A glossary: Demystifying price optimisation.

Data and AI help drive profitable pricing decisions. The problem is retail technology can be riddled with terms and acronyms.

Category:
Essential knowledge

Last updated:
June 2024

Your one-stop guide to pricing optimisation terms and acronyms.

In an always evolving retail landscape, price optimisation is a key area for success.

Whether you’re a seasoned retailer or just starting out, we’ve created a glossary of retail pricing optimisation terminology to bridge any knowledge gaps.

A metric for determining the relationships between products, including how likely they are to be bought in the same transaction. High affinity items are often ideal targets for promotions, such as BOGOF offers.

Computer systems that can perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and language translation.

A notification or warning triggered by a pricing system when certain conditions are met (e.g. low stock, high discounts, etc.)

A way for two software programs to communicate and share data/functions e.g. connecting HyperFinity with an ERP or CRM system.

The average price at which products are sold over a period of time.

A record of who made pricing changes, what was changed, and when for auditing purposes.

The regular non-discounted price of a product.

Technologies, applications and practices for collecting, integrating, analysing, and presenting business information to support better decision making.

Metrics around how many customers view a product page versus how many actually purchase.

Guidelines or constraints applied to pricing decisions, such as setting maximum discount percentages.

Software for creating, managing and publishing digital content like prices or product descriptions.

Adjusting prices based on new data/performance to better achieve goals.

Making decisions by analysing objective data rather than anecdotes or gut instinct.

The process of customising a system’s settings or parameters.

Restrictions or limitations placed on the pricing optimisation model, like minimum margins or price gap to competitors.

The amount paid by the retailer to acquire a product.

Different situations or requirements that influence a customer’s purchase decision.

Technologies/processes for enhancing decision-making capabilities.

A prediction of customer demand over time using AI, based on historical data.

A measure of how customer demand changes when price, assortment, or stock availability changes. For example, if a pair of navy blue sliders is out of stock, demand may transfer to the same pair of sliders in a different colourway.

A deduction from the regular selling price of a product.

Software that manages core business processes like inventory, pricing etc.

The process of extracting data from sources, transforming it, and loading it into a data warehouse/analytics system.

When a product’s actual performance deviates significantly from forecasted/expected performance.

An estimate of future demand, sales, or performance based on past data and intelligence.

Inventory spread across many locations instead of consolidated making fulfilment challenging.

Product categorisation based on quality/price tiers (e.g. good=basic, better=mid-range, best=premium).

Constraints set on a pricing optimisation model to prevent undesirable outcomes.

Multi-level classification system organising products into categories, subcategories, etc.

Evaluation of how a pricing change will affect demand, revenue, profit etc.

Methodology of inflating prices, promoting select products, reviewing results.

Metrics used to measure performance against targets.

High importance products that drive a large portion of a business’s revenue/profit.

An assortment plan defining what products to carry in what locations.

Products that individually sell in lower quantities but collectively can be very profitable.

The difference between a product’s selling price and its cost – the profit earned.

Temporarily discounting a product’s price, often to clear inventory.

The process of tracking and quantifying metrics of interest.

A customer loyalty mechanic. Retailers offer discounts for consumers checking out with their loyalty card. Tesco’s ‘Clubcard Prices’ is a great example of successful member pricing.

A quantifiable measure used to track performance (ex: units sold, gross margin return on investment).

Methodology of modelling performance, optimising prices/assortments, measuring results.

A data science technique which demonstrates how customer demand changes with price increases and/or decreases. For example, demand for inelastic items doesn’t change, regardless of price.

Using machine learning to understand the potential impact of pricing decisions, including KPI shifts and demand transference.

A measure of how customer behaviour is influenced by price.

Every item in a retailer’s portfolio should be tagged at size level under a group ID. This helps retailers identify stock issues e.g. fragmented, overstocked, or understocked.

Finding the optimal solution within given constraints to maximise desired outcomes.

Manually changing/bypassing system-recommended prices based on manager judgment.

Summarising revenues, costs/expenses and profitability over time.

Overarching principles and structures that guide an organisation’s pricing approach.

Modelling the estimated impacts of proposed price changes.

Data file containing individual product prices for uploading/integrating into systems.

Difference between a company’s price and competitors’ prices for the same product.

Any change made to the price of a product (increase, decrease, discount etc).

Software application that recommends optimal prices to maximise revenue/profit goals.

A set of modelled outcomes based on different pricing strategies/inputs.

Data, analysis and insights utilised to make more informed pricing decisions.

Methodology of using analytics to set product prices to maximise revenues/profits.

Temporarily discounting or incentivising the sale of specific products.

A grouping of related products (e.g. shirts, trousers, footwear etc).

The mix of product categories and individual products offered.

Information describing products like name, features, specifications etc.

Associations between products for analytics (e.g. accessory products).

The amount of revenue remaining after accounting for costs and expenses.

Return on investment – profit/gains generated relative to the investment/cost.

Aggregating and summarising lower-level data to higher levels (e.g. category/total sales).

Pricing strategies involving non-rounded ‘psychological’ prices.

The loss of sales or revenue because of one product displacing another one.

Products carried for a particular season/period of the year.

The percentage of inventory units that were sold.

A unique identifier assigned to each distinct product.

Amount of inventory relative to forecasted sales.

The maximum profit margin that could be achieved under perfect conditions.

Levels within a product hierarchy/categorisation (premium, mid-range, value etc).

An employee/role dedicated to merchandising and pricing responsibilities.

How a product is positioned relative to quality and cost in the market.

Related versions of the same core product (different colours, sizes etc).

Graphical depictions of data/analytics to facilitate understanding.

The sequence of processes and approvals required to complete a task like a pricing change.

What’s next?

Knowledge is power and data is profit. Contact our team today to schedule a demo or talk through our new pricing module.

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