Biz Tips: The Top 7 Features of an NLP Tool for Customer Feedback Analysis

Biz Tips: The Top 7 Features of an NLP Tool for Customer Feedback Analysis


The Top 7 Features of an NLP Tool for Customer Feedback Analysis

The voice of the customer has been an essential matter since the appearance of Six Sigma after World War II. However, capturing customer feedback has always been a significant challenge for marketing. This is because most answers in studies and focus groups tend to be biased. The most useful feedback for a company is that which emerges naturally from clients’ emotions, either highly positive as praise or profoundly negative as a result of dissatisfaction.

The rise of the Internet has given customers the opportunity to expose their point of view freely and even to exchange opinions with other clients using the same product or service.

The new challenge for companies is to now make sense of all the available information which can be found online regarding their products. Some organizations have undertaken the daunting task of analyzing their clients’ opinions through manual review, but in the case of large organizations receiving thousands of individual feedback per day, this is not a feasible solution. Natural language processing (NLP) offers the right tools to get real-time, unbiased opinions from real customers. The only drawback of this solution is that, so far, systems are not performant enough to understand the sarcastic nuances present in negative comments.

A recent study from the sentiment analysis symposium in New York identifies the top 7 items which need to be included in any good NLP tool for customer analysis software. There is a short overview of the ideas discussed in that study and even some examples of actual use.

1. Synonym detection tool

To create a proper classification of all the topics discussed by clients, first of all, a good NLP algorithm will identify the same ideas expressed in different ways. This starts with synonym analysis and continues with stemming, identifying plurals, and even removing typos.

This step is crucial because not identifying the different ways people talk about the same thing can lead to misinterpretation and not turning information into actionable points. Keep in mind that synonyms are very dependent on the context. Therefore, the NLP tools should be dataset-specific.

2. Understanding the intention of the customer

When people are being sarcastic or trying to express a negative comment mildly, they can use expressions which can be misinterpreted by the algorithm. In these cases, the tool should take into consideration the context and look for negation adjectives and adverbs revealing the true meaning of the comment.

Even positive comments can be misclassified, especially when there is a case of double negation, for example, “not bad at all”.

3. Let data speak for itself

Classic studies had pre-defined categories regarding customer feedback. Most of these revolved around quality, pricing, packaging, delivery, and return policies. All other aspects of customer feedback which didn’t fit into these categories were simply labeled” Other”. If this category, named ”Other” is too large it’s a clear warning sign that your approach is wrong and not very insightful. It means that you are focusing on the wrong categories.

A good NLP tool constantly scans and listens to the data identifying trending topics, as you can see on Twitter or Google Trends. Let the machine dynamically define essential issues, or you’re just looking at what you expect to see.

4. Cross-check information

Verify how accurate your algorithm is by creating a case study. Select a theme and select all comments related to that specific theme. Ask your team of marketers to analyze manually the problems expressed in the selected sample and to identify the underlying sentiments. Compare the results you get from the human team with the estimation of the computer to identify any potential misfits or biases. This will help signal existing issues with the algorithm. Retrain the algorithm and Retest both the current sample and a new one to make sure it is well calibrated.

5. Work with what you have

Although machine learning performs best on large datasets, you should choose an NLP tool which yields accurate results even when fed with data. This becomes especially important in the case of smaller companies. To overcome this problem of insufficient data, another possibility is to join forces with a similar company and join your data sources. You could also repurpose and use general access data like surveys and census data.

6. Allow dynamic changes

Customer feedback has evolved, new topics develop while others die. The NLP tool of your choice should reflect these changes as close as possible to real-time interests change, therefore the algorithm should be able to create new categories and merge old ones. This dynamic instructs the analysts about market trends. Keep in mind that the more specific you are in your analysis, the better you serve your clients.

7. Think about actionable points

Most studies look impressive, but it all comes down to real and actionable insights. You need to be able to see the results and have a clear image of the user persona associated with its respective problems.

For example, in the case of a rebranding, you could scan the online reactions to the new label and conclude that generation Z does not appreciate it. Depending on how important this category is for your business, you could ignore the result or create a new line, dedicated specifically to this target market.

Next steps

The key takeaways are related to the vast wealth of information provided value free text and client reviews and the challenges which emerge from the fact, but these opinions are unstructured.

When using an NLP tool, you must take into consideration the fact that most customer feedback comes in a very disorganized state including misspellings, abbreviations, typos, autocomplete, which can make the classification and analysis tasks harder.

Also, the way each person expresses itself can be a real challenge for the algorithm, which amplified in the case of enhanced sarcasm.

Sometimes customer feedback doesn’t contain all the necessary details for proper analysis. For example, imagine that a comment reads “too expensive” and you sell hundreds of products without knowing what exactly they refer to.

As even most people have a hard time reading all the nuances of client review, we can expect that it will take some time until machines will be performant enough to understand human emotions properly but we’re getting there one step at a time.

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