Jargon Buster

Artificial intelligence (AI) in digital advertising refers to the use of machine learning algorithms and data analytics to automate, optimise, and personalise ad campaigns. AI enhances targeting, predicts consumer behaviour, manages budgets, and delivers relevant content, ultimately improving campaign efficiency, engagement, and return on investment (ROI) for advertisers.

A computational model inspired by the structure of the human brain, used to recognise patterns and make decisions.

The Advertising Standards Authority is the UK’s independent regulator of advertising across all media. It applies the Advertising Codes, which are written by the Committee of Advertising Practice (CAP) for non-broadcast media including online, and the Broadcast Committee of Advertising Practice (BCAP) for broadcast media. The ASA's remit includes acting on complaints and proactively checking the media to take action against misleading, harmful or offensive advertisements.

The dimensions of a display screen’s image expressed as a ratio of the horizontal width to the vertical height.

Attention is a consumer looking at or listening to an ad at the time they were exposed to it. A typical base measure for attention is time, and attention measures can be used in different media contexts such as video or audio. 

Attribution is the technique used to measure the monetary impact a piece of communication has on real business goals. For example: sales (volume and total), profit, revenue and retention. The application of attribution modelling in digital advertising allows marketers to understand what events (e.g. display ad exposure, active search, search ad exposure, price comparison site) truly influence individuals to convert and thus allocate credit to different formats and tatics within the customer path to purchase. Early attribution models include first-touch and last-touch attribution. Models broadly recognised as more accurate include, but not-only, multi-touch linear, multi-touch time decay and multi-touch algorithm-based.

Also known as “churn rate,” this can be used to reflect the percentage of consumers who abandon each stage of the purchase process, from exposure to an ad to the point of conversion or, for subscriber-based business models, the percentage of customers who stop using the product or service. This percentage is an important part of evaluating the return on marketing investment and/or customer lifetime value modelling.    

Group of people with a common set of characteristics whom an advertiser wants to show an ad. More specifically this is a list or group of customers or individuals that is most likely to purchase a given product or service from an advertiser.

A process of connecting advertiser target audience with publisher audience for targeting them through digital advertising channels.