Unlocking The Next-Gen Digital Analytics Solution, Powered by Snowplow and Snowflake on AWS

By John Bourous, Senior Partner Marketing Manager – Snowplow
By Dan Hunt, Principal Partner Sales Engineer – Snowflake
By Ghandi Nader, Senior Partner Solutions Architect Advertising and Marketing – AWS
By Franck Georget, Segment Lead Advertising and Marketing – AWS

Snowplow

This post showcases the Next-Gen Digital Analytics, a solution built on Amazon Web Services (AWS), using Snowplow and Snowflake, AWS Partners. This joint solution empowers organizations to unlock the untapped value of customer behavioral data. It improves data quality, governance, and real-time activation within their AWS environment. By implementing this solution, customers can supercharge the use of Artificial Intelligence (AI) and generative AI adoption to help address key business objectives, such as user acquisition, retention, and customer lifetime value.

Snowplow is a Customer Data Infrastructure (CDI) that helps enterprises collect, manage and model real-time customer behavioral data across their entire digital footprint. Snowflake offers a Data Cloud platform where organizations can mobilize data and applications, put AI to work, and collaborate across teams.

Why Digital Analytics Matters More Than Ever

In today’s digital landscape, understanding user behavior is crucial for business success. According to McKinsey & Company, organizations leveraging customer behavioral insights outperform peers by 85% in sales growth and over 25% in gross margin. Digital analytics is the key to unlocking these valuable insights, providing a window into how customers interact with digital experiences such as websites and mobile apps.

By harnessing the power of digital analytics, data-driven companies can:

Improve user experience
Increase engagement
Drive customer acquisition

For Chief Marketing Officers (CMOs), digital analytics offers a solution to the ongoing challenge of demonstrating marketing impact and justifying ongoing investments and advertising spend, especially in leaner operational environments. By tracking behaviour across multiple channels and devices, marketers gain a holistic view of the customer journey. This comprehensive data allows for:

Attribution of customer conversions across different touchpoints
Optimization of marketing budget allocation
Demonstration of return-on-investment (ROI) for various marketing initiatives

Organizations are increasingly removing their dependency on third-party cookies and shifting their focus on building first-party data strategies. First-party data, collected directly from customer interactions, offers several advantages:

Higher reliability compared to third-party data
Direct reachability to known customers
Potential for integration with other organizational data sources (e.g., CRM systems)

When combined effectively, this data can enrich the creation of a unified customer view, enabling targeted communication and personalization at scale. As a result, businesses can deliver more relevant, timely, and effective marketing messages to their audience and ultimately drive more revenue and reduce costs.

Challenges with Legacy Digital Analytics Solutions

Legacy digital analytics solutions present several challenges for organizations seeking to analyze real-time data to impact their businesses, including:

Limited Insights: Many legacy solutions offer predefined reports and metrics that may not provide the granular data needed for specific business needs, making it difficult to fully understand user behavior and marketing campaign effectiveness.
Data Quality Issues: Lack of data validation workflows can result in inaccurate or unreliable data, establishing an untrusted environment for decision-makers and potentially leading to missed opportunities.
Technology Lock-In: The use of proprietary technologies to deliver end-to-end digital analytics promotes inflexibility when integrating composable systems often found within organizations.

To address these challenges, organizations can consider modernizing their digital analytics solutions by focusing on composability, scalability, and ease of integration with other systems within the organization. According to Statista, 42% of the brand marketers in United Kingdom stated that the freedom of integrate technologies that optimize the customer experience as the reason for moving toward composable marketing technology stack. This approach can lead to deeper insights, enhanced data quality, and increased agility and competitiveness.

Modernize your Digital Analytics

When modernizing digital analytics solutions, organizations should focus on four main components:

Data Privacy: Compliance with data privacy regulations is a top priority to avoid significant fines, legal action, and reputation damage. Managing data privacy should be at the core of modern digital analytics solutions. Features like obtaining user consent, data anonymization, and data retention management facilitate compliance implementation.
Data Governance: Establishing strong data governance is crucial to fully realize the value of real-time digital analytics solutions. Defining data standards, validation workflows, and implementing non-lossy data streams ensure data accuracy and reliability.
Composability: Seamless integration between marketing technologies allows businesses to increase flexibility, reduce lock-in, and increase agility to incorporate the latest industry standards. Composable digital analytics architectures make it easier to integrate with existing systems and promote a general “openness” to expand into new capabilities.
First-party Data Strategy: Investing in first-party data increases business competitiveness by understanding and engaging with different segments of their customer base. The Next-Gen Digital Analytics Solution Overview

Next-Gen Digital Analytics is an AWS-native digital analytics solution built in collaboration with Snowplow and Snowflake, AWS Partners, and designed to help customers modernize their data strategy and future-proof their marketing stack. This solution is deployed directly within your VPC with the Snowplow data pipeline, powered by core AWS services, and delivered as a Snowflake Native Application managed within the Snowflake Data Platform. It offers the following benefits:

