Ascent360 has a revenue attribution model that attributes sales revenue to marketing campaigns such as an email campaign. Our goal is to help our clients understand how much revenue is generated when they run or execute digital marketing campaigns. This will help them understand the value of their marketing actions and ultimately to determine which actions have the most value.
How do we attribute revenue? We do this by reviewing and analyzing data on consumer actions, such as the click of a Facebook Ad or the opening of an email.
Software Edition Information
This article applies to the following software editions:
Revenue Attribution Overview:
At a high level, Ascent360 will attribute revenue based upon the actions of an individual who receives your marketing communications. Imagine if you send an email called “October Newsletter” to a consumer on 10/01/2020. The next day, the consumer may open the email and click on a link in the email to browse your website. A few days later, the consumer may come into your store and purchase an item for $100. Based upon that stream of data, Ascent360 will attribute $100 in sales to the October Newsletter because they opened and clicked on that email beforehand.
Data Used in Revenue Attribution:
Ascent360 uses many sources of data to attribute revenue. These are summarized below:
- Transaction Data: Ascent360 usually is connected to all purchase transaction sources for our clients. This includes in-store point of sale systems, eCommerce systems, lodging reservation systems, property management systems or event booking systems. Each of these systems will tell us what product was purchased (or booked) along with the dates (such as the booking date and the arrival date). Some of the records that come from these systems do not have customer information. In these cases, we cannot attribute the revenue.
- Email Disposition Data: Ascent360 tracks every email that is sent along with every time an individual opens, clicks or unsubscribes from the email. We also track all bounces. If the consumer clicks the email three times, we will track each of these as well as all opens over time.
- Google Analytics Ecommerce Feed: Many of our clients use Google Analytics to track website behavior and attribute eCommerce purchases. This system is good for attributing eCommerce data across many campaigns and digital sources. However, we found that Google has a bias toward attributing their own ads which we attempt to correct for.
A few notes about the data:
- This system can work with or without Google Analytics Data. If a client does not have Google Analytics data then we only use transaction data and email disposition data.
- Our clients may need to set up Google Analytics eCommerce tracking separately. We do not offer the service to set up the Google Tracking. This is not typically a complex process but may end up being complex depending up how our clients have their systems set up.
Process and Rules for Attributing Revenue:
The process for attributing revenue starts with incoming purchase transactions. Let’s assume Ascent360 receives 100 purchase transactions for April 1st 2020.
- Match Transactions to Email Data: Attempt to match the incoming 100 records to email disposition data. We are essentially asking the question “Did the person who just made this purchase transaction get an email from us in the past 15 days? Did they open that email or click on it? (Note, 15 days is our default “Attribution Days”, meaning we look back 15 days to decide if a consumer received a marketing communication.) In this example, let us assume that we match to 25 of the 100 transactions with opens, clicks and delivered emails sent over the prior 15 days.
- Google eCommerce Matching: We will then try to match all 100 incoming transactions to eCommerce tracking data from Google Analytics. In this step, we are asking the question “Did Google Analytics attribute any of these transactions to marketing campaigns from Facebook, AdWords or Search Engine campaign? (or some other digital marketing campaign). In this example, let us assume that 30 transactions match to attribution from Google Analytics.
- Mixed Attribution: Now we know that 25 transactions match to email marketing campaigns and 30 match to Google eCommerce tracking. There will also be some overlap where transactions match to both email and google tracking data. In this example, 10 records match to both (we call this Mixed Attribution). This means that 15 will be assigned to email and 20 will be assigned to Google. The 10 "mixed attribution records" will need further review to determine where the transaction should be Attributed.
- Resolving Mixed Attribution: To determine where to attribute the “Mixed Attribution” records, Ascent360 scans "interaction data" to determine which action occurred closest to the actual purchase date. For example, if the consumer clicked on an email 5 days before a purchase and clicked on a Google AdWords ad one day before that same purchase, we will attribute revenue to the Google AdWords ad.
- Customers, Products and Revenue: Once we have all the transactions attributed, Ascent360 will connect them back to the products that were purchased, the people that bought the products, and the revenue that has been attributed. At this point, we can determine that "Email ABC" drove $15,000 in revenue.
- Build Reports: Finally, all this data will flow back into reports on revenue attribution. We will be able to see the detailed revenue and revenue rolled up to various “plays” and channels. Below are a few examples of what this may look like:
Our system will prioritize clicks over opens over deliveries. Even if an individual received an email yesterday, our system will attribute a click if it happened 5 days ago. Note the example below:
- John Smith buys a $50 shirt on March 31st.
- John received an email on March 30th but did not click or open it.
- John received an email on March 28th and opened it but did not click on it
- John clicked on a Facebook Ad on March 26th
- John received an email on March 25th. He opened it and clicked on it.
In this case, we would attribute the Facebook click which happened six days before the purchase, even though he received an email one day before purchase and he also opened an email four days before purchase. The click still wins as it is the most direct action.
Options and Settings:
There are a few key settings that can be edited based upon the Software Edition that you are subscribed to including the “Days to Attribute” as well as “Attributed Revenue Percent”.
Days to Attribute:
When a consumer takes an action, such as clicking on a Facebook Ad or Opening an email, Ascent360 will (by default) attribute revenue that occurs within the next 15 days. So, if a user clicks on a Facebook ad and makes a purchase 13 days later, Ascent360 will include that revenue in our attribution. If they make the purchase 16 days later, than we will not include the revenue. We believe that 15 days is a reasonable time frame for Attribution. By default, Google Analytics uses 90 days which we believe is too long.
Our standard Days to Attribution are shown below. This can be customized on certain editions of our platform:
Days to Attribute Options
Web Ad Click Through
1 to 90 days
1 to 90 days
1 to 90 days
1 to 90 days
The table above indicates that our platform will look back 15 days by default to attribute revenue for a transaction. As an example, if John Smith buys a shirt for $50 on March 31st, our system will look to see if John clicked on an ad on the internet or if he opened, clicked or had an email delivered from March 16th to the 31st.
Weighted Revenue Percent:
When we attribute revenue, we will automatically show 100% of the attributed revenue in our reports. If the amount of revenue that has been attributed based upon “clicks” is $1,000 we will show that on our reports. However, it some cases, our customers may choose to increase or decrease the amount of revenue shown in our reports. This is only available in some versions of our platform.
- The case for increasing Weighted Revenue Percent: If your business collects consumer information 50% of the time, than it may make sense to increase the weighted revenue percent by 2x. This is because we are probably only showing 50% of the actual attributed revenue since only 50% of the possible transactions are available for revenue attribution. (since there is no consumer data). As an example, if we show that $1000 can be attributed to “clicks” in the month of March, but only 50% of transactions have consumer information, than we can assume that $2,000 in revenue was probably driven by the marketing activities.
- The case for decreasing Weighted Revenue Percent: If most of your revenue is being driven by “delivered” emails than it may make sense to reduce the reported revenue from delivered revenue. As an example, if we are showing that $1,000 in total revenue was attributed to email campaigns in the month of March with $250 was from clicks, $250 was from opens and $500 was from delivered emails than you may choose to reduce the reported revenue from delivered emails from 100% to 50%. This would mean that delivered revenue would be reported as $250 for the month of March.
Weighted Revenue Percent Options
Type of Action
Web Ad Click Through
0% to 200%
0% to 200%
0% to 200%
0% to 200%
Customization of Software Editions:
Days to Attribute