Underlined works together with the VU AI-LAB
Underlined works together
Underlined works together with the VU AI-LAB
Underlined, together with Prof. dr. Frans Feldberg of the VU University Amsterdam worked intensely on the Artificial Intelligence (AI) LAB, part of the master’s program Digital Business & Innovation of the School of Business and Economics.
The assignment
Together we want to create a tool for HR managers, helping them to gain insight into bottlenecks that limit high employee satisfaction. Numerous studies have shown that higher employee satisfaction not only leads to higher productivity and quality, but also leads to a higher customer satisfaction. The relationship between employee satisfaction is quite simple: satisfied employees, are highly motivated, have excellent work morale and work more effectively. Together with higher productivity and quality, employee satisfaction has a direct influence on the process quality.
“Each graduation project combines the two unique aspects: data science and entrepreneurship, meaning the graduation project is related to data, supported by a dataset, and has business or societal impact.”
Bottlenecks in internal processes
The study shows there are several reasons why employees become discouraged from their jobs, such as high levels of stress, lack of internal communication within the company, lack of recognition, or no opportunity for growth at all. To maintain high customer satisfaction, managers must take an active role in identifying these factors, which we refer to as the “bottlenecks in internal processes.”
Impact on employee satisfaction and customer satisfaction
Underlined specializes in using data-driven insights to improve customer satisfaction, wants to know how existing services can be used to improve HR-related issues. To gain an overview of the bottlenecks in employee work processes and which work processes influence customer satisfaction. These bottlenecks in the work processes are still ‘hidden’ for companies.
HAPP-AI
The new data-driven business model innovation provides infrastructure insights for employers, by taking both the customer side (NPS) and the employee side (eNPS). It provides insight for both employers and employees and identifies the bottlenecks caused by employee dissatisfaction with their work. It also identifies areas where customers think the service and products are missing and sees if there is a correlation with the employee’s input. Making the human side of all companies more transparent.
In addition, the user of the tool can identify best practices and bottlenecks. It shows which elements of employee experience drive customer advocacy and provides insight into how to improve employee experiences. When the employee experience improves, employees will be more engaged and satisfied with their work, which will lead to increased productivity, better quality of service and, as a result, customers will also become more loyal.
The Underlined team of students pitched their data-driven business model innovation to a jury according to the Dragons’ Den format. Each jury member received an amount to invest in the business models that were pitched. The team managed to collect almost 1 million virtual coins from the jury and thus successfully complete the assignment.
Special thanks to:
Team 8 AI-Lab VU: Sanne, Colin, Zita, Aïmane & Lieke
and prof. dr. Frans Feldberg, Full Professor of Data-Driven Business Innovation
We would like to thank the students very much and will do further research. Our compliments to the students, we wish you every success with your study career!
Student’s Journey at Underlined
Making a lasting impact
Students have an active role in the application and elaboration of our (product) development in the field of CX, data and text mining. Developing text mining models, optimizing text mining algorithms, performing quality checks on models and algorithms, and testing our API products.
Step-by-step Journey
- Doing literature research to become an expert,
- Training, tutoring and guidance from professionals in the field,
- Applying techniques on actual customer datasets to put knowledge into practice,
- Comparing techniques and providing advise to improve business,
- Implementing techniques to make a lasting impact.
The research collection
Sharing knowledge
Underlined makes theses accessible online worldwide. In the catalogue you can find (older) student theses of which a digital version is added to the collection.
- Welke modeltechniek kan het beste gebruikt worden om de belangrijkste drivers te vinden van de Net Promoter Score?
Author: Stance Lammers, Publication: 2021 - Welke supervised machine learning techniek kan het beste worden gebruikt voor een optimale classificatie van CX data?
Author: Fenna Blom, Publication: 2021 - Predicting the occurrence of complaints within the customer journey based on process mining techniques
Author: Jasper Nooyen, Publication: 2020 - Masterthesis Consumers’ Brand Image and Positioning Perceptions on Social Media
Author: Gerdien Ridderbos, Publication: 2015 - Masterthesis Sponsoring via Social Media
Author: Bart Smarius, Publication: 2012 - Masterthesis Social Sentiment als voorspeller van NPS
Author: Hanneke van Keep, Publication: 2012
What's happening?
/report 30 June 2022
Forrester Report: CX Metrics Essentials
This report answers 10 key questions that CX leaders need to answer in order to better be able to define and measure CX metrics.
Download
/webinar 30 June 2022
With Forrester’s Leading Expert On CX Measurement
Learn how leading companies continuously optimize and measure their most beneficial CX improvements.
Watch the after movie
/e-book 11 Mar 2022
Customer Journey Mining:
An Overview
Data scientist Henrik Nijkamp and analytics consultant Milou Ehrismann help you understand the impact of journey mining.
Download
