Understand your journey effort score
Journey Management displays a summary of your effort score on the on the left side navigation. It shows the score, its trend, and the contributing factors for the past 30 days, along with an interactive breakdown of how each factor contributes to the score.
- The score – The main metric that shows your effort score for the selected time period (30 days by default), plotted against your organization’s Very Low and Very High values. This helps you understand where your average falls. When a prior period is available for comparison, the summary also shows whether the score rose or fell relative to it. The range and the score are specific to your organization. The range tells you about your customer behavior pattern and the score helps you understand improvement areas. Together, they create actionable insight
- The trend – A trend chart plots the average effort score day by day across the selected time period.
- Effort score contributors – The contributors view breaks down the effort score into its four factors, each shown as a percentage of the overall score. Select one or more contributors to see exactly how many points they represent. The weighted value reflects your organization’s actual effort model and not just which factor has the largest raw number. For example:
- A score of 16, with switches and transfers as the largest single contributor (13.1 of the 16 points). This single factor can dominate an organization’s score depending on that its own journey patterns.
- A score of 70, with wasted effort and idle time together contributing 50 points, but neither alone a majority. Correcting just one of the factors, for example, shortening every engagement, would not be enough as the combination is critical here.
Understanding effort patterns
Although the effort score as is indicates the customer’s effort, it is better understood when it is interpreted with how tightly customers cluster around that range. Once you know which pattern applies, the effort score contributors explain what is driving it and where to focus. Four patterns to watch for:
| Pattern | What it means |
|---|---|
Low average, tight range | A consistently positive experience. Most journeys require little effort, and there is minimal difference between the best and worst cases. This state should be maintained rather than adjusted. |
High average, tight range | A widespread issue rather than an isolated one. Effort is high, and customers are experiencing a similarly high level of difficulty. This is the most urgent pattern to address, since resolving the primary cause improves the experience for nearly all customers. |
Low average, wide range | A strong experience with a notable exception. Most customers have a low-effort experience, but a distinct subset experiences significantly higher effort. Although smaller in number, this group is significant and is precisely what the High and Very High segments identify. |
High average, wide range | A largely difficult experience with some exceptions. A meaningful portion of journeys proceeds smoothly, but these represent the exception rather than the norm. Attention should be directed at issues within the typical journey path, not only at the most extreme cases. |
Using segments as a filter
Select a segment to filter the entire view to those customers only, isolating what is driving effort for that group. This is useful for comparing unusually high-effort customers against everyone else, or for finding patterns worth replicating from the unusually low-effort group. The contributors view recalculates to show what drives that segment’s average.
How to read a segment’s driver profile
Compare a segment’s average duration, idle time, incomplete rate, and transfer rate against the same factors in other segments. The following two pointers help:
- Which factor, or combination of factors, is different for this segment vs. all the others, and not the number which is largest in absolute terms.
- How much volume the segment holds. A small segment with extreme values is a rare edge case worth flagging; a large segment with elevated values is a common, operationally important pattern worth addressing directly, even if its numbers are not the most extreme on the page.
Analysis best practices
- A segment tells you what kind of friction is happening and how many customers it affects. Combine the segment filter with the contributors view and the trend chart to move from noticing a pattern to understanding it.
- Narrow the contributors to the segment. Selecting a segment filter recalculates the contributors view for just that segment, showing exactly which factors are driving its points.
- Check whether a pattern is new or ongoing. Filter the trend chart to the same segment and compare across the available time windows — a flat score points to a stable, structural pattern; a climbing score suggests something changed recently.
- Weigh fixes by volume, not just severity. Low and High (each roughly 30% of customers) are systemic patterns — fixing the dominant driver helps a large share of conversations. Very High (roughly 20%) is smaller but more extreme — fixing it protects your worst-case customers rather than moving the overall average.
Use the effort score as a starting point
The effort score is designed to help you perform a deeper analysis.
- Start with your effort score contributors and the segment you want to understand more deeply.
- Build charts around the behavior attributes tied to those contributors.
- If elapsed time or wasted effort is driving the score, check duration indicators and the time between steps, adjusting connector settings as needed.
- If transfers or wasted effort are driving the score, look at terminating nodes, wrap-up codes, and exit reasons within the journey.
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