VoC or the Voice is of the customer is the term used by companies for the process of listening to customer feedback regarding their company and brand, products, and services. There are various Voice of Customer solutions available in the market to help you gather this feedback and use it as a useful data to extract insight on a large scale. The VoC (Voice of Customer) analytics programs can be highly impactful when it comes to increasing the value of the customer lifecycle and reducing customer churn.
In this post I’ll highlight how a robust VoC data analytics program can be implement. Read on to explore the steps in greater details:
1. Begin the data analytics program with the question: Any effective VoC data analytics program will focus on answering the queries. Therefore, before analyzing anything identify the query you are looking to answer clearly. For instance, a customer experience pro may ask: What were the sales of our conditioner in North America last month? Or, what modifications should we do to our motel rooms in the next year?
These queries you ask will depend on the data you have gathered, the analytics tools you are using, and the kind of analysis you perform. However, in all probability, you may not know the right questions to ask. In such cases, you can begin with broad questions.
2. Collect and prepare the data: If you have formulated the query in your mind it is time to collect the data. This data you gather must be suitable for your query. For instance, a question focused on the brand may ask for the number of tweets that mention your company. Also, product-oriented queries can take you to a range of satisfaction responses from customers.
The most common data sources used by the VoC data analytics programs are support tickets, online reviews, Facebook comments, survey responses, chat conversations, tweets, call transcripts, and emails. If you do not have the requisite data at hand you can use third-party aggregators.
3. Select the proper VoC tools: When you are required to compare complicated trends over some time you will need something more than a bare bone survey analysis tool. Similarly, if you intend to use insights derived from social media comments in the form of customer survey responses you will need a platform that is flexible and offers better customization and reporting.
For achieving the best results for your investment, select a tool with the provider having good experience in solving the issues similar to yours. Do not rush your decisions. If you choose the wrong tool you will end up with failed data analytics reports apart from the wasted resources. Ask for four things while evaluating the data analytics partner,
4. Data analysis: In the case of VoC data analytics, the reports you will generate have to be focused on replying to the initial queries. You must be focused on reporting the entities, themes, and the related topics discussed in the reviews and the sentiment expressed for each.
Other queries may require different reports depending on what you need such as intention extraction or categorization. If you are struggling to make a beginning you can either contact the support team of the supplier or go back to step 3.
5. Draw your conclusions: Some insights are self-evident while other revelations can surprise you. Many times you will end up getting answers to queries you did not think of asking. For instance, a hospitality organization was trying to decide the furniture for an upgrade.
While doing this, they uncovered a large number of reviews referring to “smell” and “trash” and with a strong negative sentiment. When they went in deep in the data they found out that several guests smelled dumpsters in the parking when they entered the hotel.
These kinds of unexpected mining insights drawn from the reviews by real customers are a terrific demonstration of a good data-driven VoC data analytics program that stands out from the pack.
6. Take steps from improving the customer experience: Many times the best course of action is pretty clear. For the example where the guests complained about the smell of the trash, the management just needs to move the dumpsters.
However, many times the conclusions are a bit more complicated. You will be forced to dwell deep in the particular data points or you may be forced to run more data analysis. But by gathering, combining, and comparing various reports into dashboards, you can uncover the insight you need for moving onto the path of better customer experience.
Voice of Customer Data Analytics Troubleshooting
Once you have selected and started using a VoC data analytics tool sometimes you will struggle to collect useful information out of the reports. In this case, go a step backward.
A clumsy unintelligible output from the tool might come out as a result of a badly configured analysis. However, it might also point to a system failure or corrupted data set. Keep in mind that simple errors many times lead to bigger errors in the case of data analytics. The failure of the analytics tool usually comes out of,
In this case, a good idea is to go back to the original query and trace your steps back. Are you certain that you have gathered the right data for answering your query? Was the data correctly prepared for data analysis? Have you studied all the documentation, guides, and tutorials?
Keep in mind that no one knows a data analytics tool better than those who built it. So, do not feel shy about asking their support team for advice or guidance. If you request for help quickly and often, you will end up saving money, time, and a lot of headaches.
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