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The holiday season is like a blockbuster movie. 

But here’s the twist: each city is a different theatre, and people love different genres! That’s where location-based hyper-targeting steps in, making sure your show is a hit in every town.

Especially the holiday releases, because those are the ones that bring-in the beaucoup bucks 💸

We’ll split the benefits of location based data into 3 stages to make this simple…

1. Pre-Purchase Location Data

2. Post-Purchase Location Data, and

3. Post-Delivery Location Data

A. Pre-Purchase Location Data

1) Open Rate, CTR, and Other Stats for Holiday Campaigns

Example Data:

• In Mumbai, holiday campaign open rates increased by 15%, with a click-through rate (CTR) of 8% during Diwali.

• Chennai experienced a 12% open rate and a 10% CTR during Diwali.

Insights: Higher engagement rates during holidays in Chennai; consider tailoring holiday campaigns for specific regions.

2) Product(s) Browsed - Demand and relevant Customer Info

Example Data:

• Western clothing saw a surge in demand in Kolkata, with a 20% increase in product views, especially among users aged 18-25.

• Electronic gadgets were popular in Mumbai, with a 15% higher engagement rate among users aged 25-35.

• Individual product demand could be analysed and compared with other competitors as well.

Insights: Tailor product recommendations based on the most browsed categories and the average age group in each region.

3) RTO History - Historic Data (from 450+ brands) of Individual Consumers to gauge risk

Example Data:

• Consumer A in Bengaluru has a 30% RTO rate over the past six months, mainly due to size-related issues.

• Monitoring Consumer B in Chennai revealed a consistent 5% RTO rate, indicating a stable purchasing pattern.

Insights: Proactively manage customer relationships with high RTO percentages through personalised support or targeted campaigns.

4) Weather-Based Targeting:

Consider adapting your promotions based on weather conditions in different regions. For example, promote warm clothing in colder regions and summer wear in warmer areas.

B. Post-Purchase Location Data

1) Checkout Data - COD or Prepaid, Device Used, Age of Consumer etc.

Example Data:

• In Kolkata, 40% of consumers prefer COD, especially on mobile devices, with a majority aged 35-45.

• Prepaid transactions are dominant in Chennai, where 60% of users aged 25-30 prefer online payments.

Insights: Customise payment options based on preferences in each region; consider age demographics for targeted promotions.

2) Personalisation Data - Most Used Coupons, Preferred Mode of Payment etc.

Example Data:

• Coupon XYZ is most used in Mumbai, accounting for a 25% increase in conversions during the checkout process.

• Users in Bengaluru frequently access the FAQ section, with 70% of them preferring detailed information before making a purchase.

Insights: Tailor personalised messages and support materials based on region-specific preferences.

3) Cross-Selling & Upselling Data - Success Percentages for Different Combinations

Example Data:

• Combining Product A and Product B resulted in a 25% higher success rate in upselling in Chennai.

• Cross-selling accessories with electronic gadgets showed a 30% success rate in Mumbai.

Insights: Adapt cross-selling and upselling strategies based on regional preferences and success rates.

4) Inventory Management Processing

Optimise your inventory based on regional demand. Ensure that popular products in one region are readily available, and consider tailoring your product offerings to meet specific local preferences.

C. Post-Delivery Location Data

1) Delivery Experience - Percentage of On-Time Deliveries, SLA Breaches etc.

Example Data:

• Mumbai consistently achieves a 95% on-time delivery rate, while Bengaluru experiences occasional SLA breaches due to logistics challenges.

Insights: Address logistics issues in Bengaluru to improve on-time delivery and reduce SLA breaches.

2) Logistic Partner Comparison - Fulfilment Rates and Pricing per location

Example Data:

• Logistic Partner X has a 90% fulfilment rate in Chennai but is costlier compared to Partner Y, which has an 85% fulfilment rate.

• Evaluating pricing and fulfilment rates helps optimise logistics partnerships in different regions.

Insights: Optimise logistic partnerships based on fulfilment rates and pricing for each region.

3) Feedback - Rating Received for Product, Service, Support etc.

Example Data:

• Kolkata consistently rates products at 4.5 out of 5, with positive feedback on customer service.

• Bengaluru reports a dip in ratings due to support issues, emphasising the need for improvement in post-purchase support services.

• Calculate cumulative ratings with the help of customised algorithm.

Insights: Identify areas for improvement in product quality, service, and support based on region-specific feedback.

4) Returns/Exchanges/Refunds Data

Example Data:

• Return rates for clothing items are higher in Mumbai, whereas electronics face more exchanges in Chennai.

• Understanding return patterns assists in tailoring policies and optimising inventory.

Insights: Streamline return and refund processes in Bengaluru to reduce processing time and improve customer satisfaction.

That’s the end of our talk on “Location-based Hyper-targeting for Holiday Season”...

☕ See you on the next coffee date!

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