Price Beat and Price Match: Rules, Evidence and Controls
Automate an approved price promise with verified competitor evidence, explicit policy limits and exception handling. Review the decision with synthetic tests.
Price Beat experience
When everyone sells the same product, price decides. Multi-brand retailers: The same branded product is on sale at several stores, so the shopper compares like for like. Range, brand and description don't set you apart — a credible best-price answer does. That's why shoppers open another tab. Agentic commerce: An AI assistant or shopping agent compares prices across stores in seconds and acts on what it finds. If your price isn't the best, or can't be shown to be, the agent moves on. It needs a policy-backed answer it can rely on, not a page to read. Price Beat gives both a governed answer: match, beat or affirm. Price Match for AI Agents: /conversational-price-match
An AI assistant or shopping agent can call the retailer's Price Beat over MCP for a real-time price check, and gets back match, beat or affirm within retailer-defined guardrails; the retailer's policy decides and the adjustment is applied through the agreed commerce integration.
Seven price-check intent triggers: In-page button (shopper taps the always-on price check button); Text highlight (product name selected, as if to copy it into a shopping search); Exit intent (cursor heads for the address bar or tab strip); Tab change & return (shopper leaves to compare, then comes back to the tab); Scroll depth & dwell (lingering on the price area without adding to basket); Predictive intent (several signals combine into a price-sensitive score); Asks in their own words (shopper types "is this cheaper anywhere else?" into on-site search or the store's chat assistant).
Outcomes: Beat or match: Match or beat within retailer-set limits; a ghost coupon applies the difference at checkout (SKU or basket rules). Affirm: Say so, with a verified-today stamp. No discount needed. Hold: Nothing changes; the exception is logged for review.
Leading toy retailer, LEGO products vs 3 approved competitors: +306% conversion with price beat, +81% with best-price affirmation. Two variants: confirming the retailer already had the best price, and citing a lower price elsewhere then making up the difference. Even affirmation alone converted price-comparison shoppers. Reported result from one deployment (Similar Inc attribution). Not a forecast; causal reading depends on that deployment's test design.
Leading pet retailer, Automated price beat policy vs an approved competitor list: +97% conversion with price beat, +79% with best-price affirmation. Helped the retailer gain market share and drive member sign-ups in a highly competitive market. Reported result from one deployment (Similar Inc attribution). Not a forecast; causal reading depends on that deployment's test design.
Leading camera retailer, Canon products vs 6 approved competitors: +167% conversion with price beat, +66% with best-price affirmation. Helped the retailer gain share in a highly competitive camera market. Reported result from one deployment (Similar Inc attribution). Not a forecast; causal reading depends on that deployment's test design.
DJ City, Price Beat and Best Price Affirmation across brands: +209.5% conversion with price beat. Session conversion uplift on Price Beat enabled items at a -2.4% average discount. AI Product Bundles protected margin: +72.1% AOV and +42.4% units per transaction. Reported result from one deployment (Similar Inc attribution). Not a forecast; causal reading depends on that deployment's test design.
The Australian · Business Review, 17 June 2024: Conversions increased by up to 30 per cent once customers see the “verified this morning lowest liquor price guarantee banner” next to the item. Public press coverage of the price-affirmation approach. It does not name Particular Audience and is not presented as a PA deployment.
Price Match & Price Beat
Automate an approved price promise.
Works with
- Pricing Intelligence — Competitor data and policy infrastructure.
- Trade Media — Supplier-funded savings, governed like media.
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Outcomes it moves
Price Beat and Price Match
Price matching meets an eligible competitor offer; price beating goes below it by an approved amount or percentage. Automation needs a verified comparison, an authorised policy and an explainable decision. Preserve the current price when no adjustment is needed, and route failed or uncertain cases to the agreed exception process.
Start with an approved price promise
Write down the products, competitors, locations and offer conditions covered by the policy. Decide which evidence is acceptable and how recent it must be. Specify what happens when the competitor offer is unavailable or cannot be compared on the same basis.
Equivalent products still need equivalent offers. Check variant, condition, currency, tax treatment and required delivery or membership terms. Missing information should not become a confident best-price statement.
PA's Pricing Intelligence describes the adjacent evidence and policy-automation service. Use a scoped review to confirm supported sources and comparison rules for your category.
Evaluate the adjustment before applying it
Calculate the candidate match or beat using the approved policy. Then test the maximum discount, minimum contribution requirement and basket rules. If the candidate is already at or above the current price, keep the current price; do not increase it merely to meet a competitor comparison.
When a candidate fails a limit, use the agreed review or decline outcome. Do not silently change it to a different amount while claiming the original price promise was fulfilled.
Work through a synthetic policy test
Suppose the current price is £100, an eligible competitor price is £95 and the approved beat is £1. The candidate price is £94. With £70 of relevant variable cost and a minimum contribution rate of 20% of selling price, the illustrative floor is £87.50: £70 divided by 0.80.
The £94 price is above that floor and represents a £6 discount. Its contribution rate on the stated cost basis is approximately 25.53%. These assumptions are invented, exclude any unlisted cost and are not a profit guarantee.
If the competitor price is £105, the calculated beat target is £104 and the current £100 price remains unchanged. If the competitor price is £80, the £79 candidate fails the floor and needs the specified exception outcome.
Keep a decision record
Record the evidence, policy version, comparison basis, candidate, limits and final outcome. Test repeated requests, changing prices and offers that expire before checkout. Confirm how the approved adjustment is applied and reversed when a qualifying purchase is cancelled.
The price-policy requirements library is a starting point for capability questions. A table field does not by itself establish a supported live setting or integration.
Measure an implemented policy
Inspect decision accuracy, shopper completion, discount funding and contribution. If you want a causal performance estimate, define a suitable comparison and disclose its design.
Use customer case evidence only with its actual sample, period and comparator. Broad conversion ranges and engagement-selected groups should not become a forecast for every product. Begin with a category policy and acceptance tests that the pricing, finance and commerce teams can approve.
Frequently asked questions
What if our current price is already below the beat target?
Keep the current price and record that no adjustment is needed. A separate competitiveness message requires valid evidence and appropriate scope; do not increase the price to the calculated target.
What if a verified competitor offer is below our policy floor?
Apply the agreed review or decline process. A floor protects the chosen cost and margin policy, but it does not make a different price fulfil the original promise. Explain the outcome clearly.
Can published case uplifts be used in our business case?
Only with their actual design and limitations, and they should not be treated as a forecast. Request sample sizes, dates, comparator definitions and uncertainty. An engagement-selected comparison is not automatically a randomised test.
Common questions
What is a price beat policy?
A promise to beat a competitor's verified price by a set amount or percentage when a shopper provides evidence, governed by eligibility rules the retailer controls.
How is price beat different from price matching?
Matching equals the competitor's price; beating goes below it by a configured margin. Both need evidence validation and eligibility rules to be safe to automate.
Can price match be automated safely?
Yes — when every claim is validated against competitor evidence, tested against eligibility rules and logged with a rationale. PA's Price Beat and Price Match for AI Agents are built to apply those checks.