Multi-Channel Attribution (MCA) is a feature that allows you to attribute leads and sales to the various marketing channels they came from. This can be useful for understanding which channels are most effective at generating leads and sales, and for allocating your marketing budget accordingly.
There are several different MCA methods available, each with its own advantages and disadvantages. The most popular MCA methods are last-click attribution (LCA), first-click attribution (FCA), linear attribution (LA), time decay attribution (TDA), position-based attribution (PBA), data-driven attribution (DDA), and rule-based Attribution (RBA).
Last click Attribution gives 100% credit to the last channel the lead interacted with before converting. This is the simplest method of MCA, but it can be inaccurate if the lead interacts with multiple channels prior to conversion.
First click Attribution gives 100% credit to the first channel the lead interacted with regardless of whether or not they converted. This method can be more accurate than LCA, but it doesn’t take into account how many times a lead interacts with a particular channel.
Linear Attribution assigns equal credit to every touchpoint in the customer journey regardless of when it occurred. This method is more accurate than LCA or FCA, but it doesn’t give any extra weight to recent touchpoints like TDA does.
Time Decay Attribution places more importance on touchpoints that occur closer in time to conversion while still giving some credit to earlier touchpoints. This method can help you identify which channels are driving immediate conversions, but it doesn’t necessarily reflect reality since people often research products over an extended period of time before making a purchase decision.
Position Based Attribution assigns 40% credit to both the first and last touchpoints in the customer journey, with the remaining 20% divided evenly among all other touchpoints. This method is more accurate than LCA or FCA, but it doesn’t give any extra weight to recent touchpoints like TDA does.
Data-Driven Attribution uses machine learning algorithms to determine how much credit each touchpoint deserves based on data from your specific campaign. This method can be very accurate, but it requires a large amount of data and is therefore not practical for all businesses.
Rule-Based Attribution allows you to manually assign weights to different channels based on your own business rules. This method gives you complete control over how attribution is calculated, but it can be time-consuming and isn’t always accurate.