Tackling Failed Deliveries: A Golden Opportunity for E-commerce Brands

India’s e-commerce sector is soaring high, powered by the dual benefits of online shopping convenience and COD options. According to the Brand Xcel 2024 Report, among the top 50 brands, Amazon jumped from 3rd to 1st and Flipkart from 7th to 5th, reflecting the rising demand for convenience, transparency, and personalization in eCommerce. 

Valued at $70 billion and projected to reach $325 billion by 2030, the ecommerce industry has attracted significant investments, like the most recent, Google’s $350 million stake in Flipkart. However, this rapid growth comes with a significant challenge: the high rate of failed deliveries, particularly COD orders. 

However, failure to keep pace with these expectations of convenience has a big impact on brands — and not a positive one. Once upon a time ago price, quality of a product, and customer service steered the customer experience, the spotlight is now on delivery.

The expectations for delivery have evolved significantly, with the surge in faster, easier, and more transparent delivery options further accelerating this shift. This has given rise to the Delivery Economy, where customers increasingly demand low-cost, rapid, and highly transparent delivery of goods. On-demand delivery apps, same-day delivery options, and subscription-based delivery services are the primary forces driving these changing expectations.

The Hidden Costs of Failed Deliveries

No one hates failed deliveries more than brands and sellers. The nightmare significantly increases operational costs, as logistics and handling expenses skyrocket. Frequent returns also disrupt inventory management, complicating stock availability and planning. Most importantly, repeated delivery failures erode customer trust, negatively impacting the overall shopping experience and damaging the retailer’s reputation.

In India, approximately 60%-65% of e-commerce orders are COD, with around 25%-30% of these orders resulting in Return to Origin (RTO). In contrast, unsuccessful deliveries for pre-paid orders range from 2%-3%. This issue shackles major players like Amazon and Flipkart, along with numerous Direct-to-Consumer (D2C) brands, impacting operational efficiency and customer satisfaction. While new D2C brands are bearing the bigger brunt, relatively established brands have already navigated this learning curve. 

Consumers often find themselves at a disadvantage because their contract is with the retailer rather than the parcel company responsible for the delivery.

Cracking the Delivery Code! 

Brands Can Outsmart Failed Deliveries with These Tactics:

  • Prepaid Payment Incentives: Offer discounts for prepaid orders to encourage online payments.

  • 50-50 Payment Option: Introduce a 50-50 payment option where customers pay half upfront and half on delivery.

  • COD Charges: Implement additional charges for COD orders to discourage its use.

  • Enhanced Customer Communication: Use SMS and email reminders to confirm delivery times with customers.

  • Flexible Delivery Schedules: Provide options for customers to choose convenient delivery slots.

  • Plan deliver sequences: Utilize predictive algorithms to identify high-risk packages and schedule them at optimal times for higher success rates. Tailor delivery sequences to specific locations for best results

  • Detailed Order Tracking: Offer real-time tracking and updates to keep customers informed about their deliveries.

  • Decentralize delivery systems: Companies should establish multiple smaller warehouses with flexible leases rather than one large hub. This approach increases efficiency, allows quicker adaptation to demand changes, and improves delivery times. Localized warehouses also enable hiring drivers familiar with their neighborhoods, enhancing route navigation and reducing lead times

  • Loyalty Programs: Reward customers who consistently opt for prepaid orders with loyalty points or rewards.

  • Customer Behavior Analysis: Utilize AI and machine learning to predict and mitigate delivery failures.

  • Pin Code Risk Assessment: Use historical data to identify high-risk areas and adjust strategies accordingly.

  • Improved Address Verification: Enhance address verification processes to reduce delivery errors.

Is analytics the answer?

E-commerce Giants, Amazon and Flipkart leverage advanced analytics based on PIN codes and customer behaviour to tag high-risk customers, often requiring prepaid orders in such cases. However, using historical data to predict RTO rates comes with its challenges. 

While these tech tools can increase successful deliveries, they may also result in lost orders. Coordination between logistics companies and e-commerce platforms, which both maintain detailed consignee profiles, could be a powerful solution if improved. Additionally, predicting customer intent based on past behaviour is tricky, especially for new users or different brands. Each RTO-saving tool addresses only a small part of the problem, achieving modest reductions only in RTO rates. 

The Path Forward

Brands must tackle the issue from the top of the funnel by ensuring they are selling to customers with the right intent. This involves leveraging analytics and historical data to predict high-risk customers and implementing measures to mitigate risks. 

Companies are going all out to improve customer contact. Ecom Express plans to launch a 'contactability suite' by June, creating a direct communication channel with customers, complementing the delivery boy’s interaction to ensure successful deliveries.

By fine-tuning systems and addressing on-ground realities, companies can significantly improve the failed delivery scenario, consequently leading to relief for delivery agents, boosting customer satisfaction and cutting costs simultaneously.

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USA

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5741 Cleveland street, Suite 120, VA beach, VA 23462

SINGAPORE

Market Xcel Data Matrix Pvt. Ltd.

190 Middle Road, # 14-10 Fortune Centre, Singapore - 188979

NEW DELHI

17, Okhla Industrial Estate Phase 3 Rd, Okhla Phase III, Okhla Industrial Estate, New Delhi,

Delhi 110020

Market Xcel Data Matrix © 2023 (v1.1.3)

USA

Market Xcel Data Matrix

5741 Cleveland street, Suite 120, VA beach, VA 23462

SINGAPORE

Market Xcel Data Matrix Pvt. Ltd.

190 Middle Road, # 14-10 Fortune Centre, Singapore - 188979

NEW DELHI

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Delhi 110020

Market Xcel Data Matrix © 2023 (v1.1.3)