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Take a peek at our powerful survey features to design surveys that scale discoveries.
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Need to map Voxco’s features & offerings? We can help!
Get exclusive insights into research trends and best practices from top experts! Access Voxco’s ‘State of Research Report 2024 edition’.
We’ve been avid users of the Voxco platform now for over 20 years. It gives us the flexibility to routinely enhance our survey toolkit and provides our clients with a more robust dataset and story to tell their clients.
Steve Male
VP Innovation & Strategic Partnerships, The Logit Group
Explore Regional Offices
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We’ve been avid users of the Voxco platform now for over 20 years. It gives us the flexibility to routinely enhance our survey toolkit and provides our clients with a more robust dataset and story to tell their clients.
Steve Male
VP Innovation & Strategic Partnerships, The Logit Group
Explore Regional Offices
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Voice of customer has been a buddy to almost all the businesses who wish to growth their empire over the market. With a convenient way of presenting the customers and their experiences and opinions, the voice of customer helps the brands to locate their customers with regards to their cx.
While the voice of customer is a process that is dedicated to one goal at a time, the real deal lies in the analysis of the data. Any business, on conducting a customer feedback survey, gathers tons and tons of data. But how your conclusions turns out totally depends on how you analyze your data.
In this article we will be looking at 7 steps of analysing your voice of customer data which will lead to reliable conclusions and insights.
Create an actionable feedback collection process.
Collating basically means gathering the data from different sources and putting it together. This put together data is then compared and examined. In the voice of customer, your data be about cx and also the metadata about your customers indicating how long have they been your customers, what is their buying pattern and so on. Make use of open-ended questions as they provide more information on the customers. You can order this metadata under specific headings defining the customer demographics and their actions-interaction with your brand.
The next you wanna take is neatly categorize your gathered feedback in a way that starts to make sense. When you conduct a survey, you surely get a lot of data, but does it make any sense at first? Absolutely not. Hence when you collate your data, you need to categorize it.
Categorization of the feedback data basically comes down to these two basis:
Before getting into touching the data, you would want to take a look at how your data actually is. Try to figure out how diverse your feedback is and how you can code it to make sense. If you find out some customers giving you different feedbacks, you should try to cover more ground and see the patterns in their responses. The ideal practice is to look through the first 50 feedbacks, and if they indicate the same thing, then you are likely going to save your time from reviewing tons of data.
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If you are a brand using the voice of customer as your go-to cx evaluation technique, then you are most likely to be in touch with all the technology. There is an emerging technology field called “Artificial Intelligence” which is destined to be making things WAY easier for us. In fact, the voice of customer makes use of AI tools.
These tools are used to code the feedback. Ring a bell? As we have discussed before, coding your feedback means summarizing the customer feedback to a few words that indicate the essence of what the customer has been trying to tell. This process is most vital in your voice of customer analytics as it will be drawing out your feedbacks as “good”, “bad” and “issues”. Your automated tools need to be precise with these coding to yield better conclusions.
There might be cases when your code is actually indicating one single issue, but as you deeper you see the topic needs breaking down.
Example: the code has labelled various feedbacks under “Product issue”. But when you go through more feedbacks, you find out this code can be broken down into more codes like “Product durability” “product manual” “Product price” and so on.
It is important that you take the initiative to dig deeper into your code. Even though it uses AI tools, you will need to look into the data yourself.
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Once you have your feedback code, you have to see how popular each code is. Meaning, how much feedbacks does a particular code cover.
This will help you understand how popular the topic of each code is and what are the patterns in the customer feedback. One way to do this is to sort your code according to alphabetical order. This way you will have a specific group of codes together like the codes starting with “Product” “email” etc. then highlight the cells that have the same feedback code which will give you the count of each code.
Now you have your bifurcated code, you have a sense of what the customers are saying. You will know the touchpoints and gaps between the customer expectations and what you are providing them as a brand. But how will you get the entire organization to work on it? The simple way is to outsource this summary to each and every team in your company.
Make sure every team that is directly or indirectly connected to the customer, KNOWS what the customers are saying. Make the voice of customer reach every nook and corner of your company so that each team and each individual will start improving their performance and services that best suit the customer experience.
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