Locality and Databricks Launch Advanced Audience Engine for AI Local Ads

What just launched, and why it matters for growth leaders

On September 29, 2025, Locality announced the Advanced Audience Engine, an identity and activation layer built with partners including Databricks to power AI-driven local advertising across streaming and broadcast. The company positions it as a way to unify advertiser first-party data with Locality’s viewership and performance signals, then plan, activate, and optimize inside LocalX. For the core claims on identity, activation, and real-time optimization, see the Locality launch press release.

For growth and marketing teams, the most expensive part of local is often inefficiency. You juggle data clean rooms, identity graphs, CDPs, multiple demand platforms, and disconnected measurement. The Advanced Audience Engine promises to shrink that sprawl. If it works as described, you get faster cycle times for creative and audience tests, higher precision in geographies that actually convert, and a single view of performance across linear and streaming. That translates into better cost per acquisition and the ability to defend or reallocate budget in quarter. If you like fast experiments, compare this cadence with our two-week growth playbook.

What is inside the Advanced Audience Engine

Locality describes the Engine as a proprietary local identity intelligence and activation framework that organizes first-party and third-party data alongside Locality’s historical media intelligence and real-time viewership signals. Functionally, that implies:

• Identity and audience assembly with deterministic and probabilistic stitching across local households and devices.
• Vertical-specific segmentation so an auto dealer or a regional health system sees different template audiences than a national QSR franchisee.
• AI-driven budget allocation and pacing that uses predictive signal to move spend among local inventory sources.
• Unified measurement and attribution expressed back inside LocalX rather than a patchwork of vendor dashboards.

Databricks shows up as the data and AI backbone. The company has been promoting a marketing-focused version of its Data Intelligence Platform that unifies customer and campaign data for real-time use by non-technical marketers. That direction matches what Locality is building for audience planning and activation. For context, see Databricks marketing data intelligence.

Why this could change how you buy local

Local buying often breaks down in three places: audience quality, activation speed, and measurement clarity. The Advanced Audience Engine claims to tighten all three.

• Audience quality: If your first-party data is actually in the loop, you can anchor local segments on last-mile outcomes, not broad demographics. That increases match rates to real converters and keeps lookalike modeling honest.
• Activation speed: When audience building, inventory access, and pacing signals live in one workspace, you can launch and adjust in hours, not weeks. That matters when local creative is time bound, such as a weekend promotion or a weather-driven offer.
• Measurement clarity: If linear, streaming, and offsite digital all roll up into one attribution spine, you can stop guessing whether CTV drove footfall and whether those households later converted online.

A two-week pilot plan that proves or disproves the promise

The goal is not to boil the ocean. The goal is to unify a minimal first-party backbone, activate two or three audience concepts in a handful of designated market areas, and get clean reads on CPA and ROAS.

Week 0, preparation checklist

Before day one, line up these inputs and controls:

• Scope: 3 to 5 DMAs with clear store coverage or service area. Include one stronghold market, one expansion market, and one control you will not target for clean incrementality reads.
• First-party data: A consented table with user or household identifiers, last 180 days of transactions, product or service categories, average order value, and postal code. Include an orders table keyed by order ID and timestamp for later match back.
• Creative: Two message variants per audience, each in 15 second CTV and 6 second digital video where possible. Keep price callouts and offer windows aligned to the two-week timeline.
• Guardrails: Frequency cap per household, brand safety requirements, and a daily budget cap per DMA.
• Measurement plan: Define conversion windows, set a holdout design, and agree on the primary and secondary metrics.

Primary metrics

• Cost per acquisition or cost per booked appointment for services.
• Return on ad spend where revenue attribution is available.

Secondary metrics

• Reach and unique households, DMA by DMA.
• Onsite engagement signal that correlates with purchase, such as add to cart or lead form completion.
• Store visit proxy if you have footfall panels or first-party check-in events.

Days 1 to 3, unify data and build the first audiences

• Ingest your customer, transaction, and product tables into the Databricks environment that underpins the Engine. Establish daily incremental loads.
• Standardize identifiers and run a data quality pass. Look for duplicate customer IDs, missing postal codes, and stale households that should be excluded.
• Define three test audiences:
  • High-intent recency: last 30 day website or app engagers with no purchase in 14 days, in DMAs with at least 10 thousand reachable households.
  • Category affinity lookalike: last 90 day buyers of a profitable category, exclude recent converters, target nearby postal codes with highest historical ROAS.
  • Competitive conquest: households that index high for competitor viewership within your service radius, layered with your first-party exclusion lists.

Days 4 to 6, activate and calibrate pacing

• Launch campaigns across Locality inventory in streaming and, where available, linear placements. Use the same budgets by DMA and audience to keep comparisons fair.
• Apply a frequency cap of 3 to 4 per household per week for CTV, and 6 to 8 for short digital video.
• Turn on the Engine’s predictive budget allocation, but enforce a minimum 30 percent of spend per audience so early algorithmic swings do not zero out a cell before it has signal.

