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Report Builder Best Practices

Last Updated June 5, 2024

Brief Overview

The WalkMe Report Builder enhances data analysis by allowing you to create custom reports tailored to your needs, going beyond the standard Insights Reports. It offers a step-by-step process for report creation, from selecting templates to adding columns and filters, ensuring the data is relevant and actionable.

This document will guide you through the best practices for using the WalkMe Report Builder. By following these practices, you can maximize the effectiveness and efficiency of your report generation.

Best Practices

Getting Full Data Data

  • Data for reports isn't updated in real-time but refreshes daily starting at 9AM UTC. For completeness, it's best to export reports up to the end of the previous day to capture a full day's data

Reporting User Inactivity

  • Reports are designed to provide information on occurred events. If an event didn't occur, it can't be directly reported
  • To figure out how many users didn't perform a certain action, export two separate reports: one listing all users and another detailing users who completed the specific action
    • By comparing these two, you can calculate the difference and find out the number of users who didn't perform the action

Yes / No Dimension

  • When using Yes/No dimensions, we recommend using them as filters rather than as columns in your report
  • Adding these fields as columns can split each record into Yes or No categories, which could flood your report with unnecessary data

Case Sensitivity in Filters

  • Filters are case-sensitive in reports. This means that if you don't enter the name of the item, event, user, or any other detail exactly as it appears in your system, the filter may not give you the results you want
  • If you're unsure of the correct name, it's best to load a preview of the data or access WalkMe Insights directly to retrieve the correct name

Data Retention Period

  • Our primary data retention period is one year. However, “All-Time” user dimensions can display information from more than one year ago
    • Note that just because the data is displayed does not mean it's stored beyond the one year
  • For “All-Time” user data, we maintain the initial record of when a user was first seen. This record sticks with the user as long as they remain active in the system
    • We consider a user inactive if they haven't shown any activity for six consecutive months

Empty Fields / Unfamiliar Item Types

  • When creating a report using "Item Name" and "Item Type" dimensions, you may come across empty fields or unfamiliar item types, like "WalkMe" (which indicates that WalkMe was loaded on the page). These records typically appear based on the specific combination of fields chosen for your report
  • To exclude these fields from your report, you can apply filters such as "Item Type is not null" and "Item Type is not WalkMe". These filters will help you to remove any irrelevant or unfamiliar records from your report and ensure that you're only seeing the data you need

Understanding Hierarchical Item Structures

  • Analyzing data for items with hierarchical structures like Smart Walk-Thrus, Surveys, or ActionBots requires understanding their composition
    • These items have parent entities (like Smart Walk-Thru ID/Name, Survey ID/Name, or ActionBot ID/Name) and child entities (like Smart Walk-Thru Step, Survey Question, or ActionBot conversation). We call the overarching entities "Parent/Item" and the detailed elements "Children/Item Components"
    • When creating a report, it's important to include fields related to both parent items and children/item components. However, this can sometimes result in empty rows because certain information is exclusive to the parent item while other data is specific to the children item components
Example

When creating a report that combines fields and measures related to Smart Walk-Thrus and their steps, applying filters specific to Smart Walk-Thru steps may produce misleading data. For instance, the "Walk-Thru Plays" measure could be displayed as zero because the calculation depends on information recorded at the parent item level rather than at the child component level targeted by the filter.

Effective Use of Measure Fields

  • The measure fields in reports quantify the elements indicated by their names. For instance:
    • "Users" counts the number of unique users
    • "Sessions" counts the number of unique sessions
    • "Items" counts the number of unique items
  • Adding these measures to a report that includes dimensions similar to these measures, such as "User ID", "Session ID", or "Item Name", does not add extra value
    • This is because the measures will count only one record at a time, as the report already specifies the details of the user, session, or item
    • These measures become more useful when you aim to aggregate specific data, like calculating the number of sessions a particular user had within a certain timeframe, or determining the number of users who were exposed to or interacted with a specific item, etc.

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