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Monday, July 21, 2008 at 5:20:00 PM Website Engagement Models
It has been quite a fight telling clients what type of engagement the customer has with the landing page or website. That is because a wide array of engagement models are available in the market today, and zeroing on a model that would make sense for that particular business is a daunting task.
One model I recently came across is of Eric Peterson, CEO Web Analytics Demystified Inc., has crafted a calculation to measure online engagement. The author of “Web Analytics Demystified” offers a new way for marketers to compile more data on their website visitors. Here are some of his suggestions:
Engagement Calculation
Describing Peterson’s engagement model as a combined metric is an understatement. It takes seven metrics, each one an index that represents an engagement factor. Most of them will be familiar to you. They all depend on the variable “n.”
The “n” is a yardstick you set for each variable to make the calculation relevant to your business. Your “n” values should be different for every index, and every marketer will have different “n” values.
“One of the ways that you could find ‘n’ is you can simply look for the average. So, what is the average click depth for all visitors? … You would set ‘n’ to be that average. [Do the] same for duration, same for recency, same for loyalty,” Peterson says.
Indices: Engagement Factors
Ci: Click Depth Index
Definition: the number of sessions having more than “n” page views divided by the total number of sessions by the user.
This index calculates the percentage of sessions a visitor clicks deeply into your website. How deeply? That depends on where you set “n.” For every session that a visitor’s page-views exceed “n,” their engagement score will increase.
Ri: Recency Index
Definition: the number of sessions having more than “n” page views that occurred in the past “n” weeks divided by the total number of sessions by the user.
The recency index calculates the percentage of sessions that a visitor returns to your website in a set amount of time (n) and views enough pages (n) to be considered engaged. Every time a visitor completes both actions, their engagement score increases.
Di: Duration Index
Definition: the number of sessions longer than “n” minutes divided by the total number of sessions by the user.
The duration index calculates the percentage of a visitor’s sessions that exceed a set time. Each time a person spends more than the set time on your website, their engagement score will increase.
Li: Loyalty Index
Definition: scored as 1 if the user has come to the site more than “n” times during the time frame being considered (otherwise scored as 0).
When people consistently return to your website in a period of time, this measure boosts their engagement score. “I score ‘1’ when visitors have come to [my] site more than five times in the past 12 months,” Peterson says.
Bi: Brand Index
Definition: the number of sessions that either begin directly with your website (have no referring URL) or are initiated by an external search for a branded term, divided by the total number of sessions by the user.
The brand index calculates the percentage of sessions a visitor arrives at your website as the result of a branded action. When a visitor types your URL directly into their browser, or if they arrive on your site after using one of your brand’s search keywords, it will add to their score.
Fi: Feedback Index
Definition: the number of sessions where the visitor gave direct feedback divided by the total number of sessions by the user.
“[This] is the sole qualitative input to this model,” Peterson says. It calculates the percentage of sessions that a visitor provides feedback – like through a Contact Us form, an email to your company or any other way. It adds to their measure of engagement.
->Ii: Interaction Index
Definition: the number of sessions where a user completed one of any specific tracked events (but not full conversion), divided by the total number of sessions by the user.
This index measures the percentage of sessions with which a visitor completes a “mini-conversion” – like subscribing to an email newsletter or downloading content. You can set your mini-conversions however you like.
Quick division
After you get the value for each index, you add them together and divide by 7 – the number of indices. A decimal between 0 and 1 results. Multiply it by 100 to convert to a percentage. This percentage is the engagement score.
The issue being when most marketers look at this score on a web based analytics report; they will not be able to put the figures together. The difference between web based and server based analytics on different vendor supported tools has been discussed earlier. I will try to put a list for you of such vendors as shared by you.
The future of engagement score needs an understanding of WOM, and with offline communications being mapped to web analytics, it will rather give a more authoritative picture.
It has been quite a fight telling clients what type of engagement the customer has with the landing page or website. That is because a wide array of engagement models are available in the market today, and zeroing on a model that would make sense for that particular business is a daunting task.
One model I recently came across is of Eric Peterson, CEO Web Analytics Demystified Inc., has crafted a calculation to measure online engagement. The author of “Web Analytics Demystified” offers a new way for marketers to compile more data on their website visitors. Here are some of his suggestions:
Engagement Calculation
Describing Peterson’s engagement model as a combined metric is an understatement. It takes seven metrics, each one an index that represents an engagement factor. Most of them will be familiar to you. They all depend on the variable “n.”
The “n” is a yardstick you set for each variable to make the calculation relevant to your business. Your “n” values should be different for every index, and every marketer will have different “n” values.
“One of the ways that you could find ‘n’ is you can simply look for the average. So, what is the average click depth for all visitors? … You would set ‘n’ to be that average. [Do the] same for duration, same for recency, same for loyalty,” Peterson says.
Indices: Engagement Factors
Ci: Click Depth Index
Definition: the number of sessions having more than “n” page views divided by the total number of sessions by the user.
This index calculates the percentage of sessions a visitor clicks deeply into your website. How deeply? That depends on where you set “n.” For every session that a visitor’s page-views exceed “n,” their engagement score will increase.
Ri: Recency Index
Definition: the number of sessions having more than “n” page views that occurred in the past “n” weeks divided by the total number of sessions by the user.
The recency index calculates the percentage of sessions that a visitor returns to your website in a set amount of time (n) and views enough pages (n) to be considered engaged. Every time a visitor completes both actions, their engagement score increases.
Di: Duration Index
Definition: the number of sessions longer than “n” minutes divided by the total number of sessions by the user.
The duration index calculates the percentage of a visitor’s sessions that exceed a set time. Each time a person spends more than the set time on your website, their engagement score will increase.
Li: Loyalty Index
Definition: scored as 1 if the user has come to the site more than “n” times during the time frame being considered (otherwise scored as 0).
When people consistently return to your website in a period of time, this measure boosts their engagement score. “I score ‘1’ when visitors have come to [my] site more than five times in the past 12 months,” Peterson says.
Bi: Brand Index
Definition: the number of sessions that either begin directly with your website (have no referring URL) or are initiated by an external search for a branded term, divided by the total number of sessions by the user.
The brand index calculates the percentage of sessions a visitor arrives at your website as the result of a branded action. When a visitor types your URL directly into their browser, or if they arrive on your site after using one of your brand’s search keywords, it will add to their score.
Fi: Feedback Index
Definition: the number of sessions where the visitor gave direct feedback divided by the total number of sessions by the user.
“[This] is the sole qualitative input to this model,” Peterson says. It calculates the percentage of sessions that a visitor provides feedback – like through a Contact Us form, an email to your company or any other way. It adds to their measure of engagement.
->Ii: Interaction Index
Definition: the number of sessions where a user completed one of any specific tracked events (but not full conversion), divided by the total number of sessions by the user.
This index measures the percentage of sessions with which a visitor completes a “mini-conversion” – like subscribing to an email newsletter or downloading content. You can set your mini-conversions however you like.
Quick division
After you get the value for each index, you add them together and divide by 7 – the number of indices. A decimal between 0 and 1 results. Multiply it by 100 to convert to a percentage. This percentage is the engagement score.
The issue being when most marketers look at this score on a web based analytics report; they will not be able to put the figures together. The difference between web based and server based analytics on different vendor supported tools has been discussed earlier. I will try to put a list for you of such vendors as shared by you.
The future of engagement score needs an understanding of WOM, and with offline communications being mapped to web analytics, it will rather give a more authoritative picture.
