Price Optimization | SignalFlare AI

The Problem

Between 2022 and 2024, aggressive price increases triggered traffic losses that revenue growth masked until it was too late.

The problem was not raising prices.

It was raising them without the data to know where, how much, and which items each market could actually bear.

It was raising them without the data to know where, how much, and which items each market could bear.

$99B

Estimated industry revenue lost

to mispricing in 2022–2024.

What we do differently

Same question.
Fundamentally different answer.

Better Data

SignalFlare triangulates mobile, credit card spending, and competitive signals to understand the true purchasing power of your actual guests — today, not last quarter. POS elasticities tell you what happened. They can't tell you what consumers can spend right now.

SignalFlare triangulates mobile, credit card spending, and competitive signals to understand the actual purchasing power of guests in a trade area. POS-based elasticities tell you what happened — not what consumers can spend right now.

Better Science

Every item runs through 10,000 probabilistic simulations so you see a range of outcomes and the risk behind each one. Basket-level elasticities capture check decomposition — the quiet risk legacy econometric models miss entirely.

Every item runs through tens of thousands of simulations so you see a range of outcomes and the risk behind each one. Basket-level elasticities capture check decomposition — what legacy econometric models miss entirely.

Better Delivery

Navigator puts a pricing advisor in every operator's hands — plain-language rationale, competitive context, and what-if scenarios on demand. Whether it's a franchisee reviewing recommendations or a corporate team planning a rollout, Navigator knows the trade area better than anyone in the room.

Navigator puts a pricing advisor in every operator's hands — plain-language rationale, competitive context, and what-if scenarios on demand — whether it's a franchisee reviewing recommendations or a corporate team planning a rollout.

How we do it

The science behind a recommendation you can actually act on.

The science behind a recommendation

Legacy pricing vendors use linear regression, a method from the 1970s that produces a single point estimate and assumes consumer behavior is constant.

SignalFlare uses a fundamentally different approach.

Dual risk measurement

The risk your POS data can't see.

Most vendors only measure traffic loss. SignalFlare also measures check degradation, where customers quietly reduce their basket to stay within a spending threshold.

Dual risk measurement

The risk your POS data can't see.

Most vendors only measure traffic loss. SignalFlare also measures check degradation, where customers quietly reduce their basket to stay within a spending threshold.

Dual risk measurement

The risk your POS data can't see.

Most vendors only measure traffic loss. SignalFlare also measures check degradation, where customers quietly reduce their basket to stay within a spending threshold.

Probabilistic simulations

All outcomes, before you commit.

Every item runs through 10,000 simulations. Instead of a single elasticity number, you get a full range of outcomes with the probability behind each one, including the downside scenarios.

Probabilistic simulations

All outcomes, before you commit.

Every item runs through 10,000 simulations. Instead of a single elasticity number, you get a full range of outcomes with the probability behind each one, including the downside scenarios.

Probabilistic simulations

All outcomes, before you commit.

Every item runs through 10,000 simulations. Instead of a single elasticity number, you get a full range of outcomes with the probability behind each one, including the downside scenarios.

Basket-level analysis

Every price change reshapes the entire basket.

Raising one item's price affects what customers order around it. SignalFlare models the entire basket simultaneously, capturing effects that item-by-item analysis misses entirely.

Basket-level analysis

Every price change reshapes the entire basket.

Raising one item's price affects what customers order around it. SignalFlare models the entire basket simultaneously, capturing effects that item-by-item analysis misses entirely.

Basket-level analysis

Every price change reshapes the entire basket.

Raising one item's price affects what customers order around it. SignalFlare models the entire basket simultaneously, capturing effects that item-by-item analysis misses entirely.

Navigator in pricing

Every franchisee with an advisor that knows their market.

Pair every franchisee with an advisor that knows their market.

Navigator is SignalFlare's AI agent, built specifically for pricing decisions at franchise scale. Franchisees stop being passive recipients of PDF reports and become active participants with intelligence available on demand.

Learn more

Impact Simulator

Operators run what-if scenarios before any price change goes live, with projected traffic, revenue, and margin impact across multiple risk levels.

Franchise AI Advisor

Navigator presents recommendations with plain-language rationale and answers questions about competitive context or elasticity without needing an analyst on the phone.

Promotional gate

Every LTO or promotional price runs through a pre-launch simulation, a launch validation, and an automated post-mortem that compares actual results to projections.