MTA stands for Multi-Touch Attribution, a method used to measure the impact of various advertising channels and touchpoints.

MTA refers to Multi-Touch Attribution, a technique used to evaluate the effectiveness of different marketing channels and touchpoints.

Single-source attribution assigns all credit to one consumer touchpoint where multi-touch attribution divides this credit.

Multi-touch attribution means assigning value to all the touchpoints a customer encounters on their path to conversion.

Traditional legacy MMMs can take weeks, but more often months to build and to glean insights.

Traditional Marketing Mix Models can easily most easily be identified by their method of delivery: Excel spreadsheets and powerpoint presentations.

Yes, in many cases, Marketing Mix Modeling can and should be integrated with other methods like incrementality testing.

Marketing Mix Modeling limitations include a reliance on historical data, the inability to capture short-term effects, and the challenge of accounting for external factors that may influence sales.

AI has paved the way for a daily refresh of a model, allowing for an agile bespoke MMM for your business.

Yes, Marketing Mix Modeling works well for both addressable and non-addressable marketing channels.

Marketing Mix Modeling is particularly beneficial for businesses with multiple marketing channels, multiple sales channels, and complex advertising strategies.

To perform MMM effectively, you need historical data on sales, advertising spend, and other marketing activities.

MMMs are an important tool to provide data-driven insights into the effectiveness of marketing channels.

MMM involves analyzing historical data to quantify the relationships between marketing activities and business outcomes.

An MMM can help determine the optimal allocation of the marketing budget across and within channels.