More Than Data. Actual Decisions Improved ROAS. - SignalFlare AI | Agentic Decision Intelligence for Restaurants

Objective

A regional restaurant brand was running a paid media campaign for a seasonal menu item using an existing location insights and demographic' provider for audience targeting. The restaurant ran a side-by-side evaluation of their existing tool against SignalFlare's Navigator — building a data-driven targeting brief their agency could execute immediately and replicate across all locations.

Navigator delivered a greater ROAS, driving measurable increases in traffic and same-store sales within days of launch.

Results

1 session

Brief built and handed off — targeting output sent to agency same day.

Outperformed

Outperformed the client's existing location insights data provider, delivering deeper analysis and actionable decisions where the prior tool fell short.

All locations

Rollout confirmed — client ran the same workflow across every location.

Deeper

Next phase planned to include POS data, unlocking transaction-level correlation.

How it was done

Step 1. Tool Evaluation & Campaign Framing

The restaurant operator came in already using a location intelligence platform for foot traffic and demographic data. Navigator was evaluated alongside that existing tool.

The question: does SignalFlare’s data, which was acknowledged to be more granular, provide a greater return on ad spend, and impact on sales and traffic than the current tool? Can it run deeper/provide more insights and actions the team can implement?

The campaign context: a spring season Google Ads push for two Texas locations, targeting customers most likely to drive demand for an LTO menu item. Targeting needed to reflect the seasonal customer profile — not the brand’s everyday guest.

Step 2. Audience & Demographic Analysis

• Target audience profiles built for each location using SignalFlare’s Demand Dynamics neighborhood intelligence data

• Demographic and behavioral characteristics of the seasonal menu item customer identified

• Clear distinction drawn between the seasonal audience and the brand’s everyday guest — directly shaping ad copy direction

Step 3. Geographic Targeting Coordinates

Navigator auto generated precise coordinates for ad targeting to drive the greatest demand. No manual mapping required.

Step 4. Data Transparency Layer

The team prompted Navigator to explicitly document its sources — distinguishing between: (1) data drawn from proprietary neighborhood intelligence, (2) inferred insights, and (3) known gaps in data collection (no brand specific data was used for this test – including POS, loyalty, guest sentiment, etc.)

This transparency layer was flagged as particularly valuable when sharing recommendations with agency partners who want to understand the basis for targeting decisions.

Step 5. Structured HTML Output

The full analysis was exported as a self-contained HTML artifact: audience summary, demographics, geo-targeting coordinates, ad radius guidance, recommended keywords and bidding strategy — designed so the agency recipient could review everything without scrolling through a chat session.

Step 6. Expanding the Context Layer

• The analysis and decisions were based on the proprietary data and models in the SignalFlare Demand Dynamics platform

• Discussed uploading POS data, anonymized loyalty data, guest sentiment, past Google Ad results, etc. to further enrich the results in future campaigns

Step 1. Tool Evaluation & Campaign Framing

Step 2. Audience & Demographic Analysis

• Target audience profiles built for each location using SignalFlare’s Demand Dynamics neighborhood intelligence data

• Demographic and behavioral characteristics of the seasonal menu item customer identified

• Clear distinction drawn between the seasonal audience and the brand’s everyday guest — directly shaping ad copy direction

Step 3. Geographic Targeting Coordinates

Navigator auto generated precise coordinates for ad targeting to drive the greatest demand. No manual mapping required.

Step 4. Data Transparency Layer

This transparency layer was flagged as particularly valuable when sharing recommendations with agency partners who want to understand the basis for targeting decisions.

Step 5. Structured HTML Output

Step 6. Expanding the Context Layer

• The analysis and decisions were based on the proprietary data and models in the SignalFlare Demand Dynamics platform

• Discussed uploading POS data, anonymized loyalty data, guest sentiment, past Google Ad results, etc. to further enrich the results in future campaigns

What made it valuable

Outperformed the existing data platform on the only metric that matters: translating data into decisions the team could act on and the campaigns guided from Navigator drove higher customer traffic

Neighborhood intelligence went from raw to actionable without analyst involvement — the gap the prior tool couldn’t close

Seasonal audience distinguished from the general guest — a distinction invisible in the raw data and critical for campaign targeting

Agency received a structured brief, not a data dump — no reformatting or interpretation required

Every data source cited and explained — builds trust with clients and agency partners reviewing the output

Workflow fully templated and repeatable across all other brand locations

Navigator is huge. [Previous data provider] data is effectively useless without knowing what to do with it — Navigator removes that barrier.

Marketing Director

From Data Overload to Actionable Strategy in 6 Hours

2 Million Log Records. Minutes to Answers.