November 2023 Summaries

Snowplow has become an official member of the AWS Advertising and Marketing Technology Competency, enhancing its ability to optimize first-party customer data strategies using AWS cloud infrastructure. This designation, part of the AWS Competency Partner Program, highlights Snowplow’s technical proficiency and proven customer success, placing it alongside platforms like Braze and ActionIQ in helping companies innovate and integrate advertising and marketing technology. By collaborating with AWS, Snowplow enables the creation of scalable data foundations for Customer 360 use cases, including customer acquisition and personalization, and supports the use of AWS technologies like Privacy-Enhanced Data Collaboration. The partnership, built over a decade and marked by Snowplow's pioneering use of Redshift, allows for the migration of applications to AWS, improving key business metrics such as return on ad spend and customer conversion, and empowering marketers to deliver next-generation customer experiences.

A data contract functions as a formal agreement between data producers and consumers, akin to business contracts in supply chains, ensuring data quality, functionality, and accountability. Over the past year, data contracts have gained widespread acceptance and adoption, despite initial controversy, and are being integrated into products like Starburst and dbt, with notable contributions from figures like Andrew Jones and Chad Sanderson. Critical components of data contracts include clear ownership of data stages, schema definitions, semantics for understanding data meaning, delivery and access SLAs, data quality SLAs, and policy guidelines for data use, retention, and anonymization. These contracts help overcome the challenges of assigning responsibility within data teams and maintaining consistent data standards. As the concept evolves, there is a push toward developing open standards for data contracts, with initiatives like Datacontract.com and the Open Data Contract Standard underway, which Snowplow plans to support.

In a landscape fraught with privacy challenges and the decline of third-party data, Snowplow Analytics is pioneering a solution by enabling brands and publishers to focus on first-party data collection, which respects consumer privacy while still allowing for personalized experiences. The article discusses the contrasting views of two metaphorical tribes: the Madtech Millenarians, who fear the end of data-driven advertising due to privacy measures, and the Head-in-Sanders, who deny the impact of these changes. It highlights Apple's Intelligent Tracking Prevention as a significant move in browser privacy, affecting third-party data but sparing first-party data, which Snowplow effectively facilitates through its technology. Snowplow's approach, including its newly launched Snowplow ID Service, ensures that brands can collect rich first-party data without breaching privacy regulations or consumer trust, thus aligning with Apple's privacy goals while meeting the industry's needs for consumer insights. The text underscores the necessity for the industry to balance privacy and personalization through first-party data, a strategy that aligns with consumer expectations for both privacy and tailored digital experiences.

Snowplow's new Video and Media Analytics Accelerator, alongside the updated media-player package in dbt, enhances the understanding of user engagement with video content by offering advanced metrics and visualizations. These tools provide businesses with insights into how users interact with media, including ad views and playback metrics, to optimize content and measure marketing impact. The accelerator introduces new modeled tables for detailed ad reporting and offers three interactive dashboards to visualize engagement and advertising performance. Additionally, the latest media model updates bring improved playback and buffering metrics, supporting both web and mobile events, and are available under Snowplow's new licensing for open-source users. This comprehensive suite of features aims to improve media strategies and enhance user engagement through data-driven insights.

Snowplow has introduced a new Data Modeling pack that offers open-source users access to both existing and new data models, including the Unified Data Model, Media Model, and Video and Media Analytics Accelerator. The Unified Digital Model facilitates seamless cross-platform identity stitching, providing a unified view of customer journeys across web and mobile without the need for complex custom models. This model, along with updates to the media model, is released under a new Personal and Academic License (SPAL), which allows non-commercial use and is part of Snowplow's strategy to help teams extract business value from their data. The Video and Media Analytics Accelerator enables detailed tracking of customer interactions with videos and ads, enhancing user engagement analysis. Snowplow's shift towards this licensing model reflects its commitment to transparency and educational use while offering commercial licensing options for broader applications. The initial models under SPAL include Media, Ecommerce, and Unified Digital models, with more updates planned for future releases.

Disjointed customer journeys present challenges for data and marketing teams, necessitating accurate identification and stitching of user journeys across web and mobile platforms to optimize marketing spend and customer segmentation. Historically, data teams faced difficulties in recognizing logged-in users due to the need to build custom data models from scratch, incurring high processing costs and dealing with complex data logic. To address these issues, a new Unified Digital Model has been introduced, offering cross-platform tracking and deterministic identity resolution to unify user journeys without the need for intricate modeling. This model consolidates raw web and mobile events into derived tables, enabling seamless stitching of events from logged-in users across platforms and supporting future capabilities for anonymous session stitching. It supersedes previous models by integrating features such as custom identifiers and passthrough fields, facilitating improved efficiency and reduced development time. Existing web and mobile models will receive continued support for bug fixes, while active development shifts to enhance the Unified Digital Model. The model is accessible to Snowplow BDP customers and open-source users under a new licensing agreement, with support available for migration and implementation.

Identity stitching is a crucial technique for creating a comprehensive single customer view by linking individual behavioral events to unique users across various sessions, devices, and platforms, using Snowplow's data capabilities. This process involves collecting multiple identifiers per event, constructing a user mapping table to associate anonymous and authenticated IDs, and enriching datasets to resolve user identities, even pre-login. Snowplow's transparency and flexibility facilitate precise identity stitching, which is vital for accurately tracking customer journeys, measuring attribution, understanding conversion paths, and enhancing personalization and LTV modeling. The approach allows for expansion across platforms, such as mobile and web, and can incorporate third-party marketing identifiers like GCLID. Although shared-device usage may introduce challenges, strategies like probabilistic models and logging uncertainty can mitigate misattribution. Advanced tools such as dbt, Kafka, and Spark can further enhance identity stitching processes, tailored to specific business needs and tech stacks. Snowplow encourages consistent identifier collection and iterative complexity management to ensure high data quality and effective edge case handling.