Building advanced LLM use cases like autonomous agents or detailed analytics demands deep business understanding that even the most advanced B2B companies and technology providers struggle to achieve within complex functions like marketing or sales. Achieving this elusive goal requires a partnership between technology resources and operations teams to develop new ways of documenting business knowledge. In this report, we explain why companies pursue knowledge graphs to power advanced AI use cases within go-to-market (GTM) functions, why this is such a challenge today, and how operations teams can provide the key business context required.

Declining organic website traffic, driven largely by AI-powered search, has prompted companies to rely more heavily on intent data providers to replace those lost signals. Though bidstream providers and data co-ops have shown resilience in maintaining predictive signals, walled garden providers face the greatest risk and are responding with aggressive strategies to adapt to evolving buyer behaviors. Drawing on insights from a cross-section of providers from the most recent Forrester Wave™ evaluation on intent, provider reference customers, and Forrester clients, this report outlines the current impact of AI-powered search and the mitigation approaches emerging in the B2B intent data market.

The adoption of B2B intent data has grown significantly since 2023 as organizations recognize its potential to improve account targeting and go-to-market outcomes. But despite widespread investment and reported benefits, many companies struggle to maximize its value due to challenges like immature usage, lack of measurement, and difficulty integrating signals into internal systems. This report explores the latest trends in intent data usage, including the gaps between user expectations and measurable results.

Customer success managers (CSMs) are like pilots. They know how to fly the plane and keep customers moving toward their destination. But not even the most capable pilots could manage every variable alone. Without an air traffic control tower, flights paths would get tangled, delays would pile up, and small issues would turn into emergencies. […]

In our evaluation of B2B marketing and sales data providers, we identified the most significant ones and researched, analyzed, and scored them. This report shows how each provider measures up and helps you select the right one for your needs.

In the implement phase of the Forrester B2B Distributed Data Unification Process, the project team establishes new systems and processes for data management, related training, and ongoing monitoring of performance to drive future improvement. In this report, we discuss how operations and technology teams shift from planning to coordinated action via five stages: execute tech changes; deploy data changes; update processes; perform training; and measure, iterate, govern.

In the plan phase of the Forrester B2B Distributed Data Unification Process, the project team proposes, prioritizes, and selects from potential unification paths, documenting trade-offs such as cost, scalability, or risk. Key activities include developing aligned data models, updating processes, and defining technology requirements. The team also evaluates business impact and customer value in this phase, blending technology, process, and people changes into a defined rollout plan. In this report, we describe how the plan phase drives collaboration between operations and IT teams to define a realistic future state for data unification, balancing aspirations with real-world business constraints.

The Forrester B2B Distributed Data Unification Process is a solution for addressing fragmented and siloed data across marketing and sales functions. To successfully unify data, operations teams must proactively lead data unification efforts, collaborating with IT and stakeholders to document processes, assess data quality, and identify inefficiencies. In this report, we focus on best practices in the discover phase of the process: engaging stakeholders, defining objectives and requirements, auditing the current state, and analyzing gaps.

B2B organizations face challenges in meeting buyer expectations for personalized and consistent experiences due to fragmented and incomplete customer data. Traditional approaches to data unification often fall short because of limitations in scalability, usability, and integration across diverse data types and systems. Distributed data unification focuses on better data practices like capturing cleaner data upfront, expanding monitoring capabilities, and empowering users with data literacy. In this report, we introduce the Forrester B2B Distributed Data Unification Process, a phased strategy for distributed data unification to help operations and technical teams tackle persistent data challenges.

For B2B measurement and analytics teams, delivering meaningful, actionable insights as business conditions change requires agility, efficiency, and a clear focus on business priorities. Use this tool and the conversations it will spark to assess your organization’s strengths, challenges, and potential blind spots across each of the four key analytics process areas outlined in the Forrester B2B Analytics Ecosystem, as well as the six underlying capabilities that support those processes. Your detailed results will be mapped to the Forrester B2B Analytics Maturity Model and include a customized set of recommended actions to advance your team’s proficiency.