The headline tells it all. Being able to use the negative sentiment or NPS score to drill down to the customer comments is a powerful way to find actionable information. Your analysis results become even more relevant and valuable when you filter by topic specific sentiment or NPS score.
Whatever categorization service you decide to use, it should include a topic level sentiment analysis. This means that each topic mention gets scored with negative, neutral or positive sentiment.
- Detecting that something is wrong (or right); and
- Finding the reason for that problem (or opportunity).
The graphic above is using absolute volumes. The jump in volume in this example is caused solely by the survey process. The reason is that in certain weeks more surveys are sent out. You can see that customers are talking more about CHECKOUTS but it is difficult to see the relative importance of this.
Stacked charts hide the survey process and seasonal effects. Once you use stacked charts, it is a lot easier to see how different topics are performing.
It is impossible to receive the same number of feedbacks every day, week or month. What is important is to figure out the relative share of the topics customers or employees are talking about. Stacked charts makes this easy to do.
We are writing a series of blog posts on the topic “25 tips for extracting actionable insight from open-ended comments". If you prefer watching a video, click the link below.
Hot topics (Tip #12) are the most important customer experience attributes (or at least the most talked about attributes). They need to be tracked in more detail than other topics. In practice this means that you need to develop a dedicated topic-sentiment monitoring visualization to track the hot topics.
Verbatim analysis results don’t have an absolute benchmark except the average sentiment (Tip #26). Topic (volume) is only interesting when you:
We have spent close to a decade trying to figure out what is the appropriate level of detail in a feedback categorization system or scheme. The challenge is finding the right balance between significance and granularity.
Whole datasets clouds look good but tell very little about customer behavior. Topic clouds are more valuable, because they “pool” words and phrases into industry specific contextual “baskets”. But Topic clouds have limited analytical value.