Analyses 7 - Extended user-centric analyses
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Analyses 7 - Extended user-centric analyses
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This training chapter provides an overview of the analysis of user-centric data which is tracked by Mapp Intelligence.
The names of the "URM" dimensions and metrics have been slightly adapted and are outdated in this article. Generally, "URM - Customer" has been changed to "User -", and "URM - Customer Profile" has been changed to "User Profile -". Please find the full list here. We'll update the names soon.
1 User-centric data
1.1 Calculation of frequencies
Which information can be generated by a user?
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For the example of frequencies of visits the following information could be relevant:
What was the number of the visit at a date (e.g. of the first order)?
How many visits did a user make in total?
How many visits did a user make in the analysis time period?
Besides, the regular metric customer and customer profile views are differentiated:
Customer: The value a user had at a specific date.
Customer Profile: Focusses on the current status of the users (until his last visit). For doing so, the last visit does not necessarily have to be within the analysis time period
[Regular]: How often did the value occur in the analysis time period?
Example: "Customer Visits" vs. "Customer Profile Visits" vs. "Visits"
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Even though the last visit was not made within the analysis time period, "Customer Profile" shows the value of the last visit.
Example:
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Customer profile views are not suitable for a time-based analysis. |
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1.2 Calculation of intervals
Mapp Intelligence calculates intervals of visits and orders.
Calculation of visit intervals
For each visit, the number of days that passed since the previous visit is calculated.
The days are calculated based on the date, the time is not taken into account.
The first visit is not considered for calculation.
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By using cohorts, Mapp Intelligence allows one to analyze the days/weeks/months since the first visits (e.g., "Cohorts (Lifespan Weeks)"). |
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By using the filter engine, the analysis can be limited to specific events/visits.
Examples: Calculation days between contacts with filtering
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How many days prior to the last order users access the website?
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How many days prior to the last visit users access the website?
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Calculation of order intervals
For each order, the number of days that passed since the previous order is calculated.
The days are calculated based on the date. The time is not taken into account.
For the first order, the number of days passed since the first visit is depicted.
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1.3 Overview of dimensions and metrics
The following dimensions and metrics are calculated automatically:
Analyzing the Traffic:
Available as | ||||
|---|---|---|---|---|
Metric/value | Designation | Description | Metric | Dimension |
Page Impressions | User Profile - Page Impressions | Total page impressions to date in intervals of ten | X | |
User Profile - Page Impressions | Total page impressions | X | X | |
Visits | User – Customer Visits | Total visits to date | X | |
User Profile - Visits | Total visits | X | X | |
Days | User – Days since Contact | Days since a visit | X | |
User Profile - Days since Last Contact | Days since the last visit to date | X | ||
User Profile - Days since First Contact | Days since the first visit to date | X | ||
User Profile - Visit Frequency Avg. (Days) | Avg number of days between visits | X | ||
User – Days between contacts | Number of days between visits | X | ||
User – Days between contacts Avg. | Avg number of days between visits | X | ||
Yes | No | Shows whether something occurred within a visitor’s last visit | X | ||
Analyzing Orders:
Available as | ||||
|---|---|---|---|---|
Metric/value | Designation | Description | Metric | Dimension |
Orders | User – Orders | Total orders to date | X | |
User Profile - Orders | Total orders | X | X | |
User – Orders w. Discounts | Total number of discounted orders to date in intervals of ten | X | ||
User Profile - Orders w. Discount | Total number of discounted orders | X | X | |
Order value | User – Order Value | Total order value to date | X | |
User Profile - Order Value | Total order value | X | X | |
User Profile - Order Value Avg | Avg total order value | X | ||
Discount value | User - Discount Value | Total discount value to date | X | |
User Profile - Discount Value | Total discount value | X | X | |
Discount rate % | User Profile - Discounts % | Percentage of discounted orders out of total orders | X | |
Customer Lifetime Value | User – Campaign New Visitor CLV | Total order value to date for a new visitor with campaign click | X | X |
Days | User – Days since Order | Days since the previous order (or first visit in case of the first order) | X | |
User Profile - Days since Last Order | Days since the last order | X | ||
User – Days between orders | Days between two orders | X | X | |
User – Days between orders Avg. | X | |||
Yes | No | User – Last Visit with a Purchase | Shows whether an order was placed during the last visit | X | |
User Profile - Last Customer Micro Journey | Allows restricting on the last cycle of the Customer Micro Status stretching across multiple visits. | X | ||
Why is much information available as dimension, even though figures are depicted?
