Your D2C Brand & Data from 900+ Indian D2C Brands

Every morning across India, the ground outside homes comes alive with patterns — drawn by hand - Rangoli, Kolam, Alpana, Muggu 🤷‍♂️ goes by many names. 

Patterns play a big role in India, understanding them? Even more so.

Over the past few years, we’ve worked with 900+ Indian D2C brands. And in that journey, what stands out isn’t just growth, it’s repetition.

Like the rangoli patterns drawn, the data tells a story. Let’s trace it.

Past ⏩ PATTERN ⏩ Present

Check out the Top Patterns our D2Cs learn and benefit from every day…

1. Carrier Optimisation Using Peer Category Data

Let’s start simple:

Two D2C skincare brands ship to the same pincode, Brand A uses Carrier X, Brand B uses Carrier Y. One clocks 89% SLA adherence, the other 61%. Guess which brand also has a 23% higher RTO?

Now, imagine being able to predict and benchmark:

This isn't magic — it’s pincode-level carrier prediction based on:

• Category-adjusted packaging sizes
• Payment method preferences by region
• Carrier handover latency by warehouse

One brand in Jaipur switched 128 pincodes to alternate carriers, optimised based on cross-brand data, and saw:

• SLA adherence jump by 16.2%
• RTO drop by 28.5%

2. Prepaid Nudges Engine via TOFU Flow Analysis

TOFU flows (Top-of-Funnel) are full of friction. Especially when trying to convert COD to prepaid.

But what if we knew:

• What exact time slots converted best by user region?
• What UPI nudges work in skincare vs electronics?
• What cashback ceiling nudged highest COD-to-Prepaid switch?

Using data from 900+ D2Cs, we built:

• Intent-predicted nudges at checkout page (abandoned last week? Offer ₹15 UPI cashback)
• Dynamic bank offer recommendations (based on user history)

Results:

• Prepaid share moved from 28% → 46%
• CVR (Conversion Rate) up by 19%
• RTO fell from 34% → 21%

3. NDR Pattern Matching with Behavioural Tags

Most brands treat NDR as a post-facto report. We treat it like a predictive category.

Based on over 58M+ NDR events (from 900+ D2C), we classify NDRs into:

• High Intent - Payment Issue (e.g., "No change available")
• Medium Intent - Absenteeism patterns (e.g., cluster of missed deliveries post 6PM)
• Fake NDR - Courier-triggered (rare but consistent)

Example: A jewellery brand saw 17% NDR in Bengaluru for COD orders. Deep dive showed:

• 82% of NDRs came from 4 courier partners.
• Same partners had <5% NDR with other brands.

Using cross-brand courier behaviour data, we triggered rerouting logic based on courier ID — and reduced false-NDRs by 66% in 14 days

NDR Classification Logic (Simplified)

4. COD Success Score (CSS) Based on Behavioural + Logistic Signals

We’ve built a scoring model — CSS (COD Success Score) — on 67.9M+ COD orders:

• Click–cart–order lag time: longer = more cancellations
• Device type: Android budget devices = 1.6x more likely to fail
• Pincode + previous NDR + average return window data overlay

CSS Output: Score range: 0 to 1

• 0.75 = safe to accept
• 0.5–0.75 = add verification step
• <0.5 = flag or convert to prepaid only

Impact:

• COD rejection down by 31.7% for filtered orders
• RTO loss reduced: ₹14.2L/month for mid-size brand in North India

5. Geo-Cohort LTV Clustering for Targeted CAC Allocation

Most brands deploy CAC like it’s a monolith. But acquisition in Surat ≠ acquisition in Pune.

Using cohort + geo data, we derive:

• LTV delta by city-pincode-category
• Repeat purchase windows by cohort size
• Ad-to-first-retention-event gap

Result: A D2C supplement brand running Pan-India FB ads saw:

• CAC: ₹224
• Blended LTV after 60 days: ₹488

After geo-cohort LTV insights:

• CAC (Optimised Tier 1 + UGC ad variants): ₹198
• LTV (Optimised cohort clusters): ₹712

CAC vs LTV: Before vs After

6. Cross-Brand Fraud Ring Detection Using Entity Graphs

Across 900+ brands, fraud rings tend to recycle tactics:

• Repeat pincode clusters
• Order timing patterns (1–2 AM spikes)
• Behavioural signals: COD-only, dummy names, alternate phone format

Graph Model Input:

• Shared addresses + phone patterns
• NDR repeat logic
• Checkout funnel breakage points

Case: We flagged a group of 174 phone numbers triggering 3.2K fake COD orders monthly across 9 brands.

Action:

• Joint blacklist = 93% block success
• Shared model retraining = - 27% false positives

7. Micro Time-Slot Based Fulfilment Routing

One of the most ignored levers in D2C ops: Time-of-day based fulfilment routing.

