User-Centric Data in Mapp Intelligence: Insights and Segmentation
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    User-Centric Data in Mapp Intelligence: Insights and Segmentation

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    Article summary

    1 Overview of User-Centric Data

    User-centric data includes various metrics and dimensions that provide details on:

    • Order Behavior: Total orders per user, average order value, and total discounts.

    • Visit Frequency: Total visits per user and engagement metrics like page impressions.

    These insights are essential for both segmentation in analytics and for creating target groups in Mapp Engage.


    2 Data Insights with Frequency Calculations: Perspective Examples

    For an analyst familiar with standard metrics like page impressions and visits, the user-centric view may initially seem challenging. In this view, we assess data from multiple perspectives to provide a more holistic understanding of each user’s engagement.

    Example: Understanding User Visit from Multiple Perspectives

    Let’s look at a user’s interactions over three separate visits to illustrate the different perspectives available through user-centric data. Suppose a user visits the website on three different days.

    • Session View

      Metrics in the Session View track each individual visit’s cumulative count up to that point. This means that each visit reflects the user’s total visits to date, showing a sequential tally of interactions across multiple visits. For instance, if a user has visited three times, each visit record shows the cumulative count (e.g., first visit = 1, second visit = 2, third visit = 3)

      Identification

      Metrics and Dimensions using the Session View do not include “Profile” in their titles (e.g., “User - Visits” or “User - Page Impressions”). Refer to the metric overview below for a complete list.

    • Profile View:

      The Profile View uses the same cumulative logic as the Session View but focuses on the user’s most current state. Instead of associating cumulative counts with individual visits, this view always presents the latest cumulative data for the user, regardless of whether the last visit falls within the analysis period. This provides an up-to-date snapshot of the user’s entire engagement history at any given time.

      Identification

      Metrics and Dimensions in the Profile View include “Profile” in their names (e.g., “User Profile - Visits” or “User Profile - Page Impressions”). Refer to the metric overview below for a full list.

    • Regular Metric View:

      Standard metrics, like “Visits,” record the total number of interactions within the defined analysis period. Here, you might see how often a user interacted within a specific timeframe, providing a period-based summary.

    Each of these perspectives serves a distinct purpose. The Session View gives a visit-by-visit progression, the Profile View captures the user’s latest cumulative state, and the Regular Metric View summarizes activity over a defined period. Together, they help analysts understand user behavior both sequentially and in aggregate.


    3 Understanding Visit vs. Order Intervals: Calculation Examples

    Intervals in Mapp Intelligence provide insights into the time gaps between consecutive visits or orders, helping analysts understand engagement frequency and timing patterns. By using filters, analysts can further refine these intervals, limiting them to specific events or visits. For more on applying filters, see the Use Cases section below.

    Good to Know for Visit and Order Intervals

    • Date-Based Calculation: Intervals are calculated based solely on the date, not the specific time of day. For example, a user could visit the website at 11 p.m. and then again the next day at 5 p.m., which would be counted as a one-day interval, even though fewer than 24 hours have passed.

    • Handling of First Interactions:

      • For Visits: The first visit is excluded from interval calculations, with intervals starting from the second visit onward.

      • For Orders: For the first-ever order, the interval represents the number of days since the user’s first visit, rather than since a prior order.

    Calculating Visit Intervals for a User

    To illustrate interval calculations, let’s consider a user who visits the website on three separate days:

    For this user, Mapp Intelligence calculates the interval between each visit:

    • Visit Interval “A”: For the second visit, the last contact has been one day ago

    • Visit Interval “B”: For the third visit, the last contact has been two days ago

    Note: For a deeper analysis of user engagement over time, Mapp Intelligence provides cohort analysis tools to examine intervals in days, weeks, or months since a user’s first visit. For more details on setting up and interpreting cohort data, refer to our article on Cohort Analysis in Mapp Intelligence.

    Calculating Order Intervals for a User

    If the user places orders during these visits, Mapp Intelligence also calculates the time between consecutive orders. Suppose the user makes purchases on their first and third visits, with the first purchase also being their first-ever order and their first-ever visit:

    For this user, Mapp Intelligence calculates the interval between each order:

    • Order Interval “A”: For the first order, the days between orders is 0, calculated from the date of the first visit, since it’s the first-ever order.

    • Order Interval “B”: The number of days between the first order (on the first visit) and the second order (on the third visit) is 4 days.


    4 Overview of Dimensions and Metrics

    Mapp Intelligence offers a variety of user-centric dimensions and metrics, allowing analysts to track specific aspects of user behavior over time. The following table provides an overview of the available dimensions and metrics in Mapp Intelligence, offering valuable insights for both segmentation and engagement analysis:

    Traffic-Related Metrics and Dimensions




    Available as

    Metric/value

    Designation

    Description

    Metric

    Dimension

    Page Impressions

    User - Page Impressions

    Total page impressions to date in intervals of ten


    X

    User Profile - Page Impressions

    Total page impressions
     (Dimension: intervals of ten)

    X

    X

    Visits

    User – 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

    User – Last Visit

    Shows whether something occurred within a visitor’s last visit


    X

    Order-Related Metrics and Dimensions




    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
     (Dimension: intervals of ten)

    X

    X

    Order value

    User – Order Value

    Total order value to date


    X

    User Profile - Order Value

    Total order value
     (Dimension: intervals of ten)

    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
     (Dimension: intervals of ten)

    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 Micro Customer Journey

    Allows restricting on the last cycle of the Customer Micro Status stretching across multiple visits.


    X

    Note on Metrics and Dimensions

    In Mapp Intelligence, some data points are available as both metrics and dimensions to support distinct use cases:

    • Metrics are used primarily for quantitative analysis. They allow analysts to measure the count, sum, or average of specific user actions, such as the total number of visits or orders placed. Metrics are best suited for reporting aggregate numbers and tracking key performance indicators (KPIs).

    • Dimensions provide categorical context for these actions, enabling analysts to segment data for deeper analysis. For instance, using Page Impressions as a dimension lets you view and filter impressions based on specific attributes like the user’s profile, traffic sources, or other visit details.

    Access to both a metric and a dimension for the same data point enables flexible, targeted analyses. For example, analysts can use Page Impressions as a metric to measure total impressions over time, or as a dimension to drill down into impressions by specific user segments or campaigns.

    For additional details on how inactive user data is handled, see our FAQ on Data Cleanup Rules.


    5 User-Centric Analysis with RfM and RfE Models

    Making sense of complex data, such as lifetime and non-lifetime metrics, can be challenging. The RfM (Recency, Frequency, Monetary) and RfE (Recency, Frequency, Engagement) models provide an accessible approach to analyzing user behavior. These models transform intricate data points into actionable insights by offering a structured, pre-defined setup that you can easily adapt to your needs—without requiring deep knowledge of every metric and calculation involved.

    This article provides the foundational data that underpins these models, enabling you to work with a straightforward framework that simplifies your analysis. For a more detailed dive into how these models work in practice, the Mapp Academy offers a course titled “RFM and RFE Model”. In this course, you’ll learn not only about the models themselves but also about typical use cases, group assignment methods, and ways to customize the framework to meet specific business goals. It’s an ideal next step to use these models effectively, saving time and reducing complexity.


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