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Credit is disproportionately applied to events at the beginning and end of a path to conversion. Example: 40% of credit could be given to events occurring in the last day before a desired outcome, 20% to events occurring between days 1-13, and the remaining 40% to events occurring on the first day.

Credit is applied to events at increasing or decreasing intervals along a path to conversion. Event values are usually altered based on specific time windows when the events occur. Example: 40% of credit could be given to events within 24 hours of conversion, 30% to events within 1-3 days, 20% to events within 3-7 days, and 10% to events within 7-14 days.

Divides the marketing funnel into two parts determined by a consumer’s first website visit. The upper funnel consists of every brand engagement that occurs prior to a consumer’s first site visit. The lower funnel consists of every engagement that occurs after a consumer’s first site visit and ultimately ends in a conversion.

Single source attribution assigns all credit for a desired outcome to a single event. The most basic but most widely used type of attribution is “last touch” attribution, which gives 100% credit to the last meaningful event before a desired outcome takes place, generally the last ad impression (sometimes called ad view), last click, or last engagement.

Credit is assigned to multiple events along a path to conversion based on a predetermined set of rules. Examples of rule sets include even weighting, time decay, and u-shaped.

Probablistic matching uses aggregation techniques to identify statistically significant correlations between many different anonymous data points eg. Device type, operating system, location data, IP address. Through this process a device graph and/or user profile is created for an individual user across multiple screens.

Credit across all channels leading to conversion, but percentage is determined by the order of visit. By default, the Position Based model attributes 40% of the credit to the first and the last interaction and the remaining 20% is distributed evenly to all the interactions in the middle.

Measuring ad effectiveness amongst respondents who have been exposed to advertising in a natural rather than artificial setting.

Multiple source attribution is the process of collecting and analysing more than one advertising event contributing to an outcome. This type of measurement is based on the belief that all advertising events that occur within a campaign—across channels, platforms, and formats can have an impact on consumer behaviour when contributing to a desired outcome.

An econometric model that uses historical marketing and sales data in statistical models to measure the impact of marketing on sales. MMM is causal modelling in which attempt to explain or predict market share or sales volume from marketing inputs, while controlling for other sales drivers such as seasonality and the competitive environment.

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