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April 12, 2022April 12, 2022Alexandra-Emily Kokova

Higher NPS® coverage and proactive measures though pNPS

GemSeek

100%

customer base

coverage

500 000

individual tickets

analysed

10 000

customers contacted

proactively

The business problem

Our client, an OEM for hospitals, laboratories and end patients has been running an NPS program for over 10 years. The program had become stagnant, with low response rates and very reactive.

Our task

We were tasked to find ways for our customer to innovate and solve two main pain points: low response rate and lack of proactive actions.

Our Solution

GemSeek deployed its predictive NPS machine-learning model. The model scored the expected NPS after each interaction for all of the customers regardless or not they had answered the NPS survey

Next to NPS & CSAT data, we integrated additional internal variables within the model to increase its accuracy. Namely, complaints, ticketing and monthly ops data of the equipment (e.g. downtime, service requests, maintenance, upgrades, etc.)

The model allowed our client to boost their combined response rate (actual + predicted) to 100%. They could also proactively contact dissatisfied customers even if they did not fill the NPS survey

Project Trivia:

  • About Company: OEM for hospitals
  • Industry: MedTech
  • Company Size: 10 001+ employees
  • Location: Global
  • GemSeek Capabilities: Predictive NPS

Retain, Advance and Grow your customers with predictive customer analytics.

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