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Text analytics is integral to Customer Journey Analytics

Text analytics is integral to Customer Journey Analytics

Anil Chawla

A recent Verint Digital Tipping research encompassing 2000 Indian customers suggests customers giving greater weightage to digital channels for the following key engagements-

  • Make payments
Channel Preference Percentage
My account online 43%
Speak to someone in person 18%
Mobile app 14%


  • Raise issues
Channel Preference Percentage
e-mail/sms 30%
Speak to someone on phone 20%
Speak to someone in person 12%


  • Make a new purchase
Channel Preference Percentage
My account online 29%
Mobile app 22%
e-mail/sms ; web self service 9%


Preference for digital channels generates massive volumes of big data—much more data than what organizations can collect, correlate and assess. The quantity and speed with which data is generated, as well as the diversity of that data—much of which is unstructured and difficult to analyze—can pose a huge challenge for customer centric players; minimizing their opportunity to take timely action and increase profitable outcomes.

In addition, the rise of mobility and proliferation of communication channels is further complicating the task to manage people and processes required to respond to consumer expectations in a consistent, personalized and contextual manner.

By analysing contemporary text-based channels, such as web chat, email, social media, sms and survey notes organizations can gain deeper insight into process and performance gaps. Text analytics can also help map customer experience issues and opportunities. With the European General Data Protection Regulation (GDPR) coming to play, text analytics can also drive compliance by quickly revealing regulatory breaches and failure to adhere to internal policies.

Natural Language Processing (NLP) and Text Analytics

NLP based text analytics not only offers insights into customer sentiments to reveal issues that may need immediate attention but can also predict possible underlying concerns. Analysing unstructured data can help organizations understand customer attitudes and preferences towards a brand, products, services, processes, and employees.

At an internal level text analytics provides key metrics that can help organizations assess-

  • Productivity of the text-based channels,
  • How easy was it for the customer to use the text centric channel
  • Gauge the customer handle time
  • Optimize the number of messages required to resolve an issue.

At an advanced level text analytics can also support conversational analytics deciphering unstructured data into employee and customer streams. Conversational analytics can help organizations map what customers are saying vis-à-vis as perceived by the employees.

Text analytics is also integral to customer journey analytics. With the cost of acquiring new customers and the resources to serve them steadily increasing, organizations are assessing holistic solutions to decode customer journey analytics. Text analytics integrated into customer journey analytics helps organizations to decipher-

  • Conversations that will have the most impact on customer experience. Organizations can prioritize their course of action basis critical interactions.
  • Quantifying the magnitude of a particular problem and whether the problem spreads over to other text and voice based channels.
  • Understanding how best to implement changes that will yield business benefits.

Embedding text analytics into customer journey analytics- a use case

Basis the number of e-mails received, the customer care department of a large corporation intuitively realized that customers were facing problems in making payments. By analyzing the customer journey data and sentiment associated with payments’ channels, it was clear that customers were upset about having to communicate multiple times for the failed payment redressals. After further research, the organization discovered that payment issues were happening in most channels – online, IVR, mobile app and web chat. By computing the cost per interaction and the volume of payment issue alerts, it was evident that making payments easier for customers would have a huge impact on retaining clients.

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Traditional methods of gathering digital feedback such as pop-up or pop-under surveys are increasingly ineffective because they’re intrusive, distracting and disrespectful of customers’ time.

Text analytics enables organizations to automate analysis of all feedback and distill key data to uncover process, product and service issues and help drive operational improvements through real-time and targeted action.

Even before an organization rolls out its customer journey analytics strategy it is imperative that the organization practices and supports data democratization. In many large organizations, data churned through customer and employee activities sits in silos. For instance, a digital team will use customer feedback for improving the interface, while the contact center will use speech analytics for improving agent performance. Some of the reasons why teams don’t share data with other departments-

  • Sharing data with the other teams is not an immediate priority.
  • The team is not well equipped to use or understand the data.
  • The team who does not own the data might misinterpret the data.

Data democratization can improve customer journey analytics

An organization’s contact centre and the digital team were separately receiving customer grievances to an issue related to the company’s website. The contact centre was using speech analytics while the digital team largely followed insights from the text analytics platform. When the contact center manager shared their speech analytics data with the digital team; the common website-specific issue came to the surface. The common issue was leading to both increase in grievances calls and online complaints. Previously, the issue in hand was a low priority item for the digital team, but the speech analytics results revealed the magnitude of the issue.

The digital team realized they could easily address the issue which ultimately reduced the cost of servicing the customers from both the channels.

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