CX Strategy

How to build a modern voice of the customer program

By
Joel Passen
February 8, 2023
5 min read

A guide to leveraging modern technology to build an actionable voice of the customer program.

Every business benefits from knowing what customers think and feel. A Voice of the Customer (VoC) program can help you capture and leverage customer insights to improve your products, processes, relationships, and bottom line. VoC programs have been in existence since the dawn of marketing. However, until recently, they were limited to gathering data through surveys, interviews, or focus groups. Most VoC programs fail because they rely on yesterday’s tools to address today’s challenges.

Surveys still fall short

Most companies still rely on surveys to gather customer insights. Sure, surveying customers sounds like a good idea. To some extent, surveys are a good starting point for obtaining information about customer experiences. But let’s face it, we all know that survey response rates are low. According to Delighted, a good survey response rate ranges between 5% and 30%. This means that your analysis through surveys represents only a fraction of your customer base, and typically, only the dissatisfied or extremely satisfied customers take the time to respond. Unfortunately, most VoC programs still rely on surveys as the number one data source to influence decisions about products, marketing campaigns, service processes, and more. 

Social media monitoring - meh

While social media monitoring can be a great source of customer data and insights, it has flaws. There are several reasons why it may not always be the most reliable source for customer insights. First, as with surveys, social media users’ opinions change rapidly due to the nature of the platform. The same user may have different views or opinions at different times, which can lead to issues with reliability. Companies must ensure they are looking at a large enough sample of customers and not just basing their decisions on a few users' whims. Second, as we’ve learned from politics, all sources on social media are unreliable, and there is no way to verify their accuracy or truthfulness. VoC program managers can be misled if they rely heavily on these sources without doing extra research. And finally, as with surveys, monitoring conversations on social media is a time-consuming process. Companies must dedicate resources to this task to keep up with the latest trends and conversations about their brand or products, which can be costly in terms of both money and time.

Focus groups flop

For decades, businesses have relied on focus groups to learn more about their customers. Unfortunately, focus groups flop in many of the same ways that surveys and social media monitoring fail to deliver actionable insights. First, focus groups are typically limited in size and scope, making them unsuitable for gathering insights from a large customer base with diverse segments. Second, running focus groups is costly and resource intensive. This makes it difficult for companies with limited resources to benefit from them.

The trends to watch for when building a modern VoC program

Listen, if you rely on surveys, social media, and focus groups as the main inputs for your voice of the customer program, you are not alone. These methods are still the standard. But, there is a new trend emerging driven by advancements in technology.

Innovative businesses are starting to use traditional channels of customer feedback in combination with unsolicited feedback to gain true insights into VoC.  

VoC programs have come a long way since their inception, from manually collecting data through surveys and interviews to leveraging AI-driven analytics tools today. Technology has revolutionized how organizations collect, analyze, and deliver customer insights to the teams that need them most. With modern tools and platforms, businesses can collect, analyze and leverage data on a larger scale and with greater accuracy than ever before. Here are some ways technology has changed VoC programs:

AI-driven signals 

AI has revolutionized analytics tools over the past few years by allowing companies to collect large amounts of data quickly while also uncovering signals about specific customer behavior that were not possible before. Going beyond just sentiment,  AI-driven signals help organizations develop strategies that meet customer needs better and lead to long-term success. But the real power of AI is to deliver the signals that are happening now — ones that can impact this quarter's results! 

Automation

Before, businesses had to manually enter data into various formats and generate time-consuming and backward-looking reports. But with the combination of AI-driven insights and automation, teams can now automate processes such as collecting the unabridged, unbiased, and unsolicited voice of the customer. Automation, in this sense, reduces costs and frees up resources while increasing the speed at which teams receive valuable customer feedback. 

Data integration 

Modern customer intelligence platforms can combine multiple data sources to help VoC teams get perspective, providing a richer understanding of customer signals and trends from multiple channels. Using multiple data sources in combination with machine learning algorithms, companies can create more accurate models and insights than they would have been able to do with just one data source. For example, imagine having a searchable interface on top of every inbox, video call, ticket, and survey — a single pane of glass, as it were — a window into a real-time understanding of your customers’ needs and preferences. 

It’s time to modernize your VoC program 

The success of any VoC program depends on selecting the right tools and technologies for collecting, analyzing, and interpreting data. Companies need to consider factors such as cost-effectiveness, scalability, accuracy, and speed when building and updating VoC programs. Here are the considerations to get you started. 

  1. Collect more relevant data sources

Don’t stop surveying, scouring social media, or conducting customer interviews. Gathering multiple data sources is key. But it’s time to add data sources. Customer intelligence technology is maturing quickly. Many of today’s systems allow you to create omnichannel customer experience insights by capturing and analyzing every customer interaction, regardless of channel (phone, email, chat, etc.).   

  1. Analyze and interpret customer data 

Once relevant data has been collected, teams must analyze it effectively to draw meaningful conclusions. This requires the effective use of AI technologies such as natural language processing (NLP) or computer vision (CV). Effective analysis helps uncover signals and patterns that wouldn’t be visible from just looking at raw numbers or statistics like the results of surveys. 

  1. Deliver what matters - now

Finally, companies should use the signals gained from the analysis process to take actionable steps to improve their services or operations to better serve customers’ needs. This could involve implementing changes based on customer feedback or altering marketing strategies according to changing trends in customer preferences.

Overall, creating a modern VoC program is essential for businesses in today's competitive market. By understanding its fundamentals and leveraging advanced technology, companies can gain valuable insights that can help them succeed.

