360° Customer Profiling Data

Your brand needs value, and it needs it ASAP!!‍

Customer acquisition costs are rising. Retention rates are declining. RTOs, SLA breaches, returns etc. are eating into margins. Yet, most D2C brands are still relying on fragmented data and intuition.

This is where 360° Customer Profiling becomes essential. Instead of looking at customers in silos—separating acquisition, conversions, and retention—a full-circle approach connects every touchpoint into one actionable intelligence system.

At Pragma, with insights from 800+ Indian D2C brands, we have identified how brands can optimise every phase of the customer journey—pre-purchase, post-purchase, and post-delivery—using structured, data-driven profiling methods.

Let’s break it down systematically.

The Three Challenges You’ll Always Face

In a digital-first business, customer relationships—whether for a new startup or a well-established company—there are 3 key stages:

1. Acquisition
2. Conversion
3. Engagement (Retention)

And perfecting the 3 comes only with data across customer journey stages…

I. Behavioural Data Profiling – The Foundation of 360° Customer Profiling in Indian D2C Ecommerce

Why do two customers landing on the same website behave completely differently?

One browses multiple categories, reads reviews, and completes a purchase within 10 minutes. The other exits within seconds. Both came from the same ad campaign, but only one converted.

This is where Behavioural Data Profiling comes in—tracking and analysing customer actions before they purchase to identify high-intent users, optimise conversion funnels, and cut acquisition costs.

For Indian D2C brands battling rising CAC (₹300-₹700 per customer) and a conversion rate of just 1-3%, understanding behaviour before the purchase is non-negotiable.

Let’s break down how structured profiling at the pre-purchase stage can transform acquisition efficiency.

1. Understanding Pre-Purchase Behaviour: The Hidden Indicators of Intent

In a typical Indian D2C store, 90-95% of visitors leave without buying. But that doesn’t mean they were never going to convert—it just means their intent wasn’t nurtured properly.

How do we differentiate between high-intent and low-intent visitors?
Through real-time behaviour tracking:

Key Customer Profiling Methods for Pre-Purchase Behaviour:

🔹 Session Tracking & Heatmaps: Identify where users drop off and optimise those touchpoints.
🔹 Engagement Scoring: Rank visitors based on time spent, clicks, and product views.
🔹 Propensity Modelling: Predict which customers are most likely to convert based on historical patterns.

🚀 Application: Indian D2C brands using event-based tracking and predictive profiling report a 25-30% increase in conversion rates.

2. Acquisition Cost Optimisation – Stop Paying for Low-Intent Traffic

Most Indian D2C brands run broad Facebook and Google ads with generic targeting, leading to:

• High bounce rates (50-80% on paid traffic).
• Poor add-to-cart rates (<5%).
• Escalating CPA with diminishing ROAS.

💡 Data Insight:

• Visitors engaging with 3+ pages have a 2X higher conversion probability.
• Visitors who interact with reviews or FAQs are 1.5X more likely to complete checkout.

Solution: Behaviour-Driven Ad Targeting

Instead of running blanket remarketing ads for every website visitor, segment them into structured profiles:

🚀 Application: Brands leveraging behaviour-based ad segmentation reduce ad waste by 20-30% while improving ROAS by 2-3X.

3. Checkout Funnel Profiling – Identifying Where Customers Drop Off

💡 Indian D2C Checkout Drop-off Rates:

• Overall Cart Abandonment: 65-75%
• Mobile vs. Desktop Abandonment: 78% vs. 66%
• COD Orders Drop-off Rate: Higher than prepaid by 15-20%

Instead of just blaming high cart abandonment on COD preferences, use checkout profiling to understand where and why users leave.

How Customer Profiling Fixes Checkout Abandonment:

🔹 Dynamic Checkout Experience: Returning users get a faster checkout with pre-filled details.
🔹 Exit Intent Triggers: Identify users likely to abandon and push real-time offers.
🔹 WhatsApp Abandonment Recovery: Use past purchase behaviour to personalise recovery messaging.