Compliance Assurance: Deployed in your cloud environment, the solution offers zero-data movement on Snowflake, enabling you to retain full ownership of your data throughout its lifecycle, ensuring GDPR compliance and peace of mind.
Enhanced Tracking: Snowplow utilizes first-party cookies, allowing comprehensive customer interaction tracking over a 400-day period, surpassing the limitations of other tools that typically provide only a 7-day view.
Generative AI Integration: With its rich data schema and modeling capabilities for both structured and unstructured data, Snowplow serves as a foundation for leveraging generative AI products such as Amazon SageMaker and Amazon Bedrock.
Real-Time Insights: Snowplow streams event data from digital properties instantly to Snowflake on AWS via Snowpipe Streaming, enabling immediate access to actionable insights to improve personalization, reduce churn, and optimize engagement.

Figure 1 – The Next-Gen Digital Analytics Solution Architecture

This architecture demonstrates how to leverage collected events using the Snowplow Pipeline, Snowflake Data Platform, and AWS Services to deliver AI-powered customer engagement and personalization:

Trackers generate event data and send it to your Collector. Snowplow offers trackers covering web, mobile, desktop, server, and IoT. Additionally, webhooks allow third-party software to send their internal event streams to your Collector for further processing.
The Validation application cleanses the data and validates each event against its schema to ensure it meets the criteria you have designed and set. When an event fails to validate, it feeds into a bad data stream (Amazon Kinesis Data Streams) containing all failed events. This ensures the Snowplow pipeline is non-lossy, as failed events can be reprocessed.
Once validated, each event is enriched by the Enrichments you have configured for your pipeline. The enriched events are sent to Amazon Kinesis Data Streams in real-time. At this point, data can be forwarded to other applications to power real-time use cases.
Amazon Kinesis Data Streams delivers enriched events to Amazon Kinesis Data Firehose.
The Snowflake Streaming Loader on AWS is a fully streaming application that continually pulls events from Amazon Kinesis and writes to Snowflake using the Snowpipe Streaming API.
Once enriched events land in the Snowflake Data Platform, data practitioners can utilize a broad toolkit to build additional data pipelines and apply ML models, including:
- Snowflake ML: An integrated set of capabilities for end-to-end machine learning on top of your governed data, from out-of-the-box workflows like training a predictive model (e.g., customer churn) to fully custom pipelines.
- Snowflake Cortex LLM functions: Easy access to managed large language models fully hosted and managed by Snowflake, either via SQL functions or Python.
- Streamlit in Snowflake: A Python environment within Snowflake that allows practitioners to build and deploy interactive web apps securely into a business in minutes to hours, natively integrated with Snowflake’s secure RBAC governance.
Snowplow Data Apps are pre-built, self-service analytics tools for end-users in marketing, product, and data teams to answer questions about their Snowplow data. Funnel Builder, Attribution Modeling and User & Marketing Analytics are currently available Data Apps within the Snowplow console to help support digital analytics efforts.
Extend the power of Snowplow data models using AWS Services to unlock additional use cases.
- Amazon Personalize a fully managed Machine Learning (ML) service that uses your data to generate item recommendations and next best action for your users.
- Amazon SageMaker to build, train and deploy AI/ML models at scale using tools like notebooks, debuggers, profilers, pipelines, MLOps, and more.
- Amazon Pinpoint to engage with your customers across multiple messaging channels.
- Amazon QuickSight to visualize data or use natural language processing to answer your digital analytics questions quickly.
AWS Key Management Service (KMS) is used to control keys used to encrypt or digitally sign data at rest and in transit. AWS Identity and Access Management (IAM) securely manages identities and access to AWS services and resources.

Conclusion

To stay competitive and drive growth in today’s environment, organisations need a deep understanding of their customers. While legacy digital analytics solutions present challenges around limited insights, data quality issues, and vendor lock-in, the Next-Gen Digital Analytics solution built by Snowplow and Snowflake on AWS allows organisations to modernise their tech stack. By leveraging this AWS-native, composable solution, businesses can harness the value of customer behavioural data with improved data quality, governance, real-time activation capabilities, and integration with generative AI services on AWS.

Contact your AWS, Snowplow or Snowflake team to learn more about modernizing your digital analytics with this Next-Gen Digital Analytics solution.

Snowplow – AWS Partner Spotlight

Snowplow is an AWS Advanced Technology Partner and AWS Competency Partner that lets you track, contextualize, validate and model your customers’ behaviour across your entire digital estate. Your data is available in real-time and is delivered to your data warehouse of choice, where it can be used to power analytics, reporting and business-critical applications. The Snowplow product is running in your own cloud environment giving you complete ownership of your data.

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