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If improving revenue retention is a key priority in FY25, here is some food for thought. If you believe data is the essential foundation for improving retention, imagine the possibilities with 50-100x more data about your customers. Here’s the thing: Every business has this customer data, but 99% of businesses are sleeping on a data set that could change their business. It’s the unstructured data that’s sitting in ticketing systems, CRMs, chat systems, surveys, and the biggest silo by volume - corporate email systems. Most of us still rely on structured data like usage, click rates, and engagement logs to gauge our customers' health. However, structured data provides only a partial view of customer behavior and revenue drivers. Unstructured data—like customer emails, chats, tickets, and calls —holds the most valuable insights that, when leveraged, will significantly improve revenue outcomes.

Why Unstructured Data is Essential for Revenue GrowthImproving Customer Retention: Unstructured data helps businesses identify early warning signs of dissatisfaction, allowing them to create proactive interventions before customers churn. Repeated mentions of poor experiences, response lags, product-related frustration, and more in call transcripts, cases, and emails indicate potential churn risks. By identifying these trends while they are trending, businesses will improve retention.

Fueling Product Innovation: Let’s face it: Our customers bought a product or service. Post-sales teams don’t develop products and are limited in what they can directly impact. Product teams need more unbiased, unfiltered contextual customer data, and they need it consistently. Unstructured data provides real-time feedback on how customers use products and services. Businesses can analyze customer feedback from multiple channels to identify recurring requests and pain points. This data fuels product innovation and informs customer-led roadmaps that lead to higher engagement rates and more profound value. Developing products that directly respond to customer feedback leads to faster adoption, better advocacy, and a competitive advantage.

Identifying Expansion Opportunities: Unstructured data reveals customer needs and preferences that structured data often overlooks. Businesses can uncover untapped expansion opportunities by analyzing email, chats, and case feedback. These insights help identify additional products or services that interest customers, leading to new upsell or cross-sell possibilities. To drive immediate improvements in revenue retention, the key isn't pouring resources into complex churn algorithms, chatbots, or traditional customer success platforms—it's being more creative with the data you're already collecting. Start listening more closely to your customers, identify the patterns in their pain points, and share this knowledge with your peers who can improve your offerings. This is the year to start thinking outside of the box.

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Last year, I bought a pair of ski pants and the zipper fell out on the first chair lift. I called Burton, and they offered an exchange. New pants, first chair, same problem. Support informed me that I was required to return the pants for repair. The repairs would be completed after ski season. For the inconvenience, Burton offered me a 20% discount on my next purchase of skiwear. The next time I am in the market for skiwear that I can't wear during ski season, I will use that coupon.

I started my first business over 25 years ago. Since that day, I have lived in an almost constant state of fear that somehow, somewhere, things would get so broken that we'd treat a customer like this.

Let's be clear, no one who runs a business wants stuff like this to happen. Yet, it happens all the time.

If you run a software company, your engineering team will have usage tools and server logs to tell you when your product is "down" or running slowly. They can report which features are being used and which ones aren't. You'll learn that certain features in your product cost more to run than others, maybe because of a bad query, code, or something else. And you'll know what needs to be upgraded.

However, every time a customer contacts a business, they are "using" (or "testing") your product. If you sell ski pants, your product is ski pants, and your customer service team. If you sell software, your product is your tech and your customer service.

Yet, your customer-facing teams have very poor usage data, if any at all. Which feature of our service gets used the most (billing, success, support)? What are the common themes? Is one group working more effectively than the others? Does a team need an upgrade? 

(BTW, what costs more, your AWS bill or your payroll?)

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That's where your customer-facing teams are today. Until you can get answers from these systems as easily as an engineer can, you’ll continue to churn, annoy customers, and try to hire your way out of a retention problem. It won’t work.

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The question is no longer about whether you will use AI; it’s when. And no matter where you are on your journey, navigating the ethical implications of AI use is crucial. Ethical AI is not just a buzzword but a set of principles designed to ensure fairness, transparency, and accountability in how businesses use artificial intelligence. In the case of Sturdy, we’ve made ethical AI a core commitment. These principles guide our every move, ensuring AI benefits businesses without crossing the line into unmitigated risk.

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Ethical AI refers to developing and deploying AI systems that prioritize fairness, transparency, and respect for privacy. For businesses, this means using AI to make smarter decisions while ensuring that the data and technologies used do not cause harm or reinforce biases. The importance of this cannot be overstated—AI has the potential to either empower or exploit, and ethical guidelines ensure we remain on the right side of that divide.

Sturdy’s Commitment to Ethical AI

Sturdy's approach to AI revolves around several inviolable principles:

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  3. Privacy and Protection: One of the most critical aspects of Sturdy’s approach is its commitment to not allowing any entity—whether a business or government—to use its technology in ways that violate privacy. If a client were found to be doing so, Sturdy would terminate the relationship.
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Human Oversight and the Role of AI

At the core of Sturdy’s AI principles is the belief that AI should not replace human decision-making but augment it. Our Natural Language Classifiers (NLCs) are built to detect risks and opportunities based on the probability that a conversation indicates a particular issue. For example, when a customer complains about a "buggy" product, Sturdy’s AI might tag it as a "Bug" and label the customer as "Unhappy." However, humans remain in control—analyzing the situation and deciding the best action.

Final Thoughts

Sturdy's approach to AI exemplifies how businesses can responsibly use technology to drive growth and improve operations while safeguarding ethics. They demonstrate that AI doesn’t need to infringe on privacy or replace human decision-making. Instead, AI should be a tool that empowers teams, ensures transparency, and upholds ethical standