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AWS Retail Global

CouponBirds

Data Lake / Data Warehouse Modernization · Marketing Personalization

Shoppers are looking for coupons to save money, and merchants are using coupons to attract shoppers, increase sales, and improve customer loyalty. However, in the process of expanding and upgrading its business, the company encountered a number of technical challenges: The difficulty and long development cycle in large-scale, real-time data governance: CouponBirds has built a large user base and a large database of merchants thanks to 9 years in the industry.

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4x
In addition, it also uses multiple fully managed products and services of AWS to significantly reduce the cost of data governance by 50%.
Reducing product development cycle to 3 months The entire data governance architecture of AWS is not just easy to scale up and maintain.
The company’s product development cycle has been reduced from 8 months to 3 months.

Reported impact

Business outcome

A 4-time improvement of data governance and a 50% cost reduction for data processing By using data stream processing tools like Amazon Kinesis Data Streams and Amazon Data Firehose, CouponBirds is able to easily handle large-scale data streams from multiple sources and improve its system’s data governancee by 4 times. In addition, it also uses multiple fully managed products and services of AWS to significantly reduce the cost of data governance by 50%. Key reported metrics include improvement in data governance 4x; 4x; cost reduction in data governance 50%.

  • Compliance
  • Cost Reduction
  • Customer Experience
  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

Founded in 2015, CouponBirds provides information about high-quality discounts and coupons to online shoppers worldwide. To efficiently handle the growing data traffic and improve user experience, CouponBirds adopted AWS products like Amazon Kinesis Data Streams, Amazon Data Firehose, AWS Lambda, Amazon OpenSearch Service, Amazon ELB, Amazon CloudSearch, Amazon Personalize, AWS IAM, and Amazon VPC,to build a new data governance architecture. This new data governance architecture allows CouponBirds to be better-positioned to address the needs of its users around the world while driving ongoing innovation and business growth. Founded in 2015, CouponBirds provides information about high-quality discounts and coupons to online shoppers worldwide, helping millions of them get more with less each month.

Data flow

Source data is ingested through Amazon Kinesis Data Streams, Amazon Data Firehose, stored or indexed in Amazon OpenSearch Service, processed with AWS Lambda, and delivered through Amazon CloudSearch, Amazon Personalize.

Solution architecture

9 components · 5 layers
  1. Ingest
    • Amazon Kinesis Data Streams Captures and buffers high-volume event streams from source systems.
    • Amazon Data Firehose Delivers streaming data into downstream storage and analytics services.
  2. Compute
    • AWS Lambda Runs event-driven compute for real-time processing without server management.
  3. Storage
    • Amazon OpenSearch Service Stores searchable indexes or vectors and serves low-latency search and analytics queries.
  4. Serving
    • Amazon CloudSearch Powers low-latency search experiences inside the application.
    • Amazon Personalize Generates personalized recommendations for end users.
    • Amazon ELB Balances traffic across application instances during demand spikes.
  5. Governance
    • Amazon VPC Isolates workloads and network segments inside secure private cloud boundaries.
    • AWS IAM Controls authentication and authorization across AWS resources.

Architecture clues

  • After the success in building its data governance architecture, CouponBirds continued to use the serverless computing platform AWS Lambda to process the mission-critical data with a high requirement for real-time process
  • Amazon CloudSearch and Amazon Personalize help to innovatively upgrade the user experience To make its products and services more aligned with the needs of users today for personalized and intelligent offerings, CouponBi
  • By partnering with AWS, the company quickly deployed a global network, to effectively reduce the network latency for its users around the world.
  • The secure global network of AWS helps the customer easily handle the traffic during peak hours CouponBirds knows very well the importance of network stability during peak shopping periods like Black Friday and Cyber Mon

Evidence from the source

4x
AWS helps the customer reinforce core competencies faster with its exceptional data governance solution CouponBirds uses Amazon Kinesis Data Streams to process streaming data from various sources and uses Amazon Data Firehose to seamlessly
In addition, it also uses multiple fully managed products and services of AWS to significantly reduce the cost of data governance by 50%.
Reducing product development cycle to 3 months The entire data governance architecture of AWS is not just easy to scale up and maintain.
Shoppers are looking for coupons to save money, and merchants are using coupons to attract shoppers, increase sales, and improve customer loyalty. However, in the process of expanding and upgrading its business, the company encountered a nu
The company’s product development cycle has been reduced from 8 months to 3 months.
Transparency Evidence & methodology 95/100 reference value · 87% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value95/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    7 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    6 direct source quotes retained.
  • Date provenance8/10
    The publication date comes from publisher-controlled metadata.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. AWS

    CouponBirds customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/couponbirds/
    Source type
    Vendor-published customer story
    Published
    Sep 20, 2024
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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