This paper discusses harnessing omnichannel customer feedback data using deep learning, and integrated with NPS survey data to deliver superior customer experience (CX). While there is lot of work done and tool availability in this field for English, there is a lack of algorithms and research done for local languages, such as Bahasa Indonesian. We describe our Quasi Recurrent Neural Network (QRNN) based approach that we used on customer omnichannel feedback for topic modelling, priority prediction and sentiment classification. We further demonstrate the use of our work for multiple clients in Indonesia, in order to set up an advanced CX monitoring and improvement programme (CXM). We showcase our holistic approach to customer experience, integrating omnichannel customer feedback data with NLP-based predictions. We also showcase two cases where we deployed our intelligent CXM tool and set up intelligent conversation monitoring systems to predict customers at the verge of churn-out, beforehand.
"I fully trust the predictions of product pre-testing. It is well-invested money!"In a nutshell, that's the change in perception among CEOs which would delight us.Imagine if you could test products before placing them in your sales channels in such a way that you could precisely predict whether the product would be a flop or not. Imagine if you could gather consumer feedback so precisely that you could understand exactly what consumers like and dislike about your product. And finally, imagine that there is a way to evaluate consumer feedback in a scalable and automated way to generate high pre-testing ROI.But how to do that? We found a solution by leveraging the power of semantic analytics and combining it with classic closed-survey questions to a powerful predictive algorithm that enabled us to avoid millions and change our CEOs' perceptions.
"I fully trust the predictions of product pre-testing. It is well-invested money!"In a nutshell, that's the change in perception among CEOs which would delight us.Imagine if you could test products before placing them in your sales channels in such a way that you could precisely predict whether the product would be a flop or not. Imagine if you could gather consumer feedback so precisely that you could understand exactly what consumers like and dislike about your product. And finally, imagine that there is a way to evaluate consumer feedback in a scalable and automated way to generate high pre-testing ROI.But how to do that? We found a solution by leveraging the power of semantic analytics and combining it with classic closed-survey questions to a powerful predictive algorithm that enabled us to avoid millions and change our CEOs' perceptions.
Using CRM data to harness clients' irritating experiences feedback thanks to machine learning enrich outcomes with a social data deep dive into customers' pain points and related emotions.
Using CRM data to harness clients' irritating experiences feedback thanks to machine learning enrich outcomes with a social data deep dive into customers' pain points and related emotions.
Fusing internal and external consumer feedback creates a realistic picture of business problems. A customised text analytics model takes into account organisational structure and the impact of issues on KPIs to help your clients solve them quickly.
Fusing internal and external consumer feedback creates a realistic picture of business problems. A customised text analytics model takes into account organisational structure and the impact of issues on KPIs to help your clients solve them quickly.
In research we base much of our analysis on consumer articulations but do not pay adequate attention to their silences and what they leave unsaid. Especially in eastern cultures where language is layered and subtle, silence is often used to convey opinions and feelings and cannot be ignored in our analysis. This paper examines "silence" and the unarticulations as essential parts of consumer feedback and looks at ways in which these can be classified and interpreted for more enriched qualitative research analysis.
We are living in rapidly changing times and banks are not immune from the need to change. As new banking approaches emerge, market researchers are called upon to assess likely consumer reactions to these changes. This paper argues that, because of innate human resistance to change, research should focus not just on assessing the extent to which changes will be accepted, but rather on how to aid consumers to come to terms with changes that are inevitable. It then proposes a conceptual framework within which to conduct and analyze research findings into adaptation to change, providing examples of the practical value of this conceptual framework. Finally, it points to some methods that can be used to align consumer paradigms and banking paradigms when these do not coincide; and concludes that, paradoxically, often the best guides to adaptation to the future can be discovered by studying the past.