Making VoC (Voice of Customer) data flow to the right place

Author :

Joseph Lee (CEO)

Jul 3, 2024

Making VoC data flow to the right place
Making VoC data flow to the right place
Making VoC data flow to the right place

Customer satisfaction and customer experience have been considered difficult to manage as they are qualitative data. But they can actually be easily managed through Voice of the Customer (VoC) data.

Although the VOC data has been consolidated and managed for a long time, it is only recently that it has started to be used effectively. Why hasn't VOC data been effectively utilized until now?

1. Challenges to VoC

There are two main reasons why it has been difficult to improve customer experience (CX) through the use of VoC data. One reason is that simply 'quantifying VoC data' is deemed sufficient, without linking it to activities aimed at improving customer experience. The other reason is that VoC data and insights remain confined to the CX teams.

  1. The issue of stopping at just producing quantitative indicators

Are you familiar with the indicators that are used for VoC reports? The following example is a statistical table summarizing the number of complaint VOCs.

Data like the chart above is meaningful in that it classifies various VOCs coming through customer service channels by type, but it is difficult to use this indicator to improve customer experience.

The same goes for scores such as NPS and CSAT, which are commonly used to quantify customer satisfaction. Analysis that says ‘NPS has improved by an average of 0.1 points compared to the last quarter’ does not tell ‘‘what our company should do next quarter.’

If you are satisfied with simply converting qualitative data into quantitative data, it can be said that you are not utilizing VoC data properly.

  1. The problem of VoC data staying only in CX teams

As shown above, if VoC data does not provide clear actionable guidelines for improving customer experience and remains at the level of simple category comparison, this second problem arises.

Because the direction of improvement activities to address VoC is not clear, it is inevitable that it will be difficult for relevant departments to have a sense of responsibility and accountability over these. In these situations, what often happens is that the CX teams takes over all VoC management and improvement activities.

However, because there are limits to the CX teams’ ability to manage all support tickets, VoC data needs to be analyzed and considered for improvement jointly by multiple departments.

2. How to properly utilize VoC

  1. VoC data requires “processing”

Because VoC data is unstructured data, it needs to be processed. In other words, to fully utilize VoC, processing through semantic analysis and sentiment analysis is necessary, rather than a simple categorization. By processing the data, it will reveal which areas customers are most interested in and where they are most dissatisfied based on VoC data, providing clues to solutions.

In addition, it is possible to derive deeper insights by referring not only to the information contained in the VoC itself but also to information about the customer who left the VoC (age, gender, membership level, etc.). 

Check example cases below:

Example) VoC data: Inquiry categories with a higher rate of negative sentiments 

Information: The level of DSAT is estimated to be relatively large, and details need to be identified first.

Example) VoC data: Among the VoC for product A, more than 12% of negative opinions regarding ‘finishing treatment’ were confirmed.

Information: Product A needs improvement in finishing. 

Example) VoC data: Collect and analyze only VoC left by female customers in their 30s

Information: Identifying interests and complaints of female customers in their 30s

As shown above,  it is important to conduct in-depth analysis such as sentiment analysis, and customer meta data integration to process VoC data into valuable information.

  1.  VoC data must flow to the right place

To resolve the problem of VoC data being siloed within CX teams, it is important to designate a specific department to lead improvement initiatives for critical VoC content. This requires establishing meaningful standards for classifying and analyzing VoC data, with a clear delineation of responsibility for driving improvement activities.

In order to fully utilize the insights found through VoC analysis, the insights must be delivered to the teams that can provide the most efficient solution. For example, if there is a need for product improvement, it should be assigned to the Product Management team or Engineering team that is prepping products for the next release. Also, any areas needing improvement in a digital product, such as a web site, must be communicated to the relevant team lead, such as the product manager or UX research team.

On the other hand, not all VoC needs to be routed outside of CX teams. Tasks that require efficient customer support response (e.g. provision of guide documents, etc.) can be directly handled by the CX/Operations team.

3.  VoC data utilization plan by department

We’ve confirmed that VoC data should not be managed solely by the CX teams but should be distributed across various departments for reference and improvement. Now, let's explore how each specific department can utilize VoC data effectively.

  1. Product Management, Product Design, and Engineering

First, the Product Management, Product Design, and Engineering teams—departments responsible for planning and creating tangible products—can use VoC data to identify areas for improvement. By analyzing VoC data, they can efficiently develop products that meet customer needs and requirements. For example, in a product renewal project for a cosmetics brand, the process of product development using VoC data can be summarized as follows.

Collect VOC data from the target customer base

  • The target customer base for Product A was defined as 'women in their 30s.' The existing VoC data for this customer segment was then extracted and organized from the in-house database.

