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CallMiner CX Landscape Report

Organizations aren't collecting enough data to improve CX

The last few years have been extraordinary for global customer experience (CX) teams. Customers and companies alike have struggled in the face of uncertainty, and many still have room for improvement.

According to the inaugural CallMiner CX Landscape Report, 62% of organizations believe they don’t collect all of the CX data they need and only 12% of teams collect an equal amount of solicited and unsolicited customer feedback.

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CX Landscape Report stock image

State of the CX industry

CX Landscape Survey methodology

Research methodology 

To develop the CX Landscape Report, CallMiner worked with Vanson Bourne to survey 450 senior decision makers from contact center and CX departments in the U.S., UK / Republic of Ireland and South Africa. Respondents were from organizations with 100 or more employees globally, within the healthcare, financial services and retail sectors.

Building better customer and employee experiences

CX Landscape Survey customer feedback

Limited data analysis is holding organizations back

  • Almost all respondents (96%) say their organizations are using manual analysis to some extent, such as hand-coding feedback or aggregating data using Excel or PowerPoint
  • Six in 10 (60%) say they're unable to accurately track ROI regarding customer data and feedback all of the time
  • 58% say their CX departments and teams are not completely aligned with the rest of the business

CX Landscape Report EX

Employee experience plays an important role in CX

  • The vast majority (99%) believe EX is at least somewhat important to the success of CX
  • Less than half are using unsolicited feedback sources to understand the experience of their employees
  • A third (34%) of those surveyed are challenged by customer service representative disengagement or lack of productivity

CX Landscape Report AI

Artificial intelligence is critical to the future of CX

  • While AI has been widely adopted, almost half (48%) report that their organizations aren’t fully realizing the benefits
  • The most common challenges faced are that AI-powered technology is too expensive (41%), or it’s too complex for them to implement and manage (41%)