HCLTech developed a customer data platform for a leading UK-based digital media organization and their subsidiaries on Snowflake. This platform aimed to optimize revenue per user by integrating user interactions across web, marketing and advertising systems. It enabled effective determination of end-user behavior, better retention and churn prediction. Snowflake facilitated faster time to market and real time data consumption, consolidating trusted data sources for recommendation engines. This led to personalized user journeys, faster decision-making and improved targeting of content. Our client’s profit margins improved due to a reduction in customer acquisition cost (CAC) and decreased dependency on third-party services and reporting tools.
The Challenge
Overcoming legacy challenges, faster decision-making and reducing CAC and targeted content
The challenges included unconsolidated and non-trusted data sources, a complex platform architecture operating in silos and the inability of the existing platform to centrally support various targeting methods and personalize user journeys. Our client relied on specialist data analysts and data scientists to handle complex analytical tasks, particularly behavioral analysis. The other challenges included were:
- Lower accuracy in measuring ad yield per user, managing unsold inventory, poor targeting of audiences and the absence of personalised messaging
- The existing infrastructure lacked integration and data migration capabilities resulting in inefficiency
- Fragmented automation elements led to a disjointed data ecosystem
The Objective
Analytics platform development for data-driven growth
Our client aimed to develop a powerful, adaptable platform to drive analytics products, services and insights. They sought to boost subscription revenues, enhance end-user retention through targeted advertising, expand audience and engagement levels, and tap into both existing and new markets. We collaborated closely with them and Snowflake to implement strategies aligned with their platform roadmap to achieve the below objectives:
- The goal was to democratize data by offering a core data service, enabling business units to retain control over their line of business data and systems
- Enable data usage and sharing across businesses according to established governance standards
- Assist businesses in maintaining their own analytical capabilities and technical teams
- Operate the robust, flexible and efficient platform without relying on an internal workforce
- Enable the utilization of best-of-breed technologies to scale for future growth
- Achieve faster time to market for both platform construction and data onboarding processes
The Solution
Improving operational efficiency and agility with Snowflake’s Data Cloud
We used Snowflake to build a customer data platform, establishing a single source of truth across our client’s diverse lines of business. Our client selected Snowflake as their data platform due to its ability to aggregate data from various data sources such as PIANO, Facebook Meta, Salesforce, YouTube, Google Ad Manager (GAM), Google API (GAM API) as well as news content and articles from our client's website. Snowflake's clean design architecture facilitated revenue optimization through the customer lifecycle by integrating modifications made through web, marketing and advertising systems using strategies like targeting and user journey personalization.
The Impact
Driving data excellence: HCLTech’s Snowflake Success Story
The platform acted as a unified source of truth, aggregating segmental data (e.g. location, demographics, age groups, genre affinity) for analytical purposes. Internal teams could leverage trusted data source (News Teams, Data Science Teams) to develop use cases (like a recommendation engine) without managing the operational aspects of the data platform, as Snowflake dynamically handled data management (storage, file size, structure, compression, statistics, etc.) tasks at the backend with minimal dependency on the user. Snowflake also supported content metadata creation, which was a value-add. The platform facilitated effective acquisitions, improved activation targeted strategies, enhanced retention via churn prediction, increased CPM rates (cost per mille - 1000 impressions) through richer data and improved targeting capabilities.
Significant cost savings quantified are:
- 35% improvement in recommending better content
- 50% faster improvement in delivering outcomes (i.e. the data products)
- 10 times faster feedback to the marketing team compared to manual data consolidation
- Ease of building and managing data products with enhanced security and data-sharing features
