Sweet Analytics: Elevating eCommerce Marketing through Secure Data Collaboration.

Category: Advertising and marketing

Services: DevOps, cloud architecture design and review, managed engineering teams

Sweet Analytics
  • 25% increase in conversion rates
  • 30% improvement in overall customer engagement
  • 30% increase in advertising return on investment

About Sweet Analytics

Sweet Analytics is a platform for marketing and customer analytics catering to eCommerce retailers. Its services encompass an all-in-one marketing data automation tool, empowering businesses to boost sales and gain deeper insights into their customers.

Problem statement

  • Sweet Analytics is a platform specialized in marketing and customer analytics for the eCommerce sector. Although they had cloud infrastructure in place, it was with another cloud provider, which led to a number of challenges, specifically in the arenas of privacy-enhanced data collaboration, data security, and data interoperability.
  • Originally, Sweet Analytics was constricted by the limitations imposed by their previous cloud provider. The limited Linux installation options made it difficult to implement privacy-first, customizable data collaboration solutions that could adapt to varying security needs and governance policies.
  • As Sweet Analytics grew, the platform’s resource demands increased. However, the lack of global reach by their existing cloud provider limited the ability to collaborate on data at a larger, more secure scale. 
  • Moreover, the existing cloud architecture made it complicated to integrate privacy-enhanced data solutions seamlessly. The lack of essential features for secure data collaboration, like data clean rooms and identity providers, made it even more difficult to manage and interoperate data effectively. This prevented Sweet Analytics from gaining actionable campaign insights, attributing advertising success accurately, and enriching their datasets in a secure manner.
  • Sweet Analytics required a robust, secure, and globally accessible cloud infrastructure that would allow it to not only scale but also to put in place privacy-enhanced data collaboration solutions that could help them and their customers make the most out of their collective datasets.

Proposed Solution

We tackled the unique challenges faced by Sweet Analytics by integrating Amazon Elastic Kubernetes Service (EKS) and employing a series of privacy-enhanced AWS and third-party services.

  • Scalability and Interoperability: Amazon EKS was fundamental in providing a scalable and reliable environment for Sweet Analytics, allowing for the smooth management of containerized applications. Its ability to easily integrate with other privacy-centric AWS services, such as Amazon RDS and AWS KMS, facilitated the creation of a secure, interoperable platform. We adopted Karpenter for auto-scaling and Nginx ingress for load balancing, which ensured the platform’s stability during high-demand periods.
  • Enhanced Data Security: Given the focus on privacy-enhanced data collaboration, security was paramount. We utilized AWS KMS for encryption, creating secure data clean rooms that only authorized personnel could access. AWS IAM (Identity Access Management) was used to set up granular access control, allowing for collaboration among various stakeholders while ensuring consumer data protection.
  • Real-Time Monitoring and Proactive Security: To strengthen our data security measures further, we installed real-time monitoring and alerting mechanisms using Grafana, Prometheus, and Loki. This enabled us to detect and address any security issues promptly, ensuring both data integrity and consumer data protection.
  • Efficient Data Collaboration: We developed specialized modules for Sweet Analytics to facilitate privacy-enhanced data collaboration:
  • Admin Module: Offers secure access to offline reports and additional analytics, all of which adhere to stringent data privacy regulations.
  • E-commerce Module: Features a Privacy-Enhanced Sweet Benchmarking Index that delivers market insights while maintaining data security protocols. It allows data enrichment while ensuring that no sensitive information is compromised.
  • Core Module: Provides secure insights into various analytics such as discount analysis, shipping analysis, and SKU analysis. This is achieved through secure data rooms and identity providers to ensure that sensitive information is accessible only to authorized personnel.
  • Automated Deployment: We implemented a CI/CD pipeline with security best practices embedded, making sure that any new feature or service adheres to the data security and privacy standards set forth.

