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Samba Knowledge Graph: The Catalyst for Next-Gen Media Intelligence
Navigating the Chaos of Modern Media
Today’s media landscape is rapidly expanding. Fueled by the creator economy and generative AI, this acceleration of content production is setting the stage for a highly competitive battle for consumer attention, spread across countless linear networks, streaming apps, movies, web content, social media and everything on the Web.
Samba has progressively built its understanding of this universe - starting with linear TV (2012), adding streaming (2018), and integrating web intelligence via Semasio (2024). This journey provides us and our partners deep contextual understanding of billions of media properties and web domains.
Contextual understanding is powerful, but effectively navigating the vast content and advertising landscape requires a programmatic framework that makes sense of these disparate behavioral signals and connects them across the consumer journey, mapping the relationship between people, devices and households. Integrating this within the decision making systems employed in your business is like having a crystal ball that allows you to understand your customer like never before.
Our unique combination of TV and web signals unifies this cross-platform customer journey, providing the catalyst for next gen media intelligence. We call this platform the Samba Knowledge Graph (SKG). It enriches your customer data and predicts needs before they are satisfied by your competitors, giving your business dramatic advantage and new levels of personalization to transform the customer experience. We now operate this graph at a global scale with over a billion people who have opted to be included. By modeling relationships between context, attitudes, interests across households, individuals, and platforms, the SKG transforms raw data into a structured, queryable network of intelligence.
Ontologies and Knowledge Graphs: Stepping Stones From Data to Knowledge
Understanding data structures, like ontology and knowledge graph, is key to unlocking advanced analytics and solving big data challenges. The relationship can be captured in a simple but powerful formula: Data + Ontology = Knowledge Graph.
1. Ontology: The Blueprint
An ontology is a formal system that acts as the schema or blueprint for a domain, defining entities and their relationships (e.g., "A TV Network owns a TV Show"). This shared structure ensures consistent data integration and interpretation across varied sources.
2. Knowledge Graph: The Real-World Construction
The knowledge graph is a database built from the ontology's blueprint. It organizes actual data points (nodes/entities) and their relationships (edges) into a network, strictly adhering to the ontology's rules. This organization preserves both data and crucial context.
3. Applying Contextual Reasoning and Inference
With an ontology and a contextual reasoner, the knowledge graph yields profound new insights. This structured data is vital for AI-ready applications, minimizing LLM "hallucinations," enhancing Retrieval-Augmented Generation (RAG), supporting Explainable AI (XAI), and enabling next-generation agentic workflows that make us super human and hyper scalable.
It’s Not Sci-Fi: We Use Knowledge Graphs Every Day
Knowledge graphs may sound theoretical, but they already power many of the technologies we use daily. They have become indispensable for organizing and extracting value from big data.
Google Search: When you search for the "Eiffel Tower" and see an info panel with facts like its height and location, you're interacting with Google's Knowledge Graph. Around 2012, Google shifted its search methodology "from strings to things," allowing it to understand the real-world entities behind your query and dynamically populate the info panel when you search.
LinkedIn’s Economic Graph: With a network that exceeds 1 billion nodes and 250 billion relationships, LinkedIn's graph connects people, jobs, companies, and skills. This network powers features like “People You May Know” and job recommendations by analyzing the billions of connections between professionals and their careers.
Amazon Product Recommendation: Amazon utilizes a Product Knowledge Graph as a core component of its COSMO framework to enhance product recommendations. This graph encodes product relationships, function, audience, and typical use location, enabling rich contextual inferences for the recommendation engine. COSMO uses LLMs to extract these common sense relationships from Amazon Store customer interaction data, facilitating large-scale knowledge generation and serving.
Netflix Content Recommendations: Netflix uses its knowledge graph to connect content and products and create graph embeddings that it uses in its recommendation engine. By linking movies by actors, genres, and themes, Netflix can surface hyper-personalized recommendations for what you might like to watch next.
Samba applies this same proven intelligence approach to media, connecting TV, web, and real-world behavior into a unified knowledge graph built to power cross-screen marketing, measurement, and activation.
Building the Media Universe: Inside the Samba Knowledge Graph
Samba is shifting from traditional, panel-based TV measurement (like Nielsen) to the Samba AI era of connected insight. Moving beyond data volume to dimensionality, presents a new opportunity to capture the depth and interconnectedness of data. Our advantage is fusing two massive, deterministic data sets onto a single identity spine: over 400 billion proprietary TV content and ad exposure data (ACR), and over 200 billion monthly web interactions (from Semasio). This integration is anchored by 1.489 billion connected IDs globally, creating a holistic consumer view that forms the graph's backbone.
"My mission at Samba was clear - create the foundational layer of data and semantic understanding across the multi-dimensional universe of viewership and digital behaviors data, as we move beyond simple data volume and shift the focus to dimensionality."
- Satyajeet Raje
The Power of Dimensionality: From "What They Watched" to "What They'll Do Next"
The true power of the knowledge graph emerges when we layer different dimensions of data to build a holistic, multi-dimensional view of the consumer.
First, the Foundation: Fusing TV and Web Behaviors. This is our unique strength. The graph deterministically links TV viewership patterns with web behaviors. For instance, we can identify a household of "Drama Lovers" based on their TV habits and see that they are also "In-Market Auto Intenders" based on their browsing activity. This creates a more colorful and complete picture of digital behaviors.
Next, Adding Context: Socio-Demographics. We then layer in crucial context like geography and household composition. This adds crucial depth, allowing us to move beyond observing behaviors to understanding the patterns behind them, whether at the household or individual level.
Then, Building Synthetic Behaviors. This is a game-changer for media intelligence. Using the graph's ontological structure, we can identify "digital twins" - households with similar multi-dimensional signals - to predict behaviors with high confidence for households that may be missing certain data points. This allows us to create a synthesized picture of behaviors at census scales without relying on statistical projections alone.
Finally, the Leap: Generating Predictive Intelligence. This is the most powerful step. By analyzing the rich, layered signals, we can move from descriptive insights ("what happened") to predictive intelligence ("what happens next"). The graph allows us to forecast major life events, such as identifying a "Baby in the House!" based on browsing for strollers, or predicting purchase intent, like a household preparing to "Buy a house!". This capability is the key to unlocking true personalization and delivering superior marketing outcomes.
Our Future is Open Data Collaboration, Not a Walled Garden
The Samba Knowledge Graph is the essential bedrock for the next generation of media that incorporates real-world behaviors and first-party data, to inform the inference layer and GenAI within agentic workflows.
Our philosophy has always been to be the "anti-walled garden." We are committed to democratizing media intelligence, not hoarding it. In fact, our open approach is complementary to the walled gardens we partner with, enabling a layer of media intelligence that sees beyond any single platform’s boundaries. We believe the future of data is collaborative and are building our architecture to foster federated development through privacy-safe environments like data clean rooms. By collaborating with partners like Anthropic, Databricks, Snowflake, Infosum, Amazon, and others, we allow our partners to securely combine their first-party data with our deterministic insights, creating an even richer understanding of the consumer.
What can the Samba Knowledge Graph do for you? Let us enrich your customer intelligence with media intelligence today.
Satyajeet Raje
VP of Global Data Science, Samba
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