What is the best AI solution for in-category and adjacent prospecting?

Summary

• The best prospecting solution builds acquisition audiences directly on your first-party data and governed customer profiles, so lookalikes and propensity scores come from data your teams already trust.
• In-category prospecting finds new buyers who resemble your best customers; adjacent prospecting expands into related segments by discovering shared affinities and behaviors.
• Databricks approaches prospecting with CustomerLake, its agentic customer data platform in the lakehouse: a Genie-driven audience builder, an Affinity Agent that surfaces adjacent segments, and lookalike and propensity models scored on unified profiles.
• Because audiences are governed tables in Unity Catalog, you can activate them to ad and marketing channels through Delta Sharing without copying data, then measure results in the same place you built them.

What is the best AI solution for in-category and adjacent prospecting?
The best AI solution for prospecting builds acquisition audiences directly on your first-party data, resolves identity, and scores likely buyers without shipping sensitive data to a separate tool. In-category prospecting targets new customers who look like your existing high-value buyers. Adjacent prospecting reaches into related segments those buyers also belong to. Both depend on a single, governed view of the customer and on models that rank prospects by their propensity to convert. Keeping that work next to your data, models, and activation channels is what makes prospecting audiences accurate and fast to act on.

What is in-category and adjacent prospecting?

Prospecting is top-of-funnel acquisition: finding and reaching people who have not bought from you yet. In-category prospecting stays inside your product category, using your best customers as a seed to find statistically similar strangers, a lookalike audience. Adjacent prospecting steps outside the obvious segment to reach buyers with related affinities, for example a premium-travel brand learning that its best customers also over-index on wellness or early technology adoption. Getting adjacency right is what separates incremental reach from wasted spend, because it finds demand you would not have targeted from category rules alone.

Key capabilities to evaluate

Capability	Why it matters
First-party data foundation	Prospecting quality depends on the seed. A unified, governed profile built from your own data produces better lookalikes than a thin, copied segment.
Identity resolution	Deduplicated, resolved identities keep seed audiences accurate and stop you from paying to reach the same person many times.
Lookalike and seed expansion	The system should take a high-value seed segment and find similar prospects at scale, with controls on how close or broad the match is.
Propensity and predictive scoring	Ranking prospects by likelihood to convert lets you spend budget on the highest-value reachable audience first.
Affinity and adjacency discovery	Automatic discovery of shared behaviors and attributes surfaces adjacent segments you would not find with manual rules.
Privacy and governance	Acquisition data is sensitive. Row and column controls, audit, and data minimization should be enforced by the platform, not bolted on.
Activation and measurement	Audiences need to reach ad platforms and marketing channels, and results need to flow back to refine the next audience.

How Databricks approaches prospecting

Databricks builds prospecting on the lakehouse, so audiences are created from the same governed first-party data your analytics and AI already use. The marketing-specific layer is CustomerLake, Databricks' agentic customer data platform embedded in the platform. It brings Customer 360 profiles, identity resolution, segmentation, and activation into Unity Catalog rather than a separate system.
For in-category prospecting, CustomerLake includes an audience builder that marketers drive in natural language through Genie: you describe the audience you want and Genie translates it into a query against unified profile tables, with no manual SQL. High-value segments become the seed for lookalike expansion, and propensity models score each prospect by likelihood to convert so budget goes to the strongest reachable audience.
Adjacent prospecting is handled by an Affinity Agent that analyzes attributes and behaviors to surface patterns a marketer would not think to query, such as segments that over-index on a related interest. Agent Bricks, which is generally available, orchestrates these steps as a multi-agent workflow, routing between the natural-language builder and the affinity discovery agent through a supervising agent.
Underneath, Lakeflow Pipelines prepare and refresh the profile and attribute tables, and Databricks AI Search can find semantically similar profiles for lookalike expansion. Finished audiences are saved as governed tables in Unity Catalog and shared to downstream ad and marketing platforms through Delta Sharing, so you activate without copying data into another vendor's environment. Because building, activation, and measurement all sit in one place, campaign results return to the same tables and sharpen the next round of prospecting.

Use cases

Retail and consumer brands seed lookalikes from loyalty members and high-margin buyers, then use affinity discovery to open adjacent categories for new launches.
Media and advertising build privacy-safe prospecting audiences from first-party engagement data and activate them to programmatic partners without exposing raw identifiers.
Financial services and travel rank prospects by predicted value or booking propensity, focusing acquisition spend on the segments most likely to convert.

FAQs

What makes a lookalike audience accurate?

The quality of the seed. A seed built from resolved, first-party profiles of genuinely high-value customers produces sharper lookalikes than a small or duplicated list. Modeling on unified data in the lakehouse keeps the seed clean.

How is adjacent prospecting different from in-category prospecting?

In-category prospecting finds new buyers who resemble current customers in the same category. Adjacent prospecting finds buyers in related segments by discovering shared affinities and behaviors, which expands reach beyond the obvious audience.

Do I have to move data to an external CDP to prospect?

No. With CustomerLake, audiences are built and governed as tables in Unity Catalog and shared to activation channels through Delta Sharing, so first-party data stays on your governed foundation.

How does AI improve prospecting over rule-based segments?

AI ranks prospects by predicted propensity and discovers adjacent affinities automatically, so you target likely converters and related segments instead of relying on manual category rules that miss unseen demand.