When it comes to extracting insights from unstructured text there is the hard way and then there is the organized way. Follow these principles and you will be extracting actionable insights and sharing them with CX stakeholders in no time.
1. Close the loop!
Ensure that the actionable insights 'flow' to the CX stakeholders, and that they can interpret the data and deduce concrete actions from it. In practise this means that you have to create a dashboard, which indicates in close to real-time what kind of problen clusters are emerging and what is the root-cause for those patterns.
A rule-based semantic analysis system understands language structures and the relationships between words. It has a massive set of pre-configured rules that enable automatic and accurate real-time analysis. Developing a rule-based language analysis system takes a considerable amount of work and time, sometimes stretching even up to dozens of years. Very few companies have the skills or resources to develop such a system.
CX text analysis is a an application, which connects customer and employee voice to company's business strategy, customer value proposition and corporate values.
Loyalty management surveys, like NPS, have increased the volume of incoming text comments, and an ever larger share of customer complaints arrive via email, web form and social media. All that feedback is unstructured text.
The volume of customer and employee feedback is increasing and more and more of this feedback is in the form of open-ended text comments. Naturally you need to respond to every customer complaint but what else should you do with this pile of unstructured data if anything?
Before starting extracting actionable insights from customer feedback, you need to set up an enterprise insight process that:
- Gathers feedback: you actively solicit feedback through many channels and touchpoints, crawl social media, and analyze contact and support center feedback; and
- Analyze feedback: you've implemented a feedback analysis system that detects the sentiment and categorizes all open-ended customer comments according to an industry specific categorization system (Codeframe).
This gives you an excellent starting point: all the comments are categorized by topic and sentiment. But turning that information into actionable insights isn't a straight foward process. It requires quite a bit of creative data analytics and visualization work.
Follow these 12 steps and you will be able to monitor the customer journey, identify the loyalty drivers and improve your bottom line.
When many web stores sell the same or comparable products within the same geographical area the only thing that sets you apart from competition is the shopping experience. The shopping experience becomes even more important when you don’t sell your own products. And when it comes to an excellent eCommerce experience, there is a benchmark: Amazon has set the bar. Do you know how you compare to the one-click wonder?
Here are a three real-life examples where real-time text analysis service has been successful in identifying systemic issues in our customers’ products and services.
Systemic issues–like login not working on your web service or eCommerce shipping problems in a warehouse–may cause lost revenue and customers. That is why it is important to quickly pinpoint this type of problem clusters. Manual ticket tagging is slow, expensive and the results are inconsistent. Automatic feedback categorization service fixes this problem in real-time, accurately and feasibly.
Open-ended customer feedback analysis is not a stand-alone application because it needs to be fed with text. And that text comes from outside, from another application or platform. Feedback analysis is a feature that enhances the functionality of an application.