Grant: Applied Research and Development Grants - Level 1
Red River College of Applied Arts, Science and Technology
$25,000.00
Improving Throughput and Customer Experience with Assistive Chat in Retail Sales Environments
EF 2018-19
Résumé
RAPID RTC is a Winnipeg based technology company that develops leading software in vertical markets including automotive, powersports, RV, and agriculture. The company has branch offices in Canada, USA, and Europe delivering solutions to nearly 30 countries in over 20 different languages. Their RAPID ChatTM product incorporates online chat services that connects customer leads with dealership staff through eReceptionists employed by RAPID RTC. This research project will apply Machine Learning (ML) and Natural Language Processing (NLP) techniques to develop an assistive chatbot within an automotive retail sales environment that will work in concert with RAPID RTC's eReceptionists as they handle chats between potential customers and the sales staff at automotive dealerships. One goal of the research is to improve chat throughput by having the chatbot proactively analyze the context of a chat discussion and provide relevant response suggestions to the eReceptionist. A second goal is for the chatbot to learn ""good"" conversation skills and improve overall quality ratings of the end customer experience while chatting.
Programme
Applied Research and Development Grants - Level 1
Emplacement
Manitoba, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
536575-2018
Name nom
Hoemsen, Raymond
Department département
Head Office
Organization id
22155
Institution établissement
Red River College of Applied Arts, Science and Technology
Province en
Manitoba
Province fr
Manitoba
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2018
Competition year année de concours
2018
Award amount
25000
Program id
CARD1
Program name en
Applied Research and Development Grants - Level 1
Program name fr
Subventions de recherche et développement appliquée - niveau 1
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
1561
Committee name en
Applied Research and Development Grants
Committee name fr
Subventions de recherche et développement appliquée
Area of application code
1207
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Information, computer and communication technologies
Area of application fr
Technologie de l'information, des ordinateurs et des réseaux de communications
Research subject code
2801
Research subject group en
Artificial intelligence
Research subject group fr
Intelligence artificielle
Research subject en
Natural language and speech understanding
Research subject fr
Langage naturel et reconnaissance de la parole
Partie
1
Nb partie
1
Application title
Improving Throughput and Customer Experience with Assistive Chat in Retail Sales Environments
Keyword
NSERC
Application summary
RAPID RTC is a Winnipeg based technology company that develops leading software in vertical markets including automotive, powersports, RV, and agriculture. The company has branch offices in Canada, USA, and Europe delivering solutions to nearly 30 countries in over 20 different languages. Their RAPID ChatTM product incorporates online chat services that connects customer leads with dealership staff through eReceptionists employed by RAPID RTC. This research project will apply Machine Learning (ML) and Natural Language Processing (NLP) techniques to develop an assistive chatbot within an automotive retail sales environment that will work in concert with RAPID RTC's eReceptionists as they handle chats between potential customers and the sales staff at automotive dealerships. One goal of the research is to improve chat throughput by having the chatbot proactively analyze the context of a chat discussion and provide relevant response suggestions to the eReceptionist. A second goal is for the chatbot to learn ""good"" conversation skills and improve overall quality ratings of the end customer experience while chatting.
