By Vipul Jain | Lead, Business Strategy & Analytics | ETML
Marketing campaigns booming, social media buzzing – but sales figures remain stubbornly flat? You’re not alone. Many businesses offering Cash on Delivery (COD) face a frustrating reality: a disconnect between marketing hype and the tangible revenue growth. Fake orders, sky-high return rates, and the dreaded RTO charges are silently sabotaging mane ecommerce brand’s growth. This blog post unveils a data-driven strategy to transform the COD nightmare into a sales success story.
Objective:
The objective is to achieve three key goals:
- Increase order count: The number of successful orders is intended to be significantly boosted while Cash on Delivery (COD) is offered as a payment option, thereby facilitating business growth.
- Minimize fake orders and RTO charges: The issue of fraudulent COD orders that are subsequently returned, leading to order loss and incurring RTO charges from shipping companies to be effectively combated.
- Return-To-Origin (RTO) charges from shipping companies : The positive growth observed in marketing campaigns is to be translated directly into a tangible increase in confirmed orders.
The Challenge:
Our research revealed several challenges faced by companies offering COD, particularly for high average order value (AOV) brands.
Cash on Delivery (COD) can be a double-edged sword for businesses. While it attracts customers hesitant to prepay, it also presents significant challenges. Fraudulent COD orders lead to returned items and financial losses. These “fake orders” further contribute to the burden by incurring additional “Return-To-Origin” (RTO) charges from shipping companies. Even with strong marketing that attracts potential customers, the conversion rate often remains stagnant due to this hesitancy to prepay, especially for high-value items. This disconnect between marketing efforts and actual sales is a major hurdle for businesses offering COD.
The Solution:
We implemented a comprehensive strategy to address these challenges:
- Strategic pincode exclusion: Data analysis was used to identify pincodes with historically lower delivery success rates. By temporarily excluding these areas from COD options, we minimized the risk of fake orders and improved overall delivery efficiency.
| City | Paid | COD | Cancelled | Grand Total | Cancelled Order % |
| Bangalore | 50 | 16 | -3 | 63 | -4.76% |
| Hyderabad | 35 | 12 | -2 | 45 | -3.95% |
| Mumbai | 20 | 5 | -2 | 23 | -10.26% |
| K.v.rangareddy | 15 | 10 | -6 | 19 | -28.21% |
| Chennai | 12 | 4 | -1 | 15 | -6.90% |
| Thane | 10 | 4 | -1 | 12 | -8.33% |
| Visakhapatnam | 11 | 2 | -2 | 11 | -14.29% |
| Gurgaon | 9 | 2 | -1 | 10 | -10.00% |
| Guntur | 6 | 4 | -1 | 9 | -10.53% |
| Surat | 8 | 3 | -2 | 9 | -23.53% |
| Coimbatore | 7 | 2 | -1 | 8 | -12.50% |
| Thiruvananthapuram | 5 | 2 | 1 | 8 | 6.67% |
| Kolkata | 5 | 2 | 1 | 8 | 6.67% |
| Kanchipuram | 7 | 2 | -2 | 7 | -21.43% |
| Krishna | 6 | 1 | -1 | 6 | -16.67% |
| Mysuru | 4 | 2 | 6 | 33.33% | |
| Solapur | 1 | 1 | -1 | 1 | -50.00% |
| Sikar | 1 | 2 | -1 | 1 | -100.00% |
| Purulia | 1 | 1 | 1 | 100.00% | |
| Bellary | 2 | 1 | -1 | 1 | -100.00% |
| Sirsa | 1 | 1 | -1 | 1 | -100.00% |
| Shivpuri | 1 | 1 | 1 | 100.00% | |
| Satara | 1 | 1 | -1 | 1 | -100.00% |
| Samastipur | 1 | 1 | 1 | 100.00% | |
| Pulwama | 1 | 1 | -1 | 1 | -100.00% |
2. Prepaid order event creation: We created dedicated events within marketing platforms to focus solely on prepaid orders. By optimizing campaigns around these events, we attracted customers more likely to complete their purchases.
3. Third-party payment gateway integration: A partnership with a third-party payment gateway enabled the implementation of a partial prepayment system. Charging a small upfront fee (e.g., 10%) discouraged fraudulent orders while minimizing inconvenience for genuine customers.
4. Prioritizing verified orders: We identified and prioritized verified COD orders, particularly those seeking faster delivery for time-sensitive items like party wear or formal shoes. This led to higher fulfillment rates and customer satisfaction.
5. Order cancellation analysis: Data from canceled COD orders was analyzed to identify patterns and address root causes such as late deliveries or packaging issues. To further enhance this process, we can increase the touchpoints with customers who have placed COD orders to gather feedback on why they canceled, or if the order was delivered, understand their experience and feedback.
The Results:
Implementing this data-driven approach yielded significant improvements in COD fulfillment, leading to:
| Month | Total orders | Shopify Total Orders | Backend Fulfilled Orders | Delivery % |
| Nov | 1,000 | 1,360 | 938 | 68.83% |
| Dec | 1,800 | 2,770 | 2,071 | 74.78% |
| Jan | 1,850 | 2,800 | 2,330 | 83.28% |
| Grand Total | 5,650 | 8,930 | 7,339 | 79.68% |
Cancellation Rate Reduction: We also observed a notable decrease in order cancellations following the implementation of our strategy. Here’s a comparison of cancellation rates for the past few months:
| Month | Paid | COD | Cancelled | Grand Total | Cancelled Order Value % |
| 2024-04 | 600 | 125 | 216 | 941 | 22.98% |
| 2024-05 | 450 | 241 | 59 | 750 | 7.84% |
| 2024-06 | 122 | 99 | 18 | 239 | 7.42% |
(Disclaimer : Actual numbers have been masked in all tables to protect the brand’s data privacy.)