Why does it matter? Because:

• SLA breach likelihood increases by 23% post-4PM pickup in Metro cities
• Courier performance shifts across weekday–weekend transitions
• Average OTP delay is 12.3 minutes longer in post-lunch slots in Tier-2 hubs

So we optimised pickup-slot allocation for brands based on:

• Courier efficiency per hour by warehouse region
• Last-mile congestion probability
• First-attempt delivery success by pickup time

8. Tier-2 Personalisation Models Using Regional Behavioural Vectors

Brands often stereotype Tier-2 buyers as ‘value-seekers’. Data says otherwise. From 17.1M orders across 103 cities:

• Higher CVR on mobile-optimised checkout in Hindi (Lucknow, Indore)
• Strong preference for bank-based UPI (vs. wallet)
• Longer browsing time → higher intent in skincare, not electronics

Case: A women’s wellness brand switched:

• Checkout copy to Hindi + Marathi
• Added PayTM + GPay fallback

Lift:

• Drop-off rate reduced by 22%
• CVR uplift: 2.3x in Tier-2/3 regions

Checkout Optimisation for Tier-2

9. Post-Order Journey Segmentation via Behavioural Traces

After delivery, there’s silence — but not for us. Using data from 11.2M post-purchase sessions:

• 68% of post-order page views happen within 24h of delivery
• “Where’s my order” (WISMO) drop-off correlates 0.63 with NPS improvements
• Repeat visit post-delivery = +21% higher LTV

Segmentation Flow Examples:

• “Trackers” → revisit tracking page 2–4× → nudge for review
• “Dormants” → zero activity → drop personalised WhatsApp reminder (not push)
• “Browsers” → open upsell messages within 48h → trigger loyalty onboarding
• “Previously Loyal” → has not engaged in a while → ask about previous order (optimise)

10. Refund Analysis: Dynamic Policy Based on Category × Location × Intent

Flat refund policies? That’s like using the same sunscreen in Delhi and Cherrapunji.

• Inner-city COD fashion = highest refund rates (Delhi South: 21.9%)
• Delay-induced refunds spike after 72h breach, especially in gifting
• Nutraceuticals in Tier-2 prepaid = lowest refund rates (<2%)

Smart Refund Rule Example:

• Category: Fashion
• Location: Tier-1 (Delhi, Mumbai)
• RTO probability: >18%
• → Enforce stricter image-based evidence + shorter refund window + exchange only options for select risky customers

Impact:

• Refund abuse down 28.6%
• CSAT steady (due to well-placed messaging)

11. WhatsApp Behavioural Retention Engine

WhatsApp isn’t a broadcast channel. It’s a behavioural memory engine — if you feed it right.

Using event-based triggers (browse history, past RTO, purchase lag), we built RFM-driven personalised WhatsApp journeys:

• "Left item in cart and never opened WhatsApp again? Send web-only voucher at 9:30PM."
• "Opened every broadcast but no order in 90 days? Trigger ‘Back in stock’ from best-converting category."

Case Study: A fashion D2C with 14.8% retention used our WA journey engine:

• CTR improved from 3.9% → 11.2%
• ROAS on WhatsApp moved from 2.7x → 5.6x
• Churned user reactivation (90+ days) was 22% within 4 weeks

12. Real-Time SLA Breach Forecasting Engine

We trained a light ML layer on:

• Historic carrier SLA data
• Warehouse dispatch latency (by hour, by category)
• Delivery address type (residential vs commercial)

This lets us forecast: "Will this order breach SLA before it’s even packed?"

Impact on Ops Teams:

• Predictive rerouting (alternate courier suggestion before manifest)
• Buffer inventory adjustments
• Real-time SLA dashboard for CX team

Results (Pan-India D2C):

• SLA breaches dropped by 24% MoM
• Call volume on ‘Where is my order’ dropped 31%

Forecasting Accuracy

13. Distributed Warehouse Planning via Cross-Brand Dispersion

Instead of asking, “Where should we open our second warehouse?”, ask:

• “Where are fulfilment lags killing CAC-to-LTV ratio?”

Using delivery time + repeat behaviour + inventory lag across brands, we identified:

• Cities with 2–3x average fulfilment delays
• Products with >40% reordering from Tier-2 cities
• Carrier-level performance by dispatch origin

Case: A home decor brand had all inventory in Bengaluru.

Data said:

• 44% of north India orders took 5–6 days to deliver
• Repeat rate in Delhi was 29% lower

After opening a fulfilment node in Noida:

• North zone SLA improved by 33%
• 60-day repurchase rate rose from 13% → 19.7%

Pre/Post Distributed Fulfilment Impact

14. BOFU Automation Using Cross-Brand RFM & Cart Decay Graphs

Your cart abandoners aren’t the same across categories. But they rhyme.

By pooling data on:

• Cart decay intervals (time to bounce)
• Bounce-to-conversion delay by product type
• Avg RFM score by price band & item count

We automated BOFU nudges:

• Send ₹50 discount only for RFM-High + bounce under 3 min
• Push “Only 2 left” CTA for price-band 499–899
• Trigger video walkthrough for gadgets abandoned >2x

Results:

• 8.6% uplift in final conversions (BOFU-stage only)
• 12.4% higher prepaid share in converted users

15. 1-Click Returns Routing Using Pincode-RTO Risk Index

Returns aren’t equal. A ₹249 t-shirt return from Guwahati ≠ ₹2,499 blender from Delhi.

