Signal Analytics · Price Pulse

Run your pricing analysis

Pick a company, region, country and brand. Click Calculate to surface your biggest move now, the next three for this quarter, and the long term strategic plays.

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NOW: Biggest pricing decision to make right now

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Based on the highest Expected Value ($m) opportunity in your current filters.

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NEXT: Your next pricing interventions this quarter

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LONG: Long term strategic moves

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1) Prioritize top EV markets for quick pricing tests

Highest financial impact first

Run controlled price tests in the top 3 to 5 markets by Expected Value ($m) where promo depth is high and Effective Price is materially below competitor median.

"Start where the gap is largest and the downside is cushioned by promotions."

2) Tighten promo leakage in over discounted SKUs

Margin capture

Dial back depth or frequency where Effective Price is already competitive, then re invest a fraction into base price uplift to preserve volume while improving unit margin.

"Trim the discount first, then lift the sticker, volume shock stays minimal."

3) Expand competitor price coverage and elasticity priors

Confidence up

Automate weekly competitive price and promo capture for the top 10 SKUs per market, then layer category elasticity priors to strengthen the EV ranking and de-risk outliers.

"Better competitor telemetry turns a good list into a conviction list."

Supporting tools

Drill into the data, export, and check the method

The cards above are where the decisions get made. Below: the full table, downloads, and how the model works under the hood.

All data: filter as you like, don't forget to scroll right!

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Company	Region	Country	Brand	SKU	Pack-Ladder Leak	Promo Waste Score	Psych Proximity	Charm Suggestion	Incremental Revenue ($m)	Expected Value ($m)	Local Price	FX to USD	Pack Size	Arbitrage Risk	Price USD/100	Competitor Median (USD/100)	Promo Depth %	Effective Price (USD/100)

Download the data

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Includes header, NOW / NEXT / LONG insights and top 10 data table for your current filters.

How this model works

Point of difference Hide/Show

What sets this approach apart from traditional consultancies and agencies is the way it combines live observation, advanced modelling and language intelligence to create decision grade outputs.

Live and granular: today's shelf prices, promotions and competitor medians are captured directly in market, giving a real time view rather than delayed panels or reports
Decision focused: economic logic such as pack ladder leaks, psychological thresholds and promo waste scores turns raw inputs into clear opportunities
Human at scale: a language model explains each move in plain terms so teams understand the why and how
Forward looking: we provide a ranked radar, NOW, NEXT, LONG
Variance advantage: live data plus behavioural logic delivers low variance for decision making. With first party weekly sales data variance narrows further

Inputs and data sources Hide/Show

Observed directly, market facing

Prices and promotions: captured from publicly available brand and retailer websites in each market, for example Tesco, Carrefour, Walmart, Amazon, brand.com
Competitive medians: derived from the same observed items per market, stabilized by using the median

Reference inputs

FX rates: applied from daily published feeds (e.g., ECB, Bank of England)
Market scaling: calibrated from internal category models built on historic performance

Derived calculations

Effective price per 100g = list or base price × FX ÷ pack size ÷ (1 − promo depth%)
Expected value ($m) = incremental revenue × execution probability
Pack ladder checks, psychological thresholds and charm suggestions are mechanical checks on observed prices

Variance and confidence Hide/Show

Every figure is observed or transparently calculated. Variance comes from input refresh cycles and small reference differences.

Typical variance by input

Prices and promotions: ±1–3%
Competitive medians: ±2–4%
FX rates: ±0.5% vs company internal FX
Execution probability priors: ±5–10% at SKU level
Market scaling multipliers: ±5% at country level

Total potential variance impact, public data only

At SKU level: ±8–12%
At brand or market level: ±3–5%

These ranges are already small enough to make confident prioritization decisions.

With first party weekly sales data

At SKU level: ±3–5%
At brand or market level: ±1–2%

First party sell out data calibrates execution probabilities and promo effectiveness directly, which tightens the model beyond what public inputs can do on their own.