What type of model helps a company figure out which marketing actions truly led to a sale?

The type of model that helps a company figure out which marketing actions truly led to a sale is called a marketing attribution model. A marketing attribution model is a framework for assigning credit for a conversion, such as a sale, to various marketing touchpoints a customer encountered before making a purchase. A touchpoint is any interaction a potential customer has with a company's marketing efforts, such as clicking an online advertisement, visiting a website, opening an email, or engaging with a social media post. The purpose of these models is to understand the effectiveness of different marketing channels and activities by mapping out the customer journey, which is the entire path a customer takes from their first interaction with a brand to the point of sale. By assigning credit, companies can optimize their marketing spending and improve their return on investment. Different attribution models distribute credit in various ways. For instance, a first-touch attribution model assigns 100% of the credit for a sale to the very first marketing touchpoint a customer engaged with. An example of this is if a customer first saw a display ad and then later made a purchase through a different channel, the display ad would receive full credit. Conversely, a last-touch attribution model assigns 100% of the credit to the final marketing touchpoint immediately preceding the sale. An example here would be if a customer clicked on an email link and then completed a purchase, the email would get all credit. A linear attribution model distributes credit equally across all touchpoints in the customer journey. If a customer interacted with an ad, then an email, then social media before buying, each of those three touchpoints would receive one-third of the credit. A time decay attribution model gives more credit to touchpoints that occurred closer in time to the actual sale, with less credit given to earlier interactions. For example, a website visit from yesterday would receive more credit than a blog post read a month ago. A U-shaped or position-based attribution model typically assigns a higher percentage of credit, such as 40% each, to both the first and last touchpoints, with the remaining credit, say 20%, distributed evenly among the middle touchpoints. More advanced approaches include data-driven or algorithmic attribution models, which use machine learning and statistical analysis of a company's specific historical data to dynamically assign credit to each touchpoint based on its actual impact and contribution to sales. These models move beyond predefined rules to provide a more accurate, customized understanding of which actions truly drive conversions by analyzing the unique patterns and sequences of customer interactions.