For lots of user-centric data it can be helpful to use it as dimensions, even though figures are depicted. Only by doing so further metrics and formulas can be viewed in this context.
Example of using the dimension "URM – Customer Profile Visits":
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Allows the analysis of the question: "How many users did make only 1 visit in total?"
Example of using the metric "URM – Customer Profile Visits":
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Allows the analysis of the question: "How many visits did users make in their lifetime when they had at least one visit via the channel "Direct"?"
1.4 Automatic cleanup of User data
User-centric data of users who have no login information (i.e., no Customer ID) are cleaned up automatically.
By default, user-centric data is automatically cleaned up if a user has
exactly one visit and exactly one page impression: after 30 days
exactly one visit and more than one page impression: after 180 days
more than one visit: after 360 days
Consequently, users who are registered are not deleted automatically.
Customer ID | User Profile - Visits | User Profile - Page Impressions | Will be deleted when? |
|---|---|---|---|
168456 | 1 | 1 | never |
none | 1 | 2 | after 180 days |
none | 2 | 2 | after 360 days |
none | 1 | 1 | after 30 days |
The number of days after which the single rules will be applied can be adjusted by Mapp. | |||
1.5 Use cases
How many days pass between visits?
Analysis: Individual Analysis
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Reading example | |
|---|---|
Visits | For 1,649 visits the last access happened 4 days before. |
Visit % | For 3.16 % of all visits, the last access happened 4 days before. |
The first visit is not taken into account for calculation. | |
How many days pass between visits, depending on the number of visits?
Analysis: Individual Analysis
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Reading example | |
|---|---|
Visitors | 1,360 users made their second visit one day after their first visit. |
Visits % | 15.74 % of all users with a second visit made it one day after their first visit. |
How many days pass between orders?
Analysis: Individual Analysis
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Reading example | |
|---|---|
Qty Orders | For 11 orders the last order was made 2 days before. |
For the first order, the days that passed since the first visit are depicted. | |
How many days pass between orders, when users purchased at least twice?
Analysis: Individual Analysis
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Reading example | |
|---|---|
Visitors | 138 visitors made their second order on the same day as their first order. |
How many users had more than 10 visits in the analysis period and generated more than 100 Page Impressions until this point in their lifetime?
Analysis: Individual Analysis
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Reading example | |
|---|---|
Visitors | 79,651 visitors accessed the website in March. |
Visitors > 10 visits and > 100 PI in lifetime * | 1,317 visitors accessed the website in March, had more than 10 visits in the analysis period, and until then generated more than 100 Page Impressions. |
*This is a custom metric
Used filters for the custom metric:
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What revenue was generated in the long-term via new visitors per campaign channel?
Analysis: Marketing > Campaign Categories > [Name of the campaign channel]
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Reading example | |
|---|---|
URM – Campaign New Visitor CLV | 18,057.10 € order value was generated by the new visitors tracked in the analysis time period via the channel "Display" up to the current date. |
Order Value | 5.364,10 € order value was assigned to the channel "Display" according to the default attribution model. |
Example: Visitor with campaign channels and default attribution last-campaign-wins
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How many visits are made long-term by new visitors via campaign channels?
Analysis: Marketing > Campaign Categories > [Name of the campaign channel]
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Reading example | |
|---|---|
Visits | The channel "SEO" was used in 34,059 times by new visitors in the analysis time period. |
URM – Customer Profile Visits | New visitors generated 88,605 visits up to the current date. |
Further lifetime visits per visitor * | On average 1.7 further visits were generated by new visitors up to the current date. |
*This is a custom formula
"Further lifetime visits per visitor" is configured as follows:
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2 Evaluating Users on the basis of the RFM and RFE model
2.1 General
The RFM and RFE models are a proven scoring system, that can be used for the evaluation of a visitor.
The models focus on the visitor’s behavior in different contexts.
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3 values are assigned for each of these 3 measurement criteria: 1 (bad), 2 (medium) or 3 (good).
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The underlying setup can be configured individually.
Therefore, 3³(=27) combinations are available in each model.
Example: Possible combinations in the RFM model
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The clustering allows to take targeted measures for selected user groups.
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The same visitor can belong to a RFM as well as a RFE group.
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The RFM model solely focuses on the buyer, the RFE model focuses on all visitors.
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2.2 Setup
In the setup, you configure the model’s threshold values.
