100%
customer base
coverage
500k
individual tickets
analysed
10k
customers contracted
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.
A look in the future
The model proved to be easily scalable across markets and other business units and is currently implemented within Unitymedia and Virgin Media UK. In 2020 the project will be expanded to Virgin Media Ireland.
Its predictive power can be used to predict satisfaction scores not only for the extremely unhappy, but also for the customers who are content. So we are currently developing a “Refer a friend or family” programme for Loyal Customers.
Project Trivia:
About Company: OEM for Hospitals
Industry: MedTech
Company Size: 10 001+ employees
Location: Global
GemSeek Capabilities: Competitive NPS