🚀 Application: Brands using behavioural checkout profiling recover 20-30% of lost checkouts.

4. The Role of AI & Machine Learning in Behavioural Profiling

Beyond basic analytics, ML models enable predictive customer profiling by:
✅ Scoring visitor intent in real-time.
✅ Recommending personalised product bundles based on engagement.
✅ Detecting drop-off risk before it happens.

🚀 Application: Indian D2C brands adopting AI-driven behavioural profiling see a 20-40% improvement in revenue per visitor.

Final Takeaways: Why Behavioural Profiling is the Foundation of 360° Customer Profiling

A 360° approach to customer profiling starts with pre-purchase behavioural tracking. Without it, brands are blindly spending on acquisition and failing to convert high-intent visitors.

Next Steps:

✅ If your ad costs are rising but conversions aren’t, start profiling pre-purchase behaviour.
✅ If cart abandonments are high, track behavioural exit points and optimise checkout flows.
✅ If ROAS is declining, move beyond basic demographic targeting to intent-based segmentation.

At Pragma, we help Indian D2C brands leverage customer profiling to drive measurable impact—lower acquisition costs, higher conversions, and improved retention.

II. Predictive Demographic & Location-Based Profiling – Precision Targeting for Indian D2C Brands

For most Indian D2C brands, marketing budgets disappear into a black hole of high CAC, low conversion rates, and poor ROAS.

Why?

Because brands still rely on broad, outdated segmentation instead of predictive demographic & location-based profiling.

Instead of targeting based on age, gender, and interests alone, leading brands are now:

✅ Mapping customer buying patterns across Tier-1, Tier-2, and Tier-3 cities.
✅ Using Pincode-level intelligence to detect high-RTO and low-conversion regions.
✅ Profiling customer spending behaviour to optimise pricing and promotions.

With insights from 800+ Indian D2C brands, let’s break down how demographic & location-based profiling can reduce acquisition costs, improve conversions, and slash operational losses.

1. The Problem with Generic Audience Targeting in India

Most Indian D2C brands spend ₹300-₹700 per acquisition but convert only 1-3% of visitors. A major reason? Inefficient audience segmentation.

💡 Data Insight:

• Tier-1 customers convert at 2X the rate of Tier-2/3 but have higher return rates.
• Tier-2/3 customers are 60% more likely to opt for COD, increasing RTO risks.
• Specific PIN codes have RTO rates exceeding 30%, impacting profit margins.

🚀 Impact of Predictive Demographic & Location Profiling:

• Brands using location-based segmentation have seen a 15-20% drop in CAC.
• Brands restricting COD in high-risk PIN codes reduce RTOs by 20-30%.
• Tier-2/3 focused brands using hyperlocal targeting see 25-40% better ROAS.

2. Segmenting Customers by Tier-1, Tier-2, and Tier-3 Spending Behaviour

Not all customers behave the same—even within the same state. The key to effective profiling is understanding how location influences AOV, payment preference, and repeat purchase probability.

🔍 Profiling Strategy:

• For high-AOV Tier-1 customers: Focus on premium products & prepaid discounts.
• For Tier-2/3 COD-heavy customers: Implement WhatsApp reconfirmation & RTO prevention methods.
• For repeat-buyers across all tiers: Use targeted retention campaigns with personalised offers.

🚀 Application: Brands optimising campaigns by city-tier behaviour have seen a 20-30% increase in conversion rates.

3. High-RTO PIN Code Profiling – Fixing the Biggest Profit Leak in Indian D2C

💡 The Harsh Reality of RTOs in India:

• 20-30% of all COD orders return to origin (RTO).
• Brands spend ₹50-150 per RTO attempt, causing direct revenue losses.
• Certain PIN codes have 3-5X higher RTO rates than the national average.

