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T-Mobile Closer to the Customer 2019

Descend
  • Overview
  • Participants (75)
  • Rules
  • Project gallery
  • Updates
  • Discussions
Connect with the participants – support your favorite projects by liking, sharing, and commenting on them.
T-Support
T-Support

Supporting T-Mobile's Representatives to Support Customers

Winner Winner
Varisa Gumpangkum Jinseo Park Abdulrahman Boksmati Ethan Wang + 1
4 0
T-Gist
T-Gist

Elegantly convey consumer relationship & interaction through a beautiful UI, semantic analysis, and token-summarizing

Winner Winner
Athreya Anand Rishov Sarkar Benjamin Holmes
1 0
T-Mobile T.E.R.R.A.
T-Mobile T.E.R.R.A.

T-Mobile's Empathy Recognition Relay Assistant

Winner Winner
Erika Nguyen Xavier Jo
1 0
"Queue"-Learning: An Adaptive Queueing Workflow
"Queue"-Learning: An Adaptive Queueing Workflow

Crowded stores often make it difficult for company representatives to promptly attend to customers - we fix that.

Kyle Stachowicz Will Gulian Ananth Dandibhotla Naman Bhargava
0 0
Telly
Telly

Telling Customer's Story through Data

Techie Teee
0 0
Torus
Torus

Snap nutrition details and get enlightened

Derrick Wilson-Duncan
0 0
RepPrep
RepPrep

Provides representatives with an system which generates the best recommendations for the customer given their profile

Eric Phan Sanders Lee
0 0
Prediction of Customers' Carrier Choice of BTS
Prediction of Customers' Carrier Choice of BTS

Customers are always important and we try to understand their choices on carrier

BennyWnj llxxll12345
0 0
MagentaGo
MagentaGo

Understanding customers by tracking their foot patterns to enable business intelligence and empowering sales reps

Chaitya Shah Nicholas Petosa Ethan Tien
1 0
T-Mobile Super-Rep Insights
T-Mobile Super-Rep Insights

This project will help T-Mobile reps gain faster insights into what their consumers want and need.

Jeff Jacob Joshua Philipose Thomas Thachil Sachin Konan
1 0
T-Boba
T-Boba

We made a prototype application that can be run on sample mobile devices to collect valuable user interaction data.

Luwei Ge Yiran Hu Jason Dark
3 0
Churn-Burn
Churn-Burn

Predictive classifying factors contributing to customer churn in near realtime window.

Muntaser Syed Christopher Wille + 2
0 0
Zing
Zing

An application to facilitate and improve the retail experience for T-mobile customers and employees

Donald Sangweme Leah Jean-Francois Joao Pegas Mike Fanor
0 0
TeleScribe
TeleScribe

AI powered problem solver for common mobile phone issues.

Peter Tarsoly Christopher Wille Jam Mirzakhalov Bader Taher + 1
0 0
EmpowerT
EmpowerT

Empower T is an application that would help solve the problem of staffing rates and labor at T-Mobile stores.

Grace Fejokwu bwold001 Karen Bennett Khalil Thomas-Kirkman
0 0
The Un-Churner
The Un-Churner

The Un-Churner for the Un-Carrier. Machine Learning to fight customer churn and increase retention.

Kelley Sheffield Greg Howlett Anum Bhamani Paul Kim + 1
0 0
Visual Voicemail 2.0
Visual Voicemail 2.0

Voicemail caught up to speed

Ishan Arya Rahul Bhethanabotla Vince Li
0 0
T-Mobile One
T-Mobile One

One plan, One number, One carrier. T-Mobile

Nathan Binkley Ayush Petigara Christopher Lambert
0 0
Cosai
Cosai

A Complete Store Experience Enhancement AI

Jason Tu Michael Fan
0 0
FaceIO
FaceIO

Help your store run smoothly with the latest face recognition technology and get your customers what they really want

Daneolog Davi Nakajima An Gabriel Nakajima An Matias Szylkowski
1 0
T-Mobile Customer Satisfaction Predictor
T-Mobile Customer Satisfaction Predictor

An ML algorithm that holistically determines a customer's sadtisfaction and likelihood of visiting a retail store

Curtis Welter
0 0
tmobilenear.me
tmobilenear.me

An intelligent app to shape traffic into T-Mobile stores by using machine learning & location services

Thomas Backes vincent hu samatcarnow
0 0

1 – 22 of 22

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