Qualitative Jump in Customer Experience: Omnichannel Impact of Distributional Semantics

Projektinformationen

SentiSquareCX

ID Finanzhilfevereinbarung: 868697

DOI

10.3030/868697

Projekt abgeschlossen

EK-Unterschriftsdatum 15 Mai 2019

Startdatum 1 Juni 2019

Enddatum 30 November 2019

Finanziert unter

• INDUSTRIAL LEADERSHIP - Innovation In SMEs

Gesamtkosten

€ 71 429,00

EU-Beitrag

€ 50 000,00

Koordiniert durch

SENTISQUARE SRO
Czechia

Periodic Reporting for period 1 - SentiSquareCX (Qualitative Jump in Customer Experience: Omnichannel Impact of Distributional Semantics)

Berichtszeitraum: 2019-06-01 bis 2019-11-30

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

"In a world where customer experience (CX) is becoming the #1 differentiator, the ability to monitor and manage inbound customer communications is crucial to commercial success. That is requires the deployment of Natural Language Processing (NLP) to understand, classify, direct, monitor, and respond to customer queries. SentiSquare helps contact centres deal with the increasing traffic by boosting and automating processes in channels like voice (transcribed), chat, email, tickets, and more. SentiSquare uses machine learning and distributional semantics to provide analysis of any and all customer-generated text, no matter the language. The objective of SentiSquare is to scale up and deliver the product to new markets and industries."

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

During the duration of this project, we made considerable progress towards scaling up, and several key achievements have been accomplished. To achieve good market fit, we have validated the competitiveness of our products with several large clients in telco, finance, and utilities. We have also deployed a brand new solution assessing the quality of agent communication with clients.We have also developed our ability to classify phone call transcripts, completed a chatbot support pilot project, and upgraded our Analytics user interface. On top of that, we have formulated a strategy to move towards a Software-as-a-Service model which will allow us to expand faster through speeding up the sales and implementation process.

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

To extend our technological edge, we are developing cross-lingual learning, which removes the need to build models for each language, as well as Fuzzy Classification, which builds an inferred semantic model based on little input from the client, allowing our products to be deployed immediately, without the client’s data. The combination of these new technologies has the potential to deliver the benefits of Natural Language Processing languages in which the use of the technology has been hitherto limited due the shortage data. In the European context, that means that smaller economies will be able to benefit economically at the same rate as the larger ones with access to state-of-the-art NLP for large languages. Through the streamlining of customer care processes, SentiSquare’s technology can also contribute to the increase of productivity as well as profitability of companies.

Symbolic visualisation of customer generated data into actionable elements