Instead of allowing COD orders from high-risk PIN codes, location-based profiling helps brands:

✅ Restrict COD in high-RTO zones.
✅ Apply prepaid-only policies for flagged regions.
✅ Reconfirm COD orders via WhatsApp to eliminate fake attempts.

🚀 Application: Brands using high-RTO PIN code profiling have cut COD-related losses by 25-40%.

4. Income & Spending-Based Customer Segmentation

Most brands use age and gender as primary targeting factors. But spending power is a better predictor of customer value than age alone.

🔍 Profiling Strategy:

• High-spenders get premium product recommendations & early-access launches.
• Budget-conscious buyers receive targeted discount promotions.
• Mid-tier buyers are nudged towards higher-AOV purchases with limited-time bundle deals.

🚀 Application: Brands applying income-based segmentation have increased AOV by 15-20%.

5. Leveraging AI & Machine Learning for Demographic & Location-Based Profiling

Instead of relying on historical sales reports, AI-driven profiling enables real-time segmentation:

🚀 Application: Brands implementing AI-driven profiling have seen 30-50% lower customer acquisition costs.

Final Takeaways: Why Demographic & Location-Based Profiling is Essential for Indian D2C Brands

For Indian D2C brands, predictive demographic & location-based profiling isn’t an option—it’s a necessity.

Next Steps:

✅ Audit your current targeting strategy—where are you losing money?
✅ Implement high-RTO PIN tracking & COD restriction policies.
✅ Use income-based segmentation to personalise offers & discounts.
✅ Leverage AI to detect high-converting locations & optimise ad spend.

At Pragma, we help brands integrate structured demographic & location-based profiling to increase ROAS, reduce RTOs, and maximise profitability.

III. Fraud & Risk Profiling – Controlling RTOs and Revenue Leaks in Indian D2C Ecommerce

For Indian D2C brands, RTO (Return to Origin) is the single biggest profit drain.

Every failed delivery doesn’t just mean lost revenue—it means wasted shipping costs, blocked inventory, higher operational expenses, and a damaged bottom line.

🔍 Reality Check: 

• Every 1 in 5 COD orders in India results in an RTO - 20-30% of all COD orders result in RTO
• ₹50-₹150 is lost per RTO attempt (pickup, packaging, logistics, re-shelving)
• Fraudulent RTOs—where customers falsely claim non-receipt—cost brands ₹200-₹500 per case
• Some PIN codes have RTO rates exceeding 40%, making them high-risk zones

The solution? Fraud & Risk Profiling.

Let’s break down how structured risk profiling can protect revenue and optimise logistics efficiency.

1. Breaking Down RTO – Why It Happens and How to Prevent It

Not all RTO cases are the same. To fix the problem, brands must categorise the root causes.

🚀 Impact of RTO Risk Profiling:

• Brands using COD re-confirmation reduce RTO rates by 20-30%.
• AI-based fraud detection lowers fake RTO attempts by 15-20%.
• High-RTO PIN code filtering helps brands cut COD-related losses by 25-40%.

2. Customer Profiling to Detect High-Risk Orders

One of the most effective ways to reduce RTO losses is identifying high-risk customers before dispatch.

Customer Behaviour Patterns That Indicate RTO Risk:

🚀 Application: Brands implementing customer-level RTO profiling have seen a 15-25% drop in fake COD orders.

3. High-RTO PIN Code Profiling – Eliminating Logistics Losses

Not all PIN codes are equal when it comes to RTO risk. Certain regions consistently generate more RTO cases due to poor logistics infrastructure, fake address density, or fraudulent buyers.

How to Identify High-RTO PIN Codes?

✅ Track historical RTO rates by region.
✅ Monitor SLA breaches & carrier partner performance by zone.
✅ Analyse COD vs. Prepaid conversion rates for risk-prone locations.

🚀 Application: Brands using real-time PIN code risk detection have lowered COD fraud cases by 20-30%.

