Hyper-local dynamics
SaaS aggregates all your data into one national model.
buildmmm builds separate, parallel city-level models — Mumbai vs. Delhi vs. Bangalore — to isolate local monsoons, regional festivals (Diwali, Rakhi) and traffic patterns.
Statistical precision · Not a SaaS
Managed Marketing Mix Model for Indian Quick Commerce.
Attribution dashboards double-count your sales. We engineer custom, calibrated Marketing Mix Models for Blinkit, Zepto, Instamart and Shopify brands — refitted weekly by our marketing scientists. Privacy-safe, aggregate data only. Zero code from your team.
- Adstock half-life
- 0.9 wk
- Saturation point
- 68%
- Incremental share
- 42%
Meta Robyn · Bayesian priors · Geo-lift calibration
S-curve saturation — Blinkit Sponsored Products
Base vs. incremental lift
Marginal ROAS4.1x
Diminishing-return alpha0.53
Why SaaS fails Quick Commerce
Annex 01 · The statistical hookSelf-serve modelling assumes a stable, national, always-in-stock retail world. Indian q-comm is none of those things — it is 10-minute demand shaped by dark stores, rain and duplicated shelf spend.
The operational blindspot
SaaS ignores your supply chain entirely.
We integrate On-Shelf Availability (OSA %) and dark-store Out-of-Stock rates as negative control variables. If a micro-fulfilment centre is dry, we don't penalise your creative.
Digital shelf & spend duplication
SaaS doesn't understand fanned-out keyword spend traps.
We write custom pipelines to deduplicate performance spend across Blinkit Sponsored Product Ads and Zepto Product Boosters before a single coefficient is fitted.
Fully managed, no in-house PhDs
SaaS hands you a tool and a config screen.
We do the data cleaning, model-fitting via Meta's Robyn, geo-lift calibration, and deliver decision decks with budget allocation every month.
Interactive
Explore the quick-comm system physics
Three mathematical pillars underneath every model we build for you.
Adstock — the memory effect
Ads don't act like a light switch. A WhatsApp flash sale or an app push notification ("Forgot coriander? 🌿") decays in 24 hours. A brand campaign on Meta has a longer, lingering impact.
effect(t) = spend(t) + α · effect(t−1)
Residual ad effect over days
For FMCG, food & bakery
The 26-SKU Calibration Engine
A structural fix for the biggest data-integrity gap in Indian quick commerce reporting.
The problem
Platforms only share actual sell-out data (reported_offtake) for your top 20–30 SKUs. The rest of your catalog relies on modeled estimations (estimated_offtake)—so a model fitted on the full catalog quietly inherits someone else's assumptions.
26
SKUs with real offtake
400+
SKUs on estimates
Our methodology
- 01
Ground Truth Model
We fit a high-fidelity model strictly on your platform-reported SKUs to lock down real ROAS and decay rates.
- 02
Parameter extraction
Adstock half-lives, saturation shapes and channel coefficients are exported as hard priors.
- 03
Constrained full-catalog model
Those parameters are fed programmatically as strict constraints, so global budget decisions stay structurally honest.
constraints ⊂ ground_truth → full_catalog_model
Result: one honest budget allocation across your entire catalog
Who we serve
Two operating realities. Two model architectures.
Persona A
High-Growth D2C & Shopify Brands
- Direct Shopify API integration for order, discount and CAC data
- Meta and Google PMax attribution correction against double-counted conversions
- Cross-channel budget optimization across paid social, search and affiliates
- Customer lifetime value integrated into the response function, not just first-order revenue
Persona B
FMCG, Food & Bakery Brands on Q-Comm
- Blinkit / Zepto / Instamart digital shelf and keyword mapping
- Weather and rainfall baseline controls at city and dark-store level
- Stockout and On-Shelf Availability adjustments so ads aren't blamed for supply gaps
- Category margin optimization to grow contribution profit, not just topline offtake
FAQ
Questions marketing leaders ask us first
Lead capture
Get Your Custom MMM Blueprint
We'll audit your data readiness across platforms, map your digital shelf, and return a modelling blueprint with the exact variables, geo-tests and refresh cadence we'd run for your brand.
- 45-minute diagnostic with a marketing scientist
- Data readiness scorecard across q-comm and D2C sources
- No SDK, no code, no platform migration