The application of data science and AI to improve commercial decision making. Invented by Dr Lorien Pratt and Mark Zangari.

Software that aggregates and organizes customer data from a variety of touchpoints into a single customer profile.

Data is stored and processed in a cloud database (typically AWS, Azure or Google Cloud).

Data, insight and models stored against a customer record in a database, enabling personalised customer experiences to be delivered to them.

Describes customers and products in useful ways that help improve marketing and merchandising of products.

Groups of customers who share similar characteristics.

A data science technique to identify the relationships between customers and products.

Also known as customer decision trees. A graphical representation of a customer’s buying decision process expressed in a tree format.

Leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.

Presenting business data in user-friendly views such as reports, dashboards, charts and graphs.

A field that uses scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights.

The extent to which one product is viewed as a good substitute for another product e.g. Coca Cola for Pepsi. Head over to our blog to read more about substitutability.

A simple way to segment customers based on their spending patterns. Also known as RFM (recency, frequency, monetary value).

Assesses the likely financial value of a customer over the full length of their relationship with a business.

A way of describing customer segments, typically in a highly visual way, bringing data to life for non technical people.

Data collected as a result of a customer transacting directly with a business e.g. their purchases, web browsing behaviour or customer feedback.

Refers to the digital or physical space a retailer offers brands to promote their products. For example, space on their website, app or digital displays in and around stores.

A piece of software that analyses various data sources to create insights into customer behaviour and needs, then recommend actions.

Layering involves applying multiple analytical techniques and processes, each building on those previously used to create ever greater insight and value.

Using analytical techniques to decide on the ideal set of products to sell in order to maximise sales and profit.

A set of well-defined instructions to recommend optimal pricing at any given time.

Applying analytical techniques to predict the likely demand for products, based on a combination of factors.

An analytical approach to recommending products that will most likely appeal to a customer or segment of customers.

Using analytics to determine the optimal price discounts to apply, usually to strike a balance between sales and profit.