We built a Returns Routing Engine powered by:

• Product return rates (by category, by size)
• Pincode-level RTO & SLA breach risk
• Historical courier claims data

Returns are then dynamically:

• Routed to nearest inspection warehouse (if high value + high restock chance)
• Flagged for doorstep QC (if high RTO pincode)
• Auto-refunded (if low value + low resale viability)

Impact:

• Returns ops cost dropped by 19%
• Product restocking efficiency rose 2.3x

Returns Routing Logic (Simplified)

16. Category Sentiment Clustering from 9M+ UGC Reviews

If your competitor’s 4.3 rating is hiding "good smell, bad pump", do you really envy that rating?

We cluster 9M+ user reviews across:

• Noun-Adjective pairs ("bottle fragile", "colour bleeds")
• Category-specific pain points ("sizing off", "burns eyes")
• Topic frequency + emotion vectors (joy/anger/disgust/etc.)

Result:

• Cosmetics D2Cs started flagging bad batches 3 days before escalation
• Apparel brands switched fit models based on "tight arms" phrases in reviews

Sentiment Snapshot (Hair Oil Category)

17. RTO Forecasting and Prevention Using Pincode + Product + Persona Data

RTOs aren’t a cost centre — they’re a clarity problem. We model:

• Persona-specific delivery probability (e.g. COD in Tier-2 fashion = 17.2% higher RTO)
• Pincode × category delivery success (e.g. electronics in J&K = 9% SLA breach risk)
• Product packaging failure (e.g. leaky pumps in monsoon belts)

Result:

• RTO forecasting accuracy: ±4.1% (category-adjusted)
• COD denial prevention suggestions (like pre-delivery WhatsApp CTA)

Persona + Pincode RTO Map (Simplified)

18. Warehouse Load Balancing via SLA History + Pincode Throughput

Peak season is not the time to guess your warehouse loads. We use:

• Historical SLA breaches across 900+ D2Cs
• Pincode-level throughput heatmaps
• Carrier-level reliability per fulfilment node

Case: An NCR-based omnichannel brand rerouted 18% of SKUs from Mumbai FC to Bangalore FC, based on:

• J&K SLA breach spike (+12% during winters)
• Tier-2 surge in Karnataka

Impact: SLA breach rate fell by 9.4% in 22 days.

FC Routing Decision Matrix (Simplified)

19. Hyperlocalised Logistics Scoring by Pincode × Carrier × Product Type

One carrier’s Tier-1 promise can be a Tier-3 disaster. Using 13.6M delivery records:

• Pincode-carrier efficiency scoring
• Category-adjusted SLA probability (fragile ≠ apparel ≠ supplements)
• Return rate overlays (COD+Fashion in Bihar = 26.7% RTO)

Case: A home décor brand rerouted 12% of deliveries from XpressBees to Delhivery in UP East (Gorakhpur, Azamgarh) —

• SLA breach drop: 11.2%
• RTO down: 7.4%

FAQs (Frequently Asked Questions On Benefits of Data from 900+ D2Cs)

1. What does “data from 900+ D2Cs” actually mean?

It refers to aggregated, anonymised insights collected from over 900 direct-to-consumer brands across various categories, geographies, and growth stages. This data includes benchmarks, behavioural trends, and performance metrics.

2. How does access to this data benefit my D2C brand?

You can make more informed decisions by comparing your metrics against industry benchmarks. It also helps identify what's working for similar brands in terms of marketing channels, conversion tactics, retention strategies, and more.

3. Is the data anonymised and compliant with privacy laws?

Yes, all data is anonymised and aggregated, ensuring compliance with GDPR, India’s DPDP Act, and other global data protection standards.

4. Can smaller or newer D2C brands benefit from this data?

Absolutely. Emerging brands can use these insights to leapfrog the learning curve, avoid common mistakes, and adopt proven best practices earlier in their lifecycle.

5. How frequently is the data refreshed?

We update the dataset regularly, with most performance and behavioural trends refreshed on a monthly basis to ensure relevance and accuracy.

6. What types of insights are included?

Insights range from customer acquisition costs (CAC) by channel, top-performing campaign types, retention benchmarks, checkout funnel performance, subscription uptake, to logistics and RTO rates.

7. How is this different from market research reports?

Unlike static reports, this data is live, granular, and specific to digital-first D2C brands. It reflects real-time operational realities rather than generalised industry assumptions.

8. Do I need to be a Pragma customer to access these insights?

Some high-level insights are publicly available through our blog and whitepapers. Deeper, actionable intelligence is reserved for brands using Pragma’s suite of tools.

9. Can the insights be customised to my category (e.g. skincare, apparel, supplements)?

Yes, category-specific filtering is available, allowing you to access relevant metrics for your niche, such as AOV benchmarks for nutraceuticals or return rates for fashion.

10. What’s the advantage of using Pragma’s platform over hiring a data analyst?

Pragma provides plug-and-play intelligence from real-world D2C operations at scale — something even a skilled analyst would struggle to replicate without access to this breadth of industry data.