4. AI & Machine Learning for Fraud Detection – Preventing Fake Orders & False Claims

Beyond basic filters, AI-driven risk profiling can detect fraudulent patterns before they impact revenue.

🚀 Application: Brands using AI-driven fraud detection see 40-50% fewer fraudulent COD orders.

5. RTO Risk Profiling in Action – A Case Study

A leading Indian fashion brand was struggling with:

❌ 27% RTO rate on COD orders.
❌ ₹8-10 lakhs/month in lost revenue due to returns & logistics failures.
❌ High-RTO zones causing operational inefficiencies.

Solution:

✔ Implemented AI-driven fraud detection to flag high-risk buyers.
✔ Restricted COD in high-RTO PIN codes & enforced prepaid-only for flagged orders.
✔ Used WhatsApp-based COD re-confirmation, reducing fake orders.

Results:

✅ RTO rate dropped from 27% to 14% within 90 days.
✅ ₹4-5 lakhs/month saved in operational costs.
✅ Higher COD conversions in low-risk zones, improving overall revenue.

🚀 Proof That Structured RTO Risk Profiling Works.

Final Takeaways: Why Every Indian D2C Brand Needs Fraud & Risk Profiling

Fraud & RTO risk is not a cost of doing business—it’s a problem that can be fixed.

By implementing structured risk profiling methods, brands can:

✅ Cut COD-related losses by 25-40%.
✅ Reduce fake RTO attempts using AI-driven fraud detection.
✅ Optimise logistics by identifying high-risk PIN codes.
✅ Increase profitability by enforcing smart COD policies.

At Pragma, we help brands integrate fraud & risk profiling models to reduce losses, optimise cash flow, and build a sustainable D2C business.

IV. SLA Compliance & Customer Service Profiling – Fixing Delivery Failures & Support Bottlenecks in Indian D2C Ecommerce

Reality Check: Delayed deliveries result in a 45% drop in repeat purchase probability.

A 360° approach to SLA tracking involves:

• Carrier Performance Data: Tracking which logistics partners are causing delays.
• Historical Delivery Performance: Profiling PIN codes with frequent SLA breaches.
• Customer Complaint Analysis: Identifying customers who frequently report issues.

Application: Brands using delivery & SLA profiling reduce customer churn by 15-20%.

In Indian D2C ecommerce, delays kill customer trust, and poor support destroys retention.

A failed SLA (Service Level Agreement) is more than just a missed deadline—it signals:
❌ Operational inefficiencies that increase logistics costs.
❌ Customer frustration that leads to lower repeat purchases.
❌ Negative NPS scores, impacting brand reputation.

🔍 The Reality of SLA Breaches in Indian D2C:

• Delayed deliveries lead to a 45% drop in repeat purchase probability.
• 20-25% of all customer service tickets are SLA-related issues (delayed shipments, failed pickups, refund delays).
• Brands that automate SLA tracking reduce customer complaints by 30-40%.

The solution? SLA Compliance & Customer Service Profiling.

By tracking carrier performance, identifying high-risk delivery zones, and profiling customers based on complaint history, brands can fix operational bottlenecks and enhance customer experience.

Let’s break down how structured SLA profiling improves both logistics and customer service efficiency.

1. Identifying SLA Breaches – Where Do Delays Happen?

SLA failures aren’t random—they follow predictable patterns. The first step in optimising operations is identifying where delays occur.

🚀 Application: Brands implementing SLA breach profiling have reduced delivery failures by 20-30%.

2. Carrier Performance Profiling – Fixing Logistics Failures

Not all courier partners perform equally across all regions. Some have higher delays, frequent returns, or poor last-mile success rates.

How Carrier Profiling Works:

✅ Track on-time vs. delayed deliveries per partner.
✅ Identify regions with repeated logistics failures.
✅ Switch to high-performing carriers in risk-prone areas.

🚀 Application: Brands using data-driven carrier allocation improve on-time delivery rates by 25-35%.