Mapp Q3 > Configuration > System Configuration > Account
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Changes in the setup affect all tracked users. Thus, you stay flexible and can approach your perfect setup in small steps. |
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The spread of the RFE groups can be viewed in the following analysis:
Analysis: Visitors > URM - User Relationship Management > URM - Customer RFE Group
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Reading example | |
|---|---|
Visits | 21,082 visits were made by visitors, that belonged to the RFE group 211 in the analysis time period. |
This analysis calculates the assignment to the model based on the calendar time period.
Alternatively, the user’s current status can be determined. For doing so, the dimensions “URM - Customer Profile RFE Group” or “URM - Customer Profile RFM Group” can be used.
Please note, that you choose a meaningful time period in the analysis. If you, for example, choose "Today" in the calendar, all users will have a Recency of "3". |
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Example for the assignment of a user to the "URM - Customer Profile RFM Group"
Configuration:
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Accesses of a user:
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Analysis:
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The challenge of the configuration is to find the “right” model. The setup can vary heavily depending on the industry sector.
Our consultants gladly assist you with finding an individually matching setup!
2.2.1 Example setup for equally distributed groups
To make use of this example, you need to have the right to access to Smart Notifications. If you don't see the bell symbol on top of Mapp Intelligence, please contact your Mapp Intelligence Administrator within your company.
At the notifications in Mapp Intelligence, you can get recommendations for the setup that will give you equally distributed sets of RFM/RFE groups (i.e., all groups will contain the same amount of users).
Activate the Smart Notification for RFE and RFM model.
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Open Feed and select Smart Notifications to see the calculated values.
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Use the values from the Smart Notification in your RFM/RFE Configuration ( Mapp Q3 > Configuration > System Configuration > Account).
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2.2.2 Example setup based on the Pareto principle
By using the Pareto principle visitors can be differentiated: Only the best 20 % of all visitors are business relevant.
Because Mapp Intelligence works with 3 groups, the Pareto principle has to be applied twice. Either the group of "bad" or "good" users can be split up again.
The following example shows the segmentation of users by the number of overall visits to determine the frequency in the RFE model.
Segmentation of all users
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Group 2 corresponds to all business-relevant users.Segmentation of all business-relevant users.
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Group 3 corresponds to the best users.
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Mapp Intelligence can be configured for the example as follows:
In the predefined report "Recency, Frequency and Engagement" in Mapp Q3 click on the arrow symbol near "Frequency, Customer Visits" to open the corresponding analysis.
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The values can be determined using the metric "Visitors cumulated %". Read out the values at 80 % and 96 % and insert them into the configuration mask.
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2.3 Predefined Segments
Based on the RFM/RFE model segments are set up, that depict important user groups.
Example: Configuration of the segment "Flop Buyers"
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In Mapp Intelligence reports, analyses or specific metrics can be filtered by segments.
In Mapp Marketing Automation, segments can be used for defining the target group.
Segments use the profile view. Thus, only the current status of the user is taken into account. |
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Example of predefined RFM segments:
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Segments based on the RFM model
Based on an example configuration the operating principle of the segments is described.
Example configuration for the RFM model:
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Segment name | Filtering | Description based on example configuration |
|---|---|---|
Big Spenders | **3 | More than 1,000 € revenue was generated. |
Churned buyers | 1** | The last order was made more than 90 days ago. |
Flop Buyers | 111 | The last order was made more than 90 days ago. In total less than 3 orders were made and less than 100 € revenue was generated. |
Frequent Buyers | *3* | In total more than 10 orders were made. |
High Potential Buyers | 313 | The last order was made less than 30 days ago. In total less than 3 orders were made and more than 1,000 € revenue was generated. |
Inactive Top Buyers | 133 | The last order was made more than 90 days ago. In total, more than 10 orders were made and more than 1,000 € revenue was generated. |
Small Buyers | 331 | The last order was made less than 30 days ago. In total, more than 10 orders were made and less than 100 € revenue was created. |
Top Buyers | 333 | The last order was made less than 30 days ago. In total, more than 10 orders were made and more than 1,000 € revenue was created. |
Segments based on the RFE model
Based on an example configuration the operating principle of the segments is described.
Example configuration for the RFE model:
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Segment name | Filtering | Description based on example configuration |
|---|---|---|
Churner | 1** | The last visit was made more than 10 days ago. |
Frequent Users | *3* | In total, more than 60 visits were made. |
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