There are four ways to create the categorization system (Codeframe, Taxonomy). But whatever way you choose, make sure that the system takes into account both top-down (what the management wants to see) and bottom-up (what the text makes possible) approaches. Well working categorization system requires a couple of iterations and is a balance between these two views.
Designing and implementing a uniform categorization system might seem like a daunting task but the benefits are clear. Uniformly categorized customer comments have the power to transform your organization.
Customer feedback taxonomy (aka Codeframe, Categorization system) enables you to report verbatim analysis results in the same way as structured information (like sales figures) is reported. It creates a common language within a company and brings customer’s voice into the decision making process. It also has the power to transform the organization to be more customer centric.
Here are the most important categorization system requirements.
Capture all relevant words, phrases and brands from open-ended feedback.
You cannot analyze customer feedback without categorizing it. This categorization has to be done systematically, relevantly and consistently. Your categorization system (Codeframe) needs to be uniform across the organization otherwise the text analysis results cannot be used in top management reporting.
Signal categorization turns open-text into statistical information, which enables you to
- Detect patterns (trends, weak signals);
- Benchmark organizational units; and
- Distribute the customer comments in real-time based on customer experience stakeholder roles.
There are four ways to categorize feedback:
If you get only few hundred Signals per month, this is a manageable method. With higher volumes this task becomes slow, expensive and the results are inconsistent. Humans can handle only about a dozen categories. This means that e.g. all weak signals and most emerging trends belong to the “other” category.
Before moving any further, you need to list the magnitude of your challenge. After all, it is easy to make sense of few comments arriving via one channel. One of our Ph.D.'s in computational linguistics, Timo Lahtinen, put the challenge well into words:
A couple of weeks ago I wrote about the eCommerce customer experience in general. This week I am digging deeper into the five and a half factors that form the eCommerce customer experience.
We have analyzed millions of webshop customer’s comments. This has taught us how customers talk about eCommerce. I have gathered in this blog post the key insights on how to analyze eCommerce customer comments and understand their loyalty.
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.