Marketing attribution data enables you to more effectively measure and improve your marketing campaigns by tracking individuals throughout the campaign cycle. With this data, you can see which marketing approaches work best.
You can collect marketing attribution data in a number of ways. First are online sources like website cookies and ad exposure measures. Second are offline sources like reports from sales agents or loyalty program numbers.
Related data like demographics and location data are also very useful additives, allowing you to reach a more select target market.
Common attributes of marketing attribution data fall into two main categories: explicit and implicit. Explicit data, also sometimes referred to as active data, refers to anything you have to specifically request a lead give you, like their name or email address. Implicit (or passive) data, on the other hand, is any information you can gather without requesting permission, like the amount of time spent on an app, locations where a lead has visited or spent time in, and the amount of time the lead spent outdoors.
In addition, there are several types of marketing attribution models that you can use to analyze your data. These models tend to fall under either Single-Touch or Multi-Touch Attribution models.
Single-touch attribution models may be either first-touch or last-touch models. First-touch models measure the first the first point of contact (touchpoint) with the company. Last-touch attribution models, in contrast, measure the last touchpoint a lead encountered before making a purchase. Both cases have the drawback of not measuring additional touchpoints that convince leads to become customers.
Multi-touch attribution models are more numerous: these models are known as linear, U-shaped, W-shaped, and time decay models. Linear attribution measures each touchpoint equally. U-shaped attribution weighs each touchpoint differently, assigning 40% of the conversion attribution to the first and last touchpoints each. W-shaped attribution assigns 30% each to the first touchpoint, the last touchpoint, and to point somewhere in the middle where a potential lead becomes a genuine lead.
Finally, there is the time decay model which weighs touchpoints more highly the closer they are to the sale.
Marketers use this data to craft and analyze their campaigns and promotions. They and other professionals also use it to develop unique customer segments to market to or develop new products for. They can also measure brand awareness and social media performance.
The best test of the quality of marketing attribution data is the update frequency and consistency of data within the dataset. This data category moves fast, with updates appearing daily, so frequent updates are a must.
It is also good to collect a set of historical data and test that against newer data or against the rate of conversions.
Aside from update frequency, the most important factors to consider when selecting an identity data set is the experience and methodology of the vendor. Does the vendor use a mix of offline and online data? Do they rely more on probabilistic or deterministic data to match devices to owners? What is their match rate percentage? Do they have historical data to compare to past data? Which marketing attribution model do they recommend for you and why?
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