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VERBATIM ANALYTICS Tip #20. Use negative sentiment to detect problems

[fa icon="calendar'] Dec 22, 2016 9:41:36 AM / by Matti Airas posted in Customer Journey, text analysis, NPS, customer experience, verbatim analytics

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 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. 

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VERBATIM ANALYTICS Tip #19. How to use the average sentiment to prioritize improvement efforts

[fa icon="calendar'] Dec 21, 2016 3:49:52 PM / by Matti Airas posted in Customer Journey, text analysis, NPS, customer experience, verbatim analytics

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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.

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VERBATIM ANALYTICS Tip #18. Use two heatmaps to benchmark support or sales agent performance

[fa icon="calendar'] Dec 21, 2016 3:47:45 PM / by Matti Airas posted in Customer Journey, text analysis, NPS, customer experience, verbatim analytics

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 We’ve met with dozens of contact center managers during the past six years. They all complain about the same issue: It is difficult to detect systemic issues and benchmark qualitative agent performance. This happens because of the ineffectiveness of their manual categorization process and the focus on support or sales transaction statistics (first contact resolution, number of processed web forms, sale completion rate etc.) rather than the qualitative agent performance.
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VERBATIM ANALYTICS Tip #17. Use topic, sentiment and background variable to drill-down to the root cause

[fa icon="calendar'] Dec 21, 2016 3:41:34 PM / by Matti Airas posted in Customer Journey, text analysis, NPS, customer experience, verbatim analytics

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You see the signal in your dashboard but have no idea what is causing it. Knowing that something is wrong is useless unless you know why or what is causing that problem. That’s why feedback analytics is divided into two distinct phases:
  1. Detecting that something is wrong (or right); and
  2. Finding the reason for that problem (or opportunity).
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VERBATIM ANALYTICS Tip #16 Use a topic sentiment heatmap for extracting insigths

[fa icon="calendar'] Dec 21, 2016 3:37:04 PM / by Matti Airas posted in Customer Journey, text analysis, NPS, customer experience, verbatim analytics

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I have tested many different visualizations and graphs for detecting patterns from open-ended comments during the past six years. What I have learned is that a simple topic-sentiment heatmap works well. 
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VERBATIM ANALYTICS Tip #15 Use stacked charts to hide unintended volume volatility

[fa icon="calendar'] Dec 15, 2016 11:29:43 AM / by Matti Airas posted in Customer Journey, text analysis, NPS, customer experience, verbatim analytics

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 Topic is a volatile measure. Some of the volatility is real (people are mentioning the topic more or less often in their comments) and some of it is unintended (the overall feedback volume varies due to feedback process or seasonal reasons). You need to figure out how to minimize the effect of unintended volatility in your feedback process.
Here is an example of the survey volume volatility. It is difficult to tell the volume of different topics from this chart and the reason for the volume volatility could be that during that month you just sent out fewer surveys.

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.

This stacked chart demonstrates the relative share of topic mentions each week.

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.

 

WEBINAR VIDEO

 

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VERBATIM ANALYTICS Tip #14 Monitor hot topic level volume and sentiment

[fa icon="calendar'] Dec 15, 2016 11:28:50 AM / by Matti Airas posted in Customer Journey, text analysis, NPS, customer experience, verbatim analytics

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 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.

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VERBATIM ANALYTICS Tip #13 Use topic volume to define the relative importance of a topic

[fa icon="calendar'] Dec 15, 2016 11:27:37 AM / by Matti Airas posted in Customer Journey, text analysis, NPS, customer experience, verbatim analytics

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 Verbatim analysis results don’t have an absolute benchmark except the average sentiment (Tip #26). Topic (volume) is only interesting when you: 

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VERBATIM ANALYTICS Tip #12 Define hot topics

[fa icon="calendar'] Dec 15, 2016 11:26:05 AM / by Matti Airas posted in Customer Journey, text analysis, NPS, customer experience, verbatim analytics

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 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. 

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VERBATIM ANALYTICS Tip #11 Don’t focus on the aggregated analysis results

[fa icon="calendar'] Dec 1, 2016 10:32:36 AM / by Matti Airas posted in Customer Journey, text analysis, NPS, customer experience, verbatim analytics

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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.

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