3. High-Risk SLA Zones – Avoiding Delivery Delays Before They Happen

Some areas are high-risk zones for SLA breaches, where logistics partners consistently underperform.

How to Identify SLA Risk Zones?

✅ Track PIN codes with repeated delivery delays.
✅ Monitor SLA performance across Tier-1, Tier-2, and Tier-3 locations.
✅ Use predictive analytics to flag potential failures before they occur.

🚀 Application: Brands that optimise SLA compliance by region have lowered customer complaints by 30%.

4. Customer Service Profiling – Reducing Support Load & Automating Resolutions

SLA breaches lead to ticket overload in customer support teams.

The Problem with Reactive Customer Service:

❌ 40-50% of tickets are SLA-related issues (delayed deliveries, failed pickups, refunds).
❌ Manual ticket handling increases response times by 3-5 days.
❌ Poor ticket resolution leads to a 20% drop in repeat purchases.

Solution: Customer Service Profiling & Ticket Automation

🔍 Types of SLA-Based Tickets:

🚀 Application: Brands implementing ticket automation reduce customer complaints by 30-40%.

5. AI & Machine Learning for SLA & Customer Service Optimisation

Beyond manual tracking, AI-driven SLA profiling enables:

✅ Predicting high-risk delays before they happen.
✅ Auto-routing shipments to better-performing carriers.
✅ Prioritising high-value customers for faster resolutions.

🚀 Application: Brands using AI-based SLA & support profiling have improved customer retention by 20-30%.

6. SLA & Support Optimisation – A Case Study

A Indian D2C skincare brand was facing:

❌ SLA breaches on 18% of orders, leading to customer dissatisfaction.
❌ Support tickets taking 4-5 days to resolve.
❌ High delivery failures in Tier-2/3 regions.

Solution:

✔ Implemented SLA compliance tracking to detect high-failure zones.
✔ Switched carriers for underperforming PIN codes.
✔ Automated 60% of customer support queries related to delivery delays.

Results:

✅ SLA breaches reduced from 18% to 9% within 3 months.
✅ Customer service response time improved by 50%.
✅ Higher repeat purchases from Tier-2/3 customers due to better delivery performance.

🚀 Proof That SLA & Support Profiling Works.

Final Takeaways: Why SLA Compliance & Customer Service Profiling is Critical for Indian D2C Brands

For Indian D2C brands, SLA breaches and support inefficiencies are avoidable problems.

By implementing SLA tracking, carrier profiling, and AI-driven customer service automation, brands can:
✅ Improve on-time deliveries, reducing customer churn.
✅ Lower support costs with proactive ticket automation.
✅ Increase retention by resolving issues before they escalate.

At Pragma, we help brands integrate SLA compliance & customer service profiling to fix operational inefficiencies and maximise customer satisfaction.

V. Post-Purchase Engagement Profiling – The Key to Maximising Repeat Purchases in Indian D2C Ecommerce

For most Indian D2C brands, the biggest mistake is treating all customers the same after they buy.

🔍 Reality Check:

• Acquiring a new customer costs 5X more than retaining an existing one.
• A 5% increase in retention can boost profits by 25-95%.
• 80% of Indian D2C brands allocate 70%+ of their budgets to acquisition, but only 20-30% of customers return for a second purchase.

Yet, most brands still rely on:
❌ Generic post-purchase emails that customers ignore.
❌ No segmentation of repeat vs. one-time buyers.
❌ Inefficient loyalty programs that don’t target high-value customers.

The solution? Post-Purchase Engagement Profiling.

By tracking post-purchase behaviour, categorising customers by retention probability, and leveraging AI-driven re-engagement strategies, brands can increase repeat purchases, optimise LTV, and improve marketing efficiency.

🔍 Key Profiling Metrics for Repeat Buyers:

• RFM (Recency, Frequency, Monetary) Analysis: Segments customers into High-Value, At-Risk, or Churned.
• Purchase Frequency Profiling: Identifies whether a product purchase cycle is one-time or repeatable.
• Net Promoter Score (NPS) Correlation: High NPS customers are 3X more likely to buy again.

