London (PRWEB UK) 14 April 2017
Research by predictive analytics firm Warwick Analytics shows that whilst Voice of Customer (VoC) data is being used to drive insight and actions, there is a significant chasm between what Customer Experience professionals analyse today and what they want to analyse. This chasm is primarily a technical one.
The survey, carried out amongst UK customer experience and marketing professionals across all sectors, set out to find how companies are obtaining and using VoC data to better understand issues and improve Customer Experience. The survey primarily focused on how VoC is currently being collated and analysed to aid customer satisfaction, along with the associated challenges, and how this might change in the near future.
The most desired improvements are to link their VoC directly to next best action and actual business performance.
When asked how they want to improve utilising their VoC data in the future, the single most desired outcome (47%) want to be able to automatically generate next best action recommendations from it (only 18% of respondents currently do this). The next most desired (44%) want to link their VoC data directly to business performance (only 29% of respondents currently do this).
Also high on priorities is the desire to add more datasets to their VoC such as service events or product quality (41% desired versus 18% today) and automate the reporting of unstructured data (35% versus 18%).
By far and away the most common analyses today were the Insight team compiling reports (79%), with some automation of reporting of the structured data (47%).
Progress held back by too much unstructured data.
Despite the desire to move forwards with VoC analysis, respondents feel they are being held back because of a vast amount of heterogeneous and unstructured data.
88% of respondents said that the handling of unstructured data was a challenge to their business, with 68% of them saying it was either a major or significant challenge. As a result, the majority of data being analysed is structured data such as surveys and complaints.
74% also said that the overall volume of data was a challenge to them measuring customer satisfaction.
NPS still most adopted measure of customer satisfaction
Most but certainly not all (53%) of respondents use NPS to measure their customer satisfaction with 43% also using their own C-Sat index, 43% using repurchase or churn data, 33% using Customer Effort Score and 27% using Customer Value Added.
This perhaps reflects the views of Bruce Temkin (Chair of the Customer Experience Professionals Association) when he said:
“… We rarely see a company succeed or fail based on the specific metric that it chooses. That doesn’t mean that you can chose a ridiculous metric, but most reasonable metrics provide the same potential for success (and failure). In general, NPS is a reasonable metric to choose, as our data shows that it often correlates to customer loyalty. As organisations mature, we try to get them to use metrics that are more closely aligned to their brand promises …”
Firms are mostly currently analysing structured responses
Surveys are the most widely used type of VoC data being analysed (88%), with 79% listening to social media, 74% analysing complaints and 67% using CRM data. 50% analyse reviews and 26% use mystery shopping. Only 11% analyse their NPS data for VoC.
When asked which data they would like to analyse further, above and beyond what they are already doing, social media listening (36%), CRM (36%), complaints (32%) and NPS data (32%) all scored highest.
There are automated predictive analytics technologies now appearing which can automatically process heterogeneous and unstructured data in a meaningful way. One such company is Warwick Analytics, a spin-out from Warwick University with proprietary algorithms developed from over a decade of academic research.
David Hicks, CEO of leading Customer Experience solution provider TribeCX said: “Voice of Customer data is the oxygen that enables CX executives to listen, understand and respond to customer needs and feedback. Yet the data is so overwhelming in volume and heterogeneousness that they are ironically suffocating in it. We are advising CX Leaders that there is technology now finally emerging which can harvest this data automatically, and early adopters are finally beginning to reap the rewards of automated, continuous insight from VoC.”
Dan Somers, CEO of Warwick Analytics adds: “Our automated predictive analytics technology originally spun out of Warwick University research to focus on early warning of manufacturing quality problems. Customer Experience issues are really a natural extension of that, and can be regarded as service or product quality issues. The way we automatically analyse unstructured data turns out to be as relevant to customer notes as engineering notes in terms of handling, and the use cases are ever-growing”.
Whilst CX professionals see opportunities to improve customer experience using Voice of Customer data, there are significant technical challenges to overcome to identify root causes. However, there are automated predictive analytics technologies appearing which can dynamically identify these and as the landscape matures there will be more exploitation of these rich data tools in the future.