Track your customers’ journeys: Reports for Journey Builder

Track your customers’ journeys: Reports for Journey Builder

The success of an orchestrated journey can be seen through its results: delight through customer experience.

But, to derive the real picture of what went well and what didn’t, where the drop-outs happened, what are the hotspots, the popular route of choice, common routes behind successful closures, you need to dissect the journeys step-by-step.

Journey Builder in CommandCenter comes with a built-in reporting facility. After publishing your journeys, you can view reports specific to those journeys by accessing the journey-based reports.

Configuration-based journey analyses

Journey Builder comes with three analytical components:
  1. Stage-wise segregation provides a split of record concentration for each stage in that journey, for the specified duration.
  2. Average time taken in each stage shows the average time records spend in each stage
  3. Overall record count  shows how many records were there in each stage for the chosen duration. It also shows the number of records that passed through a transition giving insights on the progression.
    
Let’s look at a use case to understand how these analyses can help your business:

Zylker is a consumer electronic retail chain that is best known for flexible orders, delivery, and returns across different purchase channels. They are able to deliver this kind of consistent CX across all of their touchpoints because they connected them with the end-to-end process, managing them from one tracking system, and correcting the course by observing convergence at each stage.

Here’s the process they have created in Journey Builder:
From exploration to sale closure, Zylker can respond with the right actions to customers based on their choices. Now, to monitor the drop-outs, returns, cart abandonment, Zylker can keep a close eye on the turn of events by monitoring records movements across the stages.

Stage-wise segregation

X-axis: Stages; Y-axis: Record count
This component lets you understand in which stage your prospects and customers are stationed at, for the selected duration. By segregating them based on their stages, you can observe the popular preferred stage, see the parity of distribution, and more.
This is a progressive path with each step as stages.
By seeing the number of records in each stage, you can compare between
  1. Online vs store visits
  2. Successful transactions and abandoned cart
  3. Customers’ convenience in waiting for product delivery or pickup at the store.
  4. Number of returns versus replacements
and more.

Average time taken in each stage

X-axis: Stages; Y-axis: Average time taken

This is another chart to understand how long all of your prospects and customers stay in a particular stage on an average. This will give you the amount of time each spends on being on a stage.

Say, for example, if your prospects station on Added to cart for a long time, then it indicates they are contemplating their choice. Zylker can nudge the customer using push notifications or remind them about the item in cart. 
This is a CX  hotspot and identifying them at the right time, will enhance the possibility of conversion.

For another, let’s say, you identify your fulfilled package often remains in the delivery partner assigned stage for long. In which case, you can change your delivery partner or raise a conflict.
This chart to analyze the average time records spent on each stage will help you compare the average progression time and identify latency if any.

Overall record count

This chart gives you how your prospects and customers trickled down through the journey for the selected duration. Based on how they progressed, you can identify the major drop-spots, observe how their choices branched through the journey, understand preferred routes, and see how many completed their journey.
Let’s say by the number of customers opted for store pickup can mean they either feel pickup is easier, or delivery is costly or is often taking time. Likewise, you can also see the ratio of customers end up raising support requests for returns/replacements.

Beyond stage-wise reports: Building deeper analyses

These three native views give you the shape of a journey. In fact, Stage-wise segregation and Average time taken are really preset versions of what you can also build yourself—with more control—using Bar Graph and Heat Map from the analytics module. The moment you want to trend that shape over time, compare it across dimensions, or hold it against a target, that's where the full analytics layer steps in.

Journey Builder carries the same seven chart types available across CommandCenter. But applied here, each one is really asking the same question: what is my orchestrated journey actually delivering?

Let's walk through them, using Zylker's own journey—the one you just saw take shape from Exploration all the way to Sales closed successfully.

Line Graph

A line graph traces performance, growth, progression, and trends of one or more entities across two measures, with a mean line to anchor the average. In Journey Builder, it tells you how long a record took to progress through the stages of a configuration you've orchestrated.

Configuration parts of a line graph

  1. The Journey Builder configuration you'd like to analyze (Add report for)
  1. The data you want to use (Choose data)
  2. The measure you prefer (Measure)
Notes
Note: For a line graph, the data selected is duration, by default.

Example: From order to delivery—assessing the speed of retail transactions

Zylker's journey isn't a straight line—it's got two Wait transitions built right in: a 5-day wait on "Added to cart" before an unresponsive record tips into "Abandoned cart," and another 5-day wait on "Post-delivery review" before the warranty window closes out. A line graph that factors in these Wait states, alongside the Journey deadline, tells Zylker something a simple stage list can't: not just how long delivery took, but how much of that time was active progress versus a customer sitting on the fence.

To build this:
  1. Provide a name for this chart
  2. Choose the Journey Builder configuration to analyze
  3. Verify the data, which is Duration
  4. Select the measure as Maximum, from Maximum, Minimum, and Average. Since Zylker's goal is to understand the longest a record can take before the end stage, Maximum surfaces the outliers worth investigating.
  5. Include Wait transitions and Journey deadline as part of the stages, to capture the full picture

Bar Graph

A bar graph is a comparative study—two or more entities, set against each other, across measures and variables. In Journey Builder, the variables are your orchestrated stages, and records are the customers moving through them.

Example: Understanding fulfillment velocity across stages

Zylker's Average time chart already flagged that "Delivery partner assigned" holds records longer than its neighbors. A bar graph lets Zylker go one step further: selecting Backorder created, Warehouse transfer, Fulfillment, and Delivery partner assigned as base stages, choosing Duration as the data, and Average as the measure—then filtering to just the store-pickup path. Turns out, store-pickup orders clear these stages nearly a day faster than home-delivery ones. That's a filter the preset report can't give you; the bar graph can.

To build a bar chart:
  1. Give a name for the report
  2. In Add report for, select the Journey Builder configuration you'd like to analyze
  3. In Choose base stages, choose the stages you want to analyze
  4. In Choose data field, select the data you want to measure. These points form the x-axis of your chart.
    1. Records displays the count of records at a stage
    2. Duration indicates the time taken for each stage
    3. Numeric field of an app gives you the flexibility to represent percentage, time, or any numeric value
      Note: Only merge fields created for number fields of that process are visible. If you create a merge field now, only records that visit that point of the journey later will reflect it—merge field data isn't applied retroactively to records that have already crossed that config point.
  5. In the measure field, choose Average for the typical value, Minimum for the lowest, or Maximum for the highest
  6. In the group by field, select the stage by which you want to group the analysis
  7. In the Filters field, add filters if required—for example, orders placed via a particular channel, or belonging to a specific segment.

Pivot Report

A Pivot Report lets you look at an orchestrated journey from two angles at once. By setting stages and fields as rows and columns, you examine how a data point shifts at the intersection of two journey attributes—patterns that stay invisible when you look at one dimension alone.

Example: Understanding channel-wise dispute patterns

Zylker wants to know if disputes cluster around a particular entry channel—online browsing versus a store visit. Setting entry channel as the row and closure outcome ("Sales closed successfully" vs. "Sales closed with dispute") as the column, and choosing Count as the operation, Zylker discovers store-visit orders carry a noticeably higher dispute rate. That's a pattern that would've stayed buried in a single stage-wise view.

To build a pivot report:
  1. Give a name for the report
  2. In Add report for, select the Journey Builder configuration you want to analyze
  3. In Select Rows, choose the stage and field for your rows. Add multiple rows for a more granular comparison.
  4. In Select Columns, choose the stage and field for your columns
  5. In Select Data, choose the field you want to analyze
  6. In Operation, choose how to summarize it: Average, Maximum, Minimum, Sum, or Count
  7. In Filters, add filters if required

Heat Map

A heat map reads an orchestrated journey in color. The intensity tells you where records concentrate, and
where a selected value swells or thins across stages—patterns that surface visually before you ever read
a number.

Example: Watching the funnel cool, stage by stage

Zylker's own numbers tell the story starkly: 4,678 records at Exploration, narrowing to 3,548 at "Approached the business," 2,972 at "Order placed," 2,367 at "Backorder created," and just 1,698 by "Return request placed." A heat map of Records/Count lights up hot at Exploration and cools steadily as records move downstream—giving Zylker's team an at-a-glance read on exactly where the journey loses its warmth, without scanning a single number.

To build a heat map:
  1. Give a name for the report
  2. In Add report for, select the Journey Builder configuration you want to analyze
  3. In Choose Data, select Records, Duration, or a numeric field from an app
  4. In Measure, choose how to summarize the data—Count, when Records is selected, for instance
  5. In Color code, choose Warm to Hot or Hot to Warm to define how intensity should progress as the value increases
  6. In Filters, add filters if required

Progression Analysis

A Progression Analysis compares how records move between selected stages of a journey. Because Zylker's journey branches—store visits rejoin the main flow at "Order placed," pickups split off toward "Arrived at store"—a middle stage can genuinely hold more, or fewer, records than a strict top-to-bottom funnel would suggest. The analysis reflects the strength of each stage, not just its position in line.

Example: Understanding how far the journey actually carries customers
Selecting "Exploration" and "Sales closed successfully" as the two stages, choosing Records as the data and Count as the measure, Zylker sees the full arc: 4,678 records enter at Exploration, and a much smaller number exit successfully closed. That gap—read alongside the stage-wise view—tells Zylker exactly how much of its funnel's width survives to the finish line, not just where it exists.

To build a progression analysis:
  1. Give a name for the report
  2. In Add report for, select the Journey Builder configuration you want to analyze
  3. In Stages, select the stages you want to compare. Add multiple stages to analyze progression across more points in the journey.
  4. In Choose Data, select the data you want to measure
  5. In Measure, choose how to summarize the selected data
  6. Repeat the configuration for each stage you want to include

Goal Chart — On reaching a particular stage

A Goal Chart tracks how records progress toward a defined stage goal, over time.

Example: Tracking progress toward a stage goal

Zylker defines "Sales closed successfully" as its goal stage and tracks the number of records reaching it across a selected date range. The chart shows a steady climb through a promotional period—then a dip right after a delivery partner change. That's not just a number on a chart; it's a prompt to look at what shifted.

To build a goal chart:
  1. Give a name for the report
  2. In Add report for, select the Journey Builder configuration you want to analyze
  3. In Define Stage Goals, select the stage you want to track as a goal
  4. Add multiple stage goals if you'd like to track progress toward more than one stage
  5. Target Meter
  6. Target Meter measures progress toward a defined target within an orchestrated journey.

Example: Measuring progress toward a target

Zylker sets a target of 2,000 records reaching "Delivered to customer" this quarter. Selecting Records as
the data, Count as the measure, and 2,000 as the target, the meter currently shows 1,830 achieved—close,
with a visible, actionable gap for the team to close out the quarter against.

To build a target meter:
  1. Give a name for the report
  2. In Add report for, select the Journey Builder configuration you want to analyze
  3. In Choose base stage, select the stage for which you want to set a target
  4. In Choose Data, select the data you want to measure—Records, Duration, or a numeric field from an app
  5. In Measure, choose how to summarize the selected data
  6. In Target Value, enter the value you want to achieve
  7. In Filters, add filters if required
Between the three native views and these seven deeper analyses, Journey Builder gives you the same two altitudes Path Finder does: the map, for reading a journey at a glance, and the measurement, for putting a number—and a target—to what you saw. The difference is what's underneath. Where Path Finder reads paths your customers found on their own, Journey Builder reads paths you designed—so every hotspot, drop-off, and goal here is a mirror held up to your own orchestration.