Days 7 to 10, creative swaps and geo tuning

• Midpoint review: identify audiences with CPA that is 20 percent above your baseline. If creative A underperforms creative B by 15 percent or more within the same audience, swap remaining impressions to the winner.
• Geo adjustments: look at postal code level ROAS or proxy. Shift up to 20 percent of budget into the top quartile postal codes within each DMA, but keep at least 50 percent in the original footprint to preserve the test design.

Days 11 to 14, holdout reads and next cycle planning

• Pull holdout reads on your control DMA against your exposed DMAs. If you track revenue, calculate ROAS as attributed revenue divided by media spend. If you track lead value, use closed loop value once available, and use lead to sale rate from the last 90 days as an interim multiplier.
• Prepare a one page decision memo: include CPA by audience and DMA, ROAS by DMA, effective reach per DMA, and a recommendation to scale, iterate, or pause. Include confidence labels so finance partners can understand the strength of the signal.

Instrumentation that keeps the test honest

Getting the plumbing right is the difference between a convincing pilot and an interesting story that finance will not fund.

• Attribution spine: Assign a unique campaign ID per DMA and per audience. Pass order ID, DMA, and audience ID into your analytics layer so conversion joins are deterministic.
• Offline match back: For stores or call centers, collect order IDs or phone numbers with timestamp at the point of sale. Run a daily privacy-safe match back to exposed households.
• Identity hygiene: Suppress recent converters for at least 7 days unless you sell replenishable items. This keeps CPA honest and avoids double counting retention as acquisition.
• Incrementality: Use at least one DMA level holdout and a 10 percent household holdout in the active DMAs. The Engine should support this directly or through linked clean room capabilities.

How to judge performance without fooling yourself

• CPA: Compare to your last two quarters of local CPA, not to a blended national number. Local typically costs more, but it should also convert at higher intent levels.
• ROAS: For ecommerce, require at least 1.5 to 2.0 ROAS in your pilot to justify scale, unless you have strong evidence of long-term value. For subscription or services, index on payback period in weeks rather than a single ROAS value.
• Efficiency over vanity: Reach will look strong in CTV, but if incremental visits or orders do not move with spend, turn down the budget and revisit audience definitions.

Where this fits in your stack

• CDP and CRM: Keep your customer truth in your CDP or CRM. Feed down to the Engine daily, and pass back conversion and exposure logs so your central profiles stay current. For broader operating patterns, see our pragmatic AI growth playbook.
• Data warehouse or lakehouse: Databricks will often sit alongside Snowflake or BigQuery in large organizations. If you already use Databricks, lean into streaming tables for faster feedback cycles.
• Measurement: Continue running your marketing mix model in parallel. Use the Engine’s attribution for fast reads and MMM for budget setting.

Risks and how to mitigate them

• Overfitting to small geos: Local segments can be tiny. Place minimum audience thresholds before activation and widen lookalikes when you dip below those thresholds.
• Creative fatigue: Local offers can burn out quickly. Bring a second wave of creative into the plan before day seven so you can swap without waiting on production.
• Identity bias: Deterministic graphs can overrepresent some households. Set reach and frequency targets that force exploration into light and medium viewers so you do not just saturate the same high-match households.

Team and process changes that make this stick

• Appoint an audience owner who partners with analytics. Their job is to keep segments fresh and aligned to business goals, not to run media.
• Give finance weekly visibility. Share CPA and ROAS rollups by DMA so they can co-own the decision to scale.
• Treat local like a product. Publish a simple release note each week with what changed in targeting or creative and why, plus what you learned. Teams testing autonomy can borrow ideas from our autonomous CRM pilot.

What success looks like after two weeks

If the Advanced Audience Engine does what it says, you should see faster launch cycles, measurable CPA improvements vs your recent local baseline, and a clear ROAS story by DMA. You should also have a repeatable workflow that can absorb new creative, new first-party signals, and new markets without a cost penalty.

Practical next steps

1. Stand up the data feed

• Define the three minimum tables you will share: customers, orders, and product or service taxonomy. Add postal code and DMA codes. Refresh daily.

2. Run the two-week pilot exactly as scoped above

• Lock the DMAs, audiences, budgets, and holdouts upfront. Resist midweek changes that add new variables.

3. Decide with numbers

• Use CPA and ROAS as the gating metrics. If CPA is lower than your last two quarter local median and ROAS hits your business threshold, scale to five additional DMAs. If not, adjust audiences and creative, and rerun a fresh two-week cycle.

4. Build the ritual

• Move to a two-week sprint for local for the next quarter. Keep a running backlog of audience hypotheses and a creative testing queue.

The bottom line

Locality’s Advanced Audience Engine is part of a broader shift to collapse audience assembly, activation, and measurement into a single, intelligence-aware workflow. Tying that to Databricks gives the Engine a credible data and AI foundation that should make first-party unification and real-time optimization more than slideware. The only way to know if it will materially change your unit economics is to run a tight pilot. Unify your data, launch in a small set of DMAs, and hold yourself to CPA and ROAS. If the numbers clear your bar, scale with confidence. If not, you will know exactly which levers to adjust in the next two-week sprint.