Background
A mid-sized e-commerce company experiencing growth in its user base faced escalating AWS infrastructure costs as its operations expanded. By the end of 2022, the rising expenditure on AWS services prompted the company to explore cost optimization solutions.
Goals
1. Reduce infrastructure costs while maintaining performance.
2. Enhance the efficiency of cloud resources.
Strategy
1. Right-Sizing with EC2 Instances
Initially, the company used a standard EC2 instance type for all applications. They adopted AWS Compute Optimizer, which recommended instance types and sizes based on workloads, leading to a reduction in over-provisioning of compute power.
2. Leveraging Spot Instances
For workloads that were not time-sensitive, the company started using EC2 Spot Instances, taking advantage of unused EC2 capacity at up to a 90% discount compared to On-Demand prices.
3. Implementing Auto Scaling
To manage fluctuations in traffic without maintaining excess capacity during off-peak hours, the company implemented Auto Scaling groups, ensuring they only paid for the resources required.
4. S3 Object Lifecycle Policies
By implementing object lifecycle policies on S3 buckets, the company automatically transitioned older data to more cost-effective storage classes or archived/deleted data based on its age and relevance.
5. Reservations and Savings Plans
For predictable and stable workloads, the company purchased Reserved Instances (RIs) and AWS Savings Plans, securing significant discounts compared to On-Demand rates.
6. Optimizing Data Transfer
They used AWS’s VPC endpoint for S3 to reduce data transfer costs between their VPC and S3. Additionally, Amazon CloudFront caching was utilized to minimize data retrieval expenses.
7. AWS Trusted Advisor
The company regularly consulted AWS Trusted Advisor, which provided real-time guidance on resource provisioning following AWS best practices. It helped identify opportunities for cost savings, security enhancements, and performance improvements.
Results
- Sales Boost: The improved sales forecasting led to a 15% increase in sales revenue, driven by fewer stockouts and effective product promotions.
- Elevated Engagement: AI chatbots handled 40% of customer queries, allowing human agents to focus on complex issues, resulting in a 20% increase in customer satisfaction.
- Enhanced Personalization: The AI-driven recommendation system boosted repeat purchases by 25%, thanks to tailored shopping experiences.
Conclusion
Through the strategic implementation of AWS AI solutions, this retailer successfully transformed its retail operations. It delivered seamless and personalized shopping experiences to its customers, surpassing its original objectives and positioning itself as an e-commerce innovator.
Future Outlook
With its successful AI adoption, the retailer looks ahead to explore further AWS AI services. This includes delving deeper into sentiment analysis and potentially exploring virtual trial rooms, ensuring they continue to lead in retail technology.
