Customer Experience Analytics
Improve profits by understanding the emotional and rational topics that drive customer loyalty.
The big data revolution has meant that there are nuggets of insight within customer experience data everywhere: CRM data, reviews, complaints, enquiries, surveys, social media etc. The ability to harvest and analyse these data in an automated way to provide predictive, actionable insight is a holy grail for marketers and customer experience professionals.
The existing state of the art when it comes to customer experience analytics is using a combination of text analytics, predictive analytics packages and significant manual input from data scientists.
But firms are getting bogged down, spending too much time and resources cleansing and transforming data. CrowdFlower estimates “data preparation” at 80%. Further, assumptions and errors are an inevitable part of the process where human judgement and skill is required.
PrediCX is a speedy way to automate the classification of text data by crucially ‘asking’ users for specific input where it needs it to optimise performance. As a result, it can automatically classify Voice of Customer (VoC) data to a high degree of accuracy with minimal user input.
Our Optimised Learning eliminates this huge amount of human intervention and increases accuracy as it quickly starts to ‘ask’ a user, who doesn’t need to be a data scientist at all, for minimum input.
It’s this amalgamation of automation and promoted human intervention that is a new breed of customer experience analytics and is creating huge opportunities for organisations looking to gain more insight and utilise more of the data they accumulate.
For example, one of the world’s largest airlines sends out tens of thousands of surveys every month to customers of all of their services. They analysed the structured data such as Customer Satisfaction (CSat) scores per touchpoint on the customer journey (cabin cleanliness, check in, meals etc.). However, despite having the state of the art text mining technology they struggled to analyse the free text in a systematic way and really only used verbatims to follow up the structured findings in discrete projects which involved highly-skilled analysts spending a lot of time.
By generating automated predictive insight from survey data, the airline was able to not only pick up the structured data such as multiple choice answers and CSat scores, but also automatically generate the reasons why their customers were happy or unhappy and get some indication on what the predictive factors were such as routes, demographics or customer segments. All in near real time.