Let’s break down how structured post-purchase profiling can drive sustainable growth for Indian D2C brands.

1. Understanding Post-Purchase Behaviour – Who Will Buy Again?

Not every customer will return. The key is identifying which ones are likely to repurchase—and why.

🚀 Application: Brands using data-driven post-purchase engagement increase repeat purchases by 25-30%.

2. RFM (Recency, Frequency, Monetary) Analysis – Segmenting Customers by Value

What is RFM Analysis?

RFM (Recency, Frequency, Monetary) profiling segments customers based on:
✅ Recency: How recently they purchased.
✅ Frequency: How often they buy.
✅ Monetary Value: How much they spend.

RFM Segmentation Framework for Indian D2C Brands

🚀 Application: Brands optimising RFM-based targeting have seen 20-40% better retention rates.

3. AI & Machine Learning for Predicting Repeat Purchases

🔍 Post-Purchase Retention Metrics for Indian D2C:

• Customers who receive an NPS survey within 24 hours are 15% more likely to repurchase.
• High-NPS customers are 3X more likely to refer new buyers.
• Churned customers who receive a targeted re-engagement campaign return at a rate of 10-15%.

How AI-Driven Profiling Predicts Retention & Churn:

🚀 Application: AI-driven post-purchase profiling can increase retention by 20-30% with predictive re-engagement.

4. Personalised Retention Campaigns – Beyond One-Size-Fits-All Marketing

Most brands send generic discount codes to all customers, hoping for repurchases. This approach fails because not all customers need a discount to return.

How to Optimise Retention Campaigns Based on Profiling:

🚀 Application: Brands using personalised retention offers see 35-40% higher engagement rates.

5. Re-Engagement Timing – When to Target Customers for Maximum Impact

Not all customers respond to retention offers at the same time.
📌 Timing is critical.

🔍 Best Practices for Post-Purchase Retargeting:
✅ First 30 Days: Educate new buyers with product tutorials & usage tips.
✅ 30-60 Days: Encourage repurchase with tailored product recommendations.
✅ 60+ Days: Re-engagement campaigns for at-risk customers.

🚀 Application: Brands that optimise re-engagement timing increase repeat purchases by 20-25%.

6. Loyalty & Subscription Models – Converting One-Time Buyers into Lifelong Customers

Indian D2C brands that move beyond transactional sales into loyalty programs & subscriptions see 35-50% higher retention rates.

🚀 Application: Brands integrating structured loyalty programs see 2X higher LTV (Lifetime Value).

7. Case Study – How a D2C Brand Increased Retention by 35%

A Indian D2C personal care brand faced:

❌ Low repeat purchase rates (only 18% of customers returned).
❌ High churn among first-time buyers.
❌ Ineffective loyalty program engagement.

Solution:

✔ Implemented AI-driven churn prediction to detect at-risk customers.
✔ Personalised post-purchase WhatsApp flows based on customer profiles.
✔ Introduced a subscription model for high-value customers.

Results:

✅ Repeat purchases increased from 18% to 35% within 6 months.
✅ Loyalty program participation grew by 40%.
✅ Higher LTV from subscription-based customers.

🚀 Proof That Structured Post-Purchase Profiling Works.

Final Takeaways: Why Every Indian D2C Brand Needs Post-Purchase Engagement Profiling

For Indian D2C brands, optimising post-purchase engagement isn’t an option—it’s a necessity for sustainable growth.

By implementing structured profiling, predictive AI models, and targeted retention campaigns, brands can:

✅ Increase repeat purchases & reduce churn.
✅ Personalise engagement for different customer segments.
✅ Boost CLV with loyalty & subscription programs.

At Pragma, we help brands integrate post-purchase profiling to build long-term customer relationships.

