Yew Tree Data Consulting was engaged by a private equity-backed adtech company specializing in cookieless targeting across premium publisher inventory. With the imminent deprecation of third-party cookies providing an opportunity to capture market share, the client sought an AI strategy to leverage machine learning (ML), natural language processing (NLP), and generative AI to enhance their business operations, strengthen their market position, and maintain a competitive edge.

Project Scope

The project was structured into three distinct phases:

1. Competitive Landscape Evaluation

2. Use Case Ideation Process

3. Readiness Assessment

Phase 1: Competitive Landscape Evaluation

The first phase involved a comprehensive analysis of the competitive landscape. Yew Tree Data Consulting focused on identifying how key competitors in the adtech space were utilizing AI to drive business value. This analysis included:

• Benchmarking AI Capabilities: Identifying and benchmarking AI capabilities and innovations among competitors.

• Use Case Documentation: Documenting specific AI use cases deployed by competitors, such as semantic contextual targeting, campaign optimisation and creative automation.

• Value Proposition Analysis: Analysing the value propositions of these AI applications, particularly in improving targeting accuracy, enhancing user engagement, and optimizing ad spend.

The deliverable for this phase was a detailed report highlighting the strategic use of AI by competitors, providing a solid foundation for the client's own AI strategy.

Phase 2: Use Case Ideation Process

In the second phase, Yew Tree Data Consulting facilitated a series of workshops and brainstorming sessions with the client’s stakeholders to ideate potential AI use cases. This collaborative process focused on identifying high-impact opportunities where AI could be integrated into the client’s operations. Key activities included:

• Identifying Strategic Goals: Identifying the client’s strategic business goals and thinking about how these could be achieved with AI.

• Generating Ideas: Generating a broad range of AI use cases, from predictive modeling for user behavior to NLP for content categorization and generative AI for dynamic ad creation.

• Prioritizing Use Cases: Scoring and prioritizing these use cases based on two key criteria: expected business impact and ease of implementation.

The output of this phase was a strategic roadmap of prioritized AI use cases, designed to align with the client’s business objectives and resource capabilities.

Phase 3: Readiness Assessment

The final phase involved a readiness assessment to evaluate the client’s current capabilities and the investments needed to implement the recommended AI use cases. This assessment covered both organizational and technical dimensions, including:

• Organizational Readiness: Evaluating the client’s existing team structure, skills, and culture to identify gaps and training needs.

• Technical Readiness: Assessing the client’s data infrastructure, technology stack, and integration capabilities to support AI initiatives.

• Investment Requirements: Identifying the types and levels of investments required in technology, talent, and processes to successfully deploy AI solutions.

The deliverable was a detailed readiness assessment report, outlining specific steps and investments needed to achieve the desired AI capabilities.

Outcomes and Impact

The comprehensive AI strategy developed by Yew Tree Data Consulting was enthusiastically received by both the client and their private equity backer. The strategic insights and recommendations provided by Yew Tree Data Consulting were immediately incorporated into the client’s product strategy and roadmap planning. Key outcomes included:

• Enhanced Competitive Positioning: The client gained a clear understanding of how to leverage AI to differentiate themselves in the market.

• Actionable Roadmap: A practical and prioritized roadmap of AI use cases ready for implementation.

• Investment Clarity: A clear view of the investments needed to build AI capabilities, allowing for informed decision-making and resource allocation.

This project not only helped the client navigate the opportunities posed by the deprecation of third-party cookies but also positioned them for long-term success through strategic AI adoption.