc_locations	reference	Locality	`place_id` `latitude` `longitude`
uc_packages	reference	Package	`package_type` `booking_type` `service_category`
uc_package_variants	reference	Duration variant	`duration_name` `duration_mins`
uc_price_snapshots	snapshot	Locality × variant × hour	`price` `original_price` `discount_percent` `booking_type`
uc_reviews	snapshot	Review	`rating` `review_text` `review_date` `filter_locality`
uc_category_rating_snapshots	snapshot	City × category × hour	`rating` `total_bookings` `category_key`
Snabbit
snabbit_services	reference	Service	`service_name` `includes` `excludes`
snabbit_locations	reference	Locality	`place_id` `pin_code` `is_serviceable`
snabbit_addresses	reference	Bookable address	`hood_name` `cluster_name` `region_name`
snabbit_availability_snapshots	snapshot	Location × service × slot × hour	`runners_count` `is_available` `from_time` `duration_mins`
snabbit_price_snapshots	snapshot	Address × duration × hour	`price` `slash_price` `service_charge` `taxes`
snabbit_offers	reference	Promotion	`discount_type` `discount_value` `min_order_value`
snabbit_offer_snapshots	snapshot	Promotion × hour	`discount_value` `max_discount` `is_active`
Pronto
pronto_services	reference	Service	`base_price` `effective_price` `inclusions`
pronto_locations	reference	Locality	`hub_id` `micro_hub_id` `serviceable_area_id`
pronto_price_snapshots	snapshot	Location × service × hour	`effective_price` `effective_recurrent_price` `saving`
pronto_slot_snapshots	snapshot	Location × slot × hour	`slots_left` `is_full` `is_surge` `surge_price`
pronto_slot_summaries	snapshot	Location × day	`total_slots` `available_slots` `surge_slots` `peak_hours`
Adjacent categories
qcom_products	reference	Product	`name` `brand` `category_path` `pack_size`
qcom_store_product_snapshots	snapshot	Store × product × hour	`price` `mrp` `inventory` `is_available`
qcom_search_result_snapshots	snapshot	Keyword × product × hour	`rank_on_page` `is_ad` `page_number`
ecom_products	reference	Product	`name` `brand` `seller_id` `category_path`
ecom_product_snapshots	snapshot	Product × hour	`mrp` `selling_price` `in_stock` `rating`
ecom_search_result_snapshots	snapshot	Keyword × product × hour	`rank_in_results` `is_sponsored` `selling_price`

The house behind this room

Plott Data reads Indian commerce, end to end.

This room is one slice of it. India is where our coverage runs deepest — quick commerce down to the dark store, e-commerce down to the seller, and now home services down to the individual worker on shift.

The same collectors run across 114+ marketplaces worldwide, so an India thesis can be tested against how the identical category behaved in another market.

114+

marketplaces tracked

25+

countries

data-point categories

India

deepest coverage

Quick commerce

Blinkit, Zepto, Swiggy Instamart, BigBasket and JioMart — priced and stock-checked at dark-store level, not national averages.

E-commerce

Amazon, Flipkart, Myntra, Nykaa, AJIO and Meesho — catalogue, seller, rank and sponsored placement.

Home services

Urban Company, Snabbit and Pronto — the category in this room, down to worker headcount per slot.

Delivered your way

REST API, scheduled CSV, or a connector straight into Snowflake, BigQuery or Redshift.

Visit plottdata.com → Ask about India coverage

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