VI. Retargeting with Purchase & Interest-Based Profiling – The Science of Re-Engagement in Indian D2C Ecommerce

For Indian D2C brands, most marketing budgets are wasted on reacquiring customers who never converted in the first place.

🔍 The Reality of Retargeting in India:

• 80% of customers don’t buy on their first visit.
• Cart abandoners convert 2X more if retargeted within 24 hours.
• Generic remarketing ads have a CTR of 1-2%, while personalised dynamic retargeting gets 5-8%.

Yet, most brands treat all past visitors the same.

Instead of showing the same product ads to every website visitor, smart D2C brands segment audiences based on purchase & interest-based profiling.

Instead of retargeting all past customers the same way, 360° profiling enables segmented re-engagement:

• Product-Based Retargeting: Customers who bought skincare last time? Recommend complementary serums instead of generic promotions.
• Seasonal Behaviour Profiling: Customers who buy summer apparel? Retarget them at the start of the next season.

Application: Brands using interest-based profiling see 50% higher CTRs on retargeting campaigns.

The Result?

✅ Higher ROAS (3-4X instead of 1-2X).
✅ Lower CAC (Cost of Acquisition) by targeting high-intent users first.
✅ Increased conversion rates (5-15% uplift from personalised retargeting).

Let’s break down how to structure retargeting based on purchase intent, browsing behaviour, and past buying history.

1. The Problem with One-Size-Fits-All Retargeting

Most brands still use basic website retargeting:

❌ Show the same product ad to everyone who visited the website.
❌ Offer flat discounts to all cart abandoners (even those who don’t need one).
❌ Push random bestsellers instead of personalised recommendations.

Why This Fails:

• High-intent visitors get the same ads as window shoppers.
• Customers who already purchased get unnecessary discount offers.
• Engaged users see irrelevant ads instead of complementary products.

🚀 Solution: Segment & retarget based on customer profiling.

2. Retargeting Based on Purchase Behaviour

🔹 First-Time Visitors vs. Returning Customers:

• First-time visitors need more education & trust-building.
• Returning visitors already have interest—nudge them towards checkout.

🚀 Application: Brands optimising first-time vs. returning visitor retargeting improve ROAS by 2-3X.

3. Interest-Based Profiling – Tracking Browsing Behaviour for Smart Retargeting

🔍 Indian D2C data shows that:

• Visitors who view 3+ pages in one session are 2X more likely to buy.
• Customers who spend over 90 seconds on a product page have a 15% higher purchase intent.

Segmenting Visitors Based on Browsing Patterns:

🚀 Application: Brands using interest-based retargeting reduce wasted ad spend by 20-30%.

4. Cart Abandonment Retargeting – Fixing the ₹1000 Crore Lost Sales Problem

💡 Indian D2C brands lose ₹1000+ crores annually due to cart abandonments.

How to Fix It?

Instead of spamming all cart abandoners with discounts, segment them into smart retargeting buckets:

🚀 Application: Brands using cart-based retargeting recover 20-30% of lost revenue.

5. Retargeting Past Buyers – Maximising Repeat Purchases

🔍 Key Data on Indian D2C Repeat Purchases:

• Existing customers have a 60-70% higher conversion rate than new visitors.
• Past buyers who receive targeted offers are 3X more likely to repurchase.

Segmenting Past Customers for Smarter Retargeting:

🚀 Application: Brands implementing past buyer retargeting increase repeat purchase rates by 30-40%.

6. ML-Driven Retargeting – Personalising at Scale

Instead of manual segmentation, ML-driven models:

✅ Predict purchase probability & retarget accordingly.
✅ Adjust offers dynamically based on past behaviour.
✅ Exclude low-intent users, reducing wasted ad spend.

🚀 Application: AI-powered retargeting optimisation boosts conversion rates by 15-25%.

7. Case Study – How a D2C Brand Increased ROAS by 3X with Smart Retargeting

A leading Indian fashion D2C brand struggled with:
❌ High ad spend but low conversions (ROAS stuck at 1.8X).
❌ Cart abandonment rate at 70%.
❌ Past buyers not returning for second purchases.