Key Metrics

  • Global Latency Reduction: Simform strategically deployed Sweet Analytics across multiple global Availability Zones (AZs), achieving a significant 30% reduction in system latency. This optimization not only strengthens data security and consumer data protection but also accelerates the delivery of campaign insights and advertising attribution.
  • Development Cost Savings: As part of Simform’s approach, we introduced cost-efficient spot instances for development environments, resulting in a remarkable reduction of over 50% in expenses. This approach not only bolsters cost-effective data solutions but also enhances campaign optimization for our clients.
  • Advertising ROI: Using our solution, Sweet Analytics and its data partners delivered an impressive 30% increase in advertising return on investment. This highlights the enhanced insights and attribution obtained through combined datasets, leading to more effective ad spend.
  • Campaign Efficiency: Through our solution, Sweet Analytics, helped businesses reduce advertising costs by 15% while simultaneously improving campaign efficiency. This signifies the effectiveness of streamlined data management in optimizing advertising strategies.
  • Customer Insights: Our solution empowered businesses to gain deep customer insights, including behavior analysis and preferences, leading to a 35% improvement in targeting accuracy.
  • Data Enrichment Impact: Data enrichment, facilitated by Sweet Analytics and data providers, resulted in a substantial 25% increase in conversion rates and a notable 30% improvement in overall customer engagement, underscoring the value of combined datasets.
  • Monitoring: With real-time monitoring enabled, we were able to set benchmarks for data access and usage patterns. Any deviation from these metrics triggers immediate alerts, allowing for swift action to prevent unauthorized data access or manipulation. This level of oversight assures both Sweet Analytics and its customers of the integrity of their data.
  • Data Encryption: Through AWS KMS, we implemented end-to-end encryption for data at rest and in transit. Encryption success rates, key rotations, and permission checks are monitored continuously, giving us quantifiable metrics on data security.

Architecture diagram


AWS Services

  • Amazon ECR (Elastic Container Registry) – We used Amazon ECR to securely store Docker images, allowing us to accelerate deployment cycles and enhance security protocols for containerized applications. The adoption of Amazon ECR supports encryption at rest, thereby contributing to a secure data handling process and offering a robust storage solution.
  • Amazon EKS (Elastic Kubernetes Service) – Amazon EKS was used to offer a managed service for our Kubernetes needs, enabling the scalable deployment of microservices and essential monitoring tools. It ensured data consistency and authorized access across multiple availability zones, which is key for secure, privacy-enhanced data collaboration.
  • Grafana, Prometheus, and Loki – Employing these monitoring tools, we could set up alerts for any anomalous activities, including data access and unusual spikes in system utilization. This real-time tracking mechanism enhanced our security posture by enabling immediate response to potential data breaches.
  • Karpenter – This open-source Kubernetes add-on provided dynamic, automated instance scaling, thereby optimizing resource allocation and cost. By doing so, it enabled us to focus more resources on data encryption and secure data-sharing features.
  • New Relic – New Relic was essential for Application Lifecycle Management, contributing to an improved, secure, and efficient development environment that aligns with privacy and data protection standards.
  • AWS Lambda – Used primarily for image processing and analytics in event-driven workloads, AWS Lambda offers serverless architecture, thereby minimizing the surface for potential security vulnerabilities. This approach is compliant with the philosophy of minimizing data exposure.
  • PostgreSQL RDS – By using PostgreSQL RDS for our analytical workload, we employed multiple layers of security features like SSL support and data encryption, guaranteeing secure data storage and ensuring compliance with privacy laws.
  • MongoDB Atlas – As our transactional database, MongoDB Atlas provides a platform that is both scalable and features built-in security protocols, ensuring that the data is secure while also being easily accessible for authorized personnel.
  • Amazon S3 – Amazon S3 was utilized to securely store application reports and as a data lake. It supports advanced encryption features and access policies, making it a secure and reliable choice for data storage needs.
  • AWS IAM (Identity and Access Management) – AWS IAM was fundamental in implementing fine-grained permission management, which ensured that only authorized personnel could access specific data. This restricted access is a vital part of our data security strategy.
  • AWS KMS (Key Management Service) – AWS KMS ensured the encryption of sensitive data in both PostgreSQL RDS and S3. By automating key rotations and enforcing strict access policies, AWS KMS played a critical role in our holistic data protection strategy.
  • Amazon CloudFront – CloudFront helped to speed up content delivery while also offering advanced security features such as HTTPS support and DDoS protection, enhancing both performance and security.
  • AWS DMS (Database Migration Service) – We used AWS DMS for a seamless migration of our PostgreSQL database from Digital Ocean to Amazon RDS, maintaining data integrity and ensuring a smooth transition that adhered to data protection guidelines.
  • Self-hosted OpenVPN – Our self-hosted OpenVPN in the EKS cluster guaranteed a secure and isolated environment for testing and development, preventing unauthorized access and potential data leaks.
  • Amazon CloudWatch – Used for RDS monitoring, CloudWatch provided an extra layer of security by allowing us to track performance metrics and set up alarms for any unusual activity, helping us to act swiftly in the case of a security event.

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