Derive insights from VOC data

  • It was found that the keywords most frequently mentioned by women in their 30s when purchasing similar products were 'whitening' and 'moisture’.

  • The main complaint about other products regarding ‘whitening’ was the lack of a quick visible attack.

Setting product development direction

  • When renewing a product, thoroughly review the composition of ingredients related to ‘whitening’ and moisture.

  • For ‘whitening’, encourage more frequent use by enhancing portability, formulation, and container design.

  1. Marketing team

Marketing teams can use VOC data to suggest customized marketing strategies. For example, for a customer who frequently uses keywords like ‘amount, cost, price’,  an effective strategy for targeted marketing campaigns could be suggesting methods related to these keywords, such as ‘Add a channel and receive a coupon’. Alternatively, marketing strategies can be tailored to customer segments by analyzing purchase cycles and frequencies using VoC data. Based on the purchase cycle of the product purchased by the customer, notifications related to user reviews, renewals of the purchased product, marketing informing of price benefits, and calculation of purchase frequency are applied to highly loyal customers according to their purchase. This allows effective complementary target marketing through discount coupons, membership promotions, etc. for products that are good for use with already purchased products.

Example) Purchase cycle for product A confirmed to be 3 months

Send updated information and benefits regarding the purchased and related products to customers who’ve purchased product A 3 months after purchase

Example) Confirm that VIP customer purchased product A

Send information on benefits along with an advertisement for product B, which complements product A, using the title  ‘Information on benefits provided only to Gold Membership customers’.

  1. Product team at Tech companies 

The product team at tech companies can make suggestions to improve User Experience (UX) based on the results of VoC data analysis. There are three main ways to improve UX.

First, by identifying and resolving solvable complaints through system inspection and improvement, it can significantly enhance the customer experience by eliminating recurring pain points. For example, enhancing the interface to address common complaints—such as payment system errors, font size issues, and insufficient product details—can greatly improve overall service usability.

Second, UX can be designed to increase the purchase conversion rate by referring to VoC data. If a specific customer group has more positive opinions about a particular product or service, exposing the product or service at the top or recommending it in a pop-up when customers with similar characteristics access the app or website can increase the conversion rate.

Third, frequently asked questions from customers can be identified from VoC data and answered in advance through User Interface (UI) elements such as tooltips. This approach creates a foundation for users to naturally learn how to use the service, reducing the need for separate inquiries.

  1. Direct use by the operating department
    Finally, not all VoC needs to be routed outside of CX teams. Improvement activities, such as updating the service response template or revamping the FAQ page, can be undertaken by the CX teams based on VoC data. In addition, improvement activities such as updating the response manual (script) or revamping the help cetner can be done by leveraging collected customer data.

    Due to the variety of customer inquiries, the response methods must also vary according to the specific customer. While providing immediate response to these inquiries improves customer satisfaction, it is difficult for all employees to respond immediately as it results in high work fatigue. In particular, responding to negative comments is one of the areas with the highest level of work fatigue. To address this, a manual and templates for responding to negative comments that can be shared among employees can significantly reduce work fatigue.

If this process is implemented smoothly, customers will receive a high-quality customer service regardless of the support agent they interact with, thereby improving both support agents and customer experiences.

According to the analysis of Company A's VoC data, the most frequent complaint during phone interactions was 'delay in phone connection.' Further investigation revealed that customers prefer calling personal work mobile phones over the customer call center number.

Solution: Revise the response manual with the following contents
A manual that instructs to send a template text message to the recipient’s personal work cell phone when the customer calls the main customer center number isn’t available can solve the delay in phone connection related to the above.

4. Benefits of managing VoC data through Syncly

Based on what was explained above, what are the benefits of managing VoC data through Syncly?

First of all, it is difficult to utilize VoC data because the data is not processed. This is where the first benefit comes in: AI analytics. Through AI analysis, VoC data written on a qualitative basis can be easily converted into quantified data through semantic unit analysis and sentiment analysis customized for each company.

The second advantage is that it provides an integrated dashboard based on VoC data processed by Syncly, creating an environment where all departments within the company can utilize the data efficiently and effectively. Through this, the scope of utilizing VOC data is expanded to various teams like Customer Experience/Support, Operations, Product, Engineering, and Marketing.

As a result, Syncly identifies keywords extracted by topic through semantic unit analysis and provides a dashboard to classify and manage customer satisfaction and dissatisfaction level based on sentiment analysis. Through this approach, each department serves as a stepping stone for strategic execution by dividing projects that maximize strengths and at the same time complement weaknesses. This allows each department to leverage its capabilities to the best of its ability and collaborate with other teams to improve the customer experience throughout the entire customer journey.

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