Solution:

✔ Implemented AI-based purchase & interest profiling.
✔ Segmented cart abandoners based on AOV & COD preference.
✔ Used dynamic retargeting ads instead of static creatives.

Results:

✅ ROAS increased from 1.8X to 5.2X within 60 days.
✅ Cart abandonment recovery improved by 25%.
✅ Repeat purchases increased by 40%.

🚀 Proof That Smart Retargeting Works.

Final Takeaways: Why Every Indian D2C Brand Needs Purchase & Interest-Based Retargeting

For Indian D2C brands, retargeting is no longer about just showing ads—it’s about showing the right ads to the right customers at the right time.

At Pragma, we help brands integrate purchase & interest-based retargeting to maximise conversions and reduce ad wastage.

Why 360° Customer Profiling is a Competitive Advantage

Brands that ignore profiling leave revenue on the table.

For Indian D2C brands, competition is no longer just about pricing or product quality—it’s about how well you understand your customers.

Brands that rely on gut feeling, demographic targeting, and generic marketing are losing to those that use data-driven customer profiling to optimise acquisition, reduce RTOs, and maximise retention.

Why Does This Matter?

• Customer acquisition costs (CAC) in India have surged by 60% in the last 3 years.
• RTO losses cost Indian D2C brands ₹1000+ crores annually.
• Retention is the real profit driver—repeat customers generate 67% more revenue than first-time buyers.

Yet, most brands don’t have a structured customer profiling system.

Those that do? They achieve:
✅ 2-3X better ROAS (Return on Ad Spend).
✅ 20-40% lower RTO rates.
✅ 30-50% higher retention & repeat purchase rates.

With 360° Customer Profiling, you aren’t just collecting data—you’re using it to drive higher conversions, lower operational costs, and increase retention.

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FAQs (Frequently Asked Questions On 360° Customer Profiling Data)

1. What is 360° customer profiling, and why does it matter for D2C brands?

• It’s the complete view of a customer’s behaviour, preferences, and interactions across all touchpoints—website, WhatsApp, social media, and past orders.
• Helps brands personalise marketing, reduce returns, increase LTV (Lifetime Value), and optimise ad spend.
• Essential for Indian D2C brands, where repeat purchase rate is <30% without personalisation.

2. What data points should be included in a 360° customer profile?

• Purchase history: What, when, and how frequently they buy.
• RTO & return patterns: Flagging serial returners and high-risk COD users.
• Engagement data: WhatsApp, email, and website interaction history.
• Browsing behaviour: Frequently viewed categories & abandoned cart trends.
• Payment preference: Prepaid vs. COD usage and risk scoring.

3. How can brands collect 360° customer data without being intrusive?

• WhatsApp opt-ins: Encourage users to opt in for exclusive deals, offering early access.
• Loyalty programs: Reward users for sharing data (e.g., birthdays, preferences).
• AI-driven website tracking: Monitor product views & abandoned carts.
• Post-purchase surveys: Get insights on shopping experience and expectations.

4. How can brands use 360° profiling to improve marketing ROI?

• Hyper-personalised WhatsApp campaigns: Send product suggestions based on past purchases.
• Smart COD nudges: Identify high-RTO customers and push them toward prepaid with exclusive discounts.
• Predictive restocking alerts: AI-based reminders when a customer is likely to reorder.
• Ad targeting refinement: Reduce wasted ad spend by retargeting engaged, high-value customers instead of one-time buyers.

5. What are the biggest challenges in implementing 360° customer profiling?

• Data silos: Unifying data from WhatsApp, website, CRM, and marketplaces.
• Privacy concerns: Ensuring compliance with Indian data protection laws.
• Real-time processing: Keeping customer profiles updated dynamically.

Tech integration: Needing robust CRM & AI-driven analytics tools to process insights effectively.