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Introduction 2 TEST TRANSLATE

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This is the first of two blog posts about my experience with customer engagement and customer experience metrics on global web properties at 3M. This first post is an overview of how we defined a custom 3M.com Engagement Index in Adobe Analytics. In the second post, I will share some surprising insights about the relationship between engagement and customer experience metrics.

 

The theory behind our engagement index is straightforward. We defined specific behaviors that our marketing partners felt were indicators of highly engaged (and assumed to be valuable) visitors. The idea was to use this as a KPI, understand the components of engagement, drive improved engagement over time, understand how different businesses or geographies vary in their engagement, and so on.

 

We were also striving for some mathematical elegance. More specifically, we wanted each individual behavioral dimension to have a comparable metric and be able to combine them into an overall composite metric. To achieve this we created an index measure for each behavioral dimension that was normalized to a [0,1] scale. This allowed us to further average together each dimensional index to create the overall composite metric. In summary, the 3M.com Engagement Index is a composite of six dimensions of visitor engagement normalized to a [0,1] scale.

 

The 3M.com Engagement Index combines six observable behaviors that represent different signals of an engaged visitor. We think about engagement as a concept related to, but not the same as, conversion. Engagement, conversion, and satisfaction are interrelated but also independent.

Retail Analogy
One way to think about these concepts is to consider a typical retail store environment. Imagine watching people in a store as they are shopping. There are many different behaviors that you might observe which might indicate a more or less engaged shopper. These shopping behaviors are different from them actually buying something (conversion) and from how happy they are with the shopping experience (satisfaction).

For the 3M.com Engagement Index, we extended this retail analogy into specific priority behaviors that we measure in Adobe Analytics. The table below is an outline of the behaviors, the digital equivalent, and the index we defined to measure the engagement behavior.

 

​​Less Engaged (-)

​More Engaged (+)

​Digital Equivalent

Behavior ​​Index

​Went to one place in the store

​Walked all over the store

​# Pages Viewed

​Page View Index (P)

​Took a quick glance

​Studied things carefully

​Page Scroll %

​Scroll Depth Index (S)

​Quickly entered and le​ft

​Spent a long time in the store

​Time on Site

​Duration Index (D)

​Stopped in one time

​Returned many times

​Repeat Visit

​Repeat Visit Index (R)

​Looked only at marketing and way-finding signs

​Studied products, picked up products, read the packaging

​Product Detail Page View

​Catalog Detail Index (C)

​Did not talk to anyone

​Asked questions, had conversation with a sales person

​Form Submit, Online Chat, Mobile Call

​Interaction Index (I)

 

Behavior Index Calculations
In order to allow the engagement index to be both normalized and "tuned", each element of the index is defined by using a threshold value. For example, if we determined that any visit with more than 5 page views represents an engaged visit, we set the threshold for the Page View Index at 5 pages. For each behavioral element of the index, we defined the behavioral index as the % of visits that exceed the threshold:

[Behavior] Index = % Visits where the index measurement > [Behavior] Threshold

 

customer experience metrics on global web properties at 3M. This first post is an overview of how we defined a custom 3M.com Engagement Index in Adobe Analytics. In the second post, I will share some surprising insights about the relationship between engagement and customer experience metrics.

 

The theory behind our engagement index is straightforward. We defined specific behaviors that our marketing partners felt were indicators of highly engaged (and assumed to be valuable) visitors. The idea was to use this as a KPI, understand the components of engagement, drive improved engagement over time, understand how different businesses or geographies vary in their engagement, and so on.

 

We were also striving for some mathematical elegance. More specifically, we wanted each individual behavioral dimension to have a comparable metric and be able to combine them into an overall composite metric. To achieve this we created an index measure for each behavioral dimension that was normalized to a [0,1] scale. This allowed us to further average together each dimensional index to create the overall composite metric. In summary, the 3M.com Engagement Index is a composite of six dimensions of visitor engagement normalized to a [0,1] scale.

 

The 3M.com Engagement Index combines six observable behaviors that represent different signals of an engaged visitor. We think about engagement as a concept related to, but not the same as, conversion. Engagement, conversion, and satisfaction are interrelated but also independent.

Retail Analogy
One way to think about these concepts is to consider a typical retail store environment. Imagine watching people in a store as they are shopping. There are many different behaviors that you might observe which might indicate a more or less engaged shopper. These shopping behaviors are different from them actually buying something (conversion) and from how happy they are with the shopping experience (satisfaction).

For the 3M.com Engagement Index, we extended this retail analogy into specific priority behaviors that we measure in Adobe Analytics. The table below is an outline of the behaviors, the digital equivalent, and the index we defined to measure the engagement behavior.

 

​​Less Engaged (-)

​More Engaged (+)

​Digital Equivalent

Behavior ​​Index

​Went to one place in the store

​Walked all over the store

​# Pages Viewed

​Page View Index (P)

​Took a quick glance

​Studied things carefully

​Page Scroll %

​Scroll Depth Index (S)

​Quickly entered and le​ft

​Spent a long time in the store

​Time on Site

​Duration Index (D)

​Stopped in one time

​Returned many times

​Repeat Visit

​Repeat Visit Index (R)

​Looked only at marketing and way-finding signs

​Studied products, picked up products, read the packaging

​Product Detail Page View

​Catalog Detail Index (C)

​Did not talk to anyone

​Asked questions, had conversation with a sales person

​Form Submit, Online Chat, Mobile Call

​Interaction Index (I)

 

Behavior Index Calculations
In order to allow the engagement index to be both normalized and "tuned", each element of the index is defined by using a threshold value. For example, if we determined that any visit with more than 5 page views represents an engaged visit, we set the threshold for the Page View Index at 5 pages. For each behavioral element of the index, we defined the behavioral index as the % of visits that exceed the threshold:

[Behavior] Index = % Visits where the index measurement > [Behavior] Threshold

 

customer experience metrics on global web properties at 3M. This first post is an overview of how we defined a custom 3M.com Engagement Index in Adobe Analytics. In the second post, I will share some surprising insights about the relationship between engagement and customer experience metrics.

 

The theory behind our engagement index is straightforward. We defined specific behaviors that our marketing partners felt were indicators of highly engaged (and assumed to be valuable) visitors. The idea was to use this as a KPI, understand the components of engagement, drive improved engagement over time, understand how different businesses or geographies vary in their engagement, and so on.

 

We were also striving for some mathematical elegance. More specifically, we wanted each individual behavioral dimension to have a comparable metric and be able to combine them into an overall composite metric. To achieve this we created an index measure for each behavioral dimension that was normalized to a [0,1] scale. This allowed us to further average together each dimensional index to create the overall composite metric. In summary, the 3M.com Engagement Index is a composite of six dimensions of visitor engagement normalized to a [0,1] scale.

 

The 3M.com Engagement Index combines six observable behaviors that represent different signals of an engaged visitor. We think about engagement as a concept related to, but not the same as, conversion. Engagement, conversion, and satisfaction are interrelated but also independent.

Retail Analogy
One way to think about these concepts is to consider a typical retail store environment. Imagine watching people in a store as they are shopping. There are many different behaviors that you might observe which might indicate a more or less engaged shopper. These shopping behaviors are different from them actually buying something (conversion) and from how happy they are with the shopping experience (satisfaction).

For the 3M.com Engagement Index, we extended this retail analogy into specific priority behaviors that we measure in Adobe Analytics. The table below is an outline of the behaviors, the digital equivalent, and the index we defined to measure the engagement behavior.

 

​​Less Engaged (-)

​More Engaged (+)

​Digital Equivalent

Behavior ​​Index

​Went to one place in the store

​Walked all over the store

​# Pages Viewed

​Page View Index (P)

​Took a quick glance

​Studied things carefully

​Page Scroll %

​Scroll Depth Index (S)

​Quickly entered and le​ft

​Spent a long time in the store

​Time on Site

​Duration Index (D)

​Stopped in one time

​Returned many times

​Repeat Visit

​Repeat Visit Index (R)

​Looked only at marketing and way-finding signs

​Studied products, picked up products, read the packaging

​Product Detail Page View

​Catalog Detail Index (C)

​Did not talk to anyone

​Asked questions, had conversation with a sales person

​Form Submit, Online Chat, Mobile Call

​Interaction Index (I)

 

Behavior Index Calculations
In order to allow the engagement index to be both normalized and "tuned", each element of the index is defined by using a threshold value. For example, if we determined that any visit with more than 5 page views represents an engaged visit, we set the threshold for the Page View Index at 5 pages. For each behavioral element of the index, we defined the behavioral index as the % of visits that exceed the threshold:

[Behavior] Index = % Visits where the index measurement > [Behavior] Threshold

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