Using the NPS correlation application

The Customer Insight for Communication Service Providers solution provides in-depth analytics to help communication service providers evaluate key subscriber metrics.

About this task

This task describes how a typical customer experience team member would use the reports to investigate net promoter score (NPS) and transactional-NPS. The data is linked to the actual activity of subscribers. Subscriber data such as services, devices, or location are linked to their NPS, providing an enhanced view of the network and services.

The screen shots shown are for example purposes and will vary depending on your particular implementation.

Procedure

  1. Open the Behavior Based Customer Insight Top Factors report.Dashboard showing scores for NPS, detractors, passives, promoters, and subscribers. Factors driving T-NPS, such as gender or age, are shown in a bar chart.

    The T-NPS score of -61.02 indicates a high number of unhappy subscribers. This number reflects promoters (approximately 9%) minus detractors (approximately 70%). Factors driving the NPS data are summarized in the bar chart and legend.

  2. Filter the data category based on Data Activity. The top 10 factors change to reflect the Data Activity category. The number of applications used on the UTRAN (3G) network is the most significant. The same dashboard shown in step 1, with the bar chart factors filtered by data activity.
  3. Click the Factor Analysis icon An icon indicating a small line graph, used for the Factor Analysis report. to launch the Factor Analysis page for NUM_APPLICATIONS_UTRAN.Report page showing scores for T-NPS, detractors, passives, promotors, and subscribers, filtered by the specific factor and a threshold.

    With the Factor Analysis threshold set at 3, subscribers who are running more than three applications on UTRAN (a 3G network) have much lower NPS scores than those who are using three or less. With a similar number of subscribers, this analysis is statistically significant. The customer experience team can then use this as a means of further investigation.

  4. Apply further filters to the analysis, setting the data to show only female customers.The same report page as step 3, being filtered additionally by gender.

    With this filter applied, the analysis shows an even greater difference between females who use more than three applications on 3G versus females who use three applications or less on 3G. A customer experience team member might then use this information for further investigation. For example, what type of applications were involved in creating this apparent negative experience for the subscribers in question?

Parent topic: Using Customer Insight for Communication Service Providers