Every advertising campaign shares the same core objectives: to reach the right audience, create meaningful engagement, and deliver measurable results. Where campaigns begin to differ is in how they pursue those objectives. The divergence starts in the strategy phase, where planners and strategists align on clear goals that, when executed effectively, drive the desired outcomes. At this stage, the business is behind the wheel—drawing on years of experience with its brand, messaging, and past campaign performance to guide decisions. The result is a campaign brief that serves as the North Star for all downstream activities, including audience creation, data science modeling, and activation. This blog explores how the common challenges advertisers face today can be resolved through an AI-powered audience generation solution on Databricks.
It’s during this exchange of campaign briefs between the hands of the business and those of data teams that agencies and advertisers begin to face challenges in aligning the core strategy to execution, Including:
To summarize these challenges, they are each a symptom of a fragmented process where:
One can’t ignore the role that technology has played in the hardening of these sub-optimal processes. Technology lacked the ability to accurately translate intent and data platforms themselves offered few tools to synthesize data insights in a meaningful way for the business. The most effective bridge between these two groups was either a data-curious strategist or a data analyst who could articulate an organization's mission, product-market fit, and strategic goals. This also assumes the strategist has access to the database, and the analyst has the ear of the business (a big assumption).
To address the gap between intent and execution that persists today, our AI-powered solution, built on Databricks Data Intelligence Platform, enables advertisers and agencies to build audiences in natural language, discover previously unknown patterns in their data, and drive more effective campaigns.
At its core, this solution leverages Databricks’ latest advancements in agentic AI – Genie, custom tool-calling agents, and Agent Bricks – to more effectively surface insights to advertisers and enable faster audience segmentation against unified population datasets that span hundreds of millions of consumers and thousands of attributes.
Consider an example to make this tangible, where a campaign planner is preparing a brief for a luxury travel brand promoting exclusive resort packages. The brief calls for "affluent travelers aged 35-54 who frequently book premium experiences." Traditionally, this brief lands on an analyst's desk, who translates it into SQL queries to segment audiences and perform adhoc analyses. While this is a reasonable approach, it inevitably narrows the strategic intent into a handful of familiar attributes.
Instead of needing to manually generate SQL, our solution enables the planner to directly describe that audience directly in natural language. Genie then translates this into a precise query against millions of records in just seconds, complete with the SQL logic behind it for full transparency.
But the real unlock comes next. The system automatically analyzes additional behavior and attributes that define this audience to identify unrealized patterns. These findings and new insights are a gamechanger. Examples for this segment may include:
And the best part is, these are not assumptions. They are statistically validated lift calculations against the full population. Planners are no longer working from just a brief, but rather working from a brief enriched with discoveries that can reshape creative strategy, media placement, and channel selection.
Further, this solution drives tangible and compounding business impact:
However, making this experience feel seamless requires careful orchestration behind the scenes. In the following sections, we will break down the three core building blocks that power this solution, including how each component works, why it's built the way it is, and the design decisions that ensure this system is production-ready.
The first step teams take in any audience workflow is defining the “who” for their campaigns to reach. Genie Spaces allow advertisers to do this in natural language, translating requests like "find affluent travelers aged 35-54 who frequently book premium experiences" into governed SQL queries executed against the population table, without any direct interaction with the data team.
While Genie may reduce the direct interactions between strategy and data teams, the data teams still play a critical role in this workflow by meticulously curating the layer under the hood. A Genie Space is only as good as the context and data it's given, and for audience generation, that means investing in four areas:
By spending time curating the data layer and metadata, the data team’s expertise is encoded once, continuously improved over time, and scaled across the entire organization. Every executive, planner, and strategist benefits from the same curated logic without filing a ticket or waiting for manual, ad-hoc analyses to be performed.
For example, the Genie in this solution is configured with over 30 curated example queries spanning common audience patterns, from "find consumers with auto purchase intent and credit scores above 750" to "identify luxury travelers in urban areas with high net worth." These examples don't just improve accuracy, they teach Genie how the organization thinks about its data.
The result is a system where a planner can describe their target audience for a given campaign brief and receive accurate segment data, audience size, percentage comparisons against the total population, and sample data – all within seconds.
Ultimately, Genie fundamentally changes how marketing teams can build audience segments and execute new campaigns by:
Together, these benefits transform audience creation from a bottleneck into a competitive advantage.
When building a comprehensive audience builder solution, defining the audience is only half the battle. The real strategic value comes from understanding the “what else” about the audience. In other words, are there unthought-of patterns, behaviors, tendencies, and affinities that might strengthen the overall campaign distribution strategy? This is where the Affinity Agent becomes a differentiator.
Once Genie builds a segment, the Affinity Agent automatically takes that segment and analyzes it against the full population to surface statistically significant patterns. In advertising terms, it answers the question every strategist should be asking but rarely has the time or tools to pursue: "Beyond the criteria I specified, what actually makes this audience unique?"
In this solution, the answer comes in the form of lift. This simple yet powerful metric compares how frequently an attribute appears in a given audience segment versus the general population. A lift of 2.0x means the given audience is twice as likely to exhibit that trait. For a campaign planner, these insights are immediately actionable. Let’s take, for example, the luxury travel audience that over-indexes on wellness content and cryptocurrency investment. These signals directly inform execution and instead of running standard travel ads across broad lifestyle channels, the media team might shift spend towards premium wellness publications, podcast sponsorships in the personal finance space, or programmatic placements on fintech platforms. The creative team, meanwhile, might lead with messaging around experiential wellness retreats rather than generic resort imagery.
Under the hood, the Affinity Agent follows a deliberate design pattern, which equips an LLM with tools that execute statistical analysis for a given audience. The agent reasons which analyses to run, but every number is computed by deterministic tools. These tools are functions which are pre-registered and governed in Unity Catalog.
In practice, a given audience serves as the input data, and the agent leverages Unity Catalog functions to compute lift against the baseline population, and returns only results that meet minimum confidence and support thresholds. The following pseudo code snippet describes how this function operates.
This separation between reasoning and computation is a deliberate and critical choice for advertising. When millions of dollars in media spend depend on audience insights, those insights need to be auditable, reproducible, and grounded in real data. By governing every analytical function through Unity Catalog, the system provides a clear lineage from natural language questions to SQL execution to statistical results. The LLM adds intelligence to the workflow without introducing risk.
With Genie handling audience creation and the Affinity Agent surfacing hidden patterns, the final piece is assembling an intelligent orchestration layer to ensure these components work together as a single, coherent experience rather than disconnected tools.
Agent Bricks makes this possible in just minutes with a few clicks. The pre-built Supervisor Agent receives every user request, determines which sub-agent is best equipped to handle it, and routes it accordingly. A request like "build me an audience of frequent luxury travelers" goes to Genie. A follow-up like "what else defines this group?" is routed to the Affinity Agent. And when a user asks a complex question like "find high-income outdoor enthusiasts and tell me what makes them unique," the supervisor chains both agents together – Genie builds the segment, then the Affinity Agent analyzes it – and returns a unified response.
For end users of this multi-agent system, the power lies in the fact that they never need to know which agent is doing the work, because the supervisor agent works together with its sub-agents to handle the requests. As a result, the experience is seamless and feels like a single conversation.
Beyond routing, the supervisor is what transforms individual agents into a compounding system. By orchestrating the handoff between Genie and the Affinity Agent, it creates a feedback loop between human intent and data discovery that doesn't exist in traditional workflows. A planner can build an audience, review the affinities that surface, and immediately refine the segment based on what they've learned, all within the same conversation. This collapses what is typically a days-to-weeks cycle of briefs, analyst queues, QA, and iteration into minutes, enabling teams to test more hypotheses and arrive at stronger audiences faster. Over time, every interaction builds a growing library of audience definitions and discovered patterns, which leads to institutional knowledge that compounds with each campaign and scales to every user in the organization.
So far, we've explored how Genie translates strategic intent into governed audience segments, how the Affinity agent uncovers patterns that no one thought to look for, and how the Supervisor agent orchestrates the two sub-agents into a seamless workflow. But these capabilities only deliver value if the people who need them most can actually access them without navigating Databricks workspaces, notebooks, or API endpoints. Databricks Apps remove this barrier, providing a native application layer that brings the entire multi-agent system into a single, intuitive interface purpose-built for the end user.
Within this app, advertisers can:
Build, discover, and iterate on audience segments through natural language.
Explore details about audiences, including underlying SQL code used to generate, affinity summaries, and recommendations for placement and campaign strategies.
Dive deeper into audience segments with integrated AI/BI Dashboards embedded directly within the app.
Save audiences as persisted tables in Unity Catalog for future viewing, management, and auditability, then, export audiences to execution channels for campaign activation.
The gap between strategic intent and data execution has persisted for years – not because organizations lack data, but because their tools weren't built to bridge it. With Databricks' agentic AI capabilities, that bridge now exists, empowering all personas with the tools to discover and build high-quality audiences that differentiate. That audience you didn't know you were looking for is already in your data, you just needed the right system to find and curate it.
To learn more about best practices for building an effective Genie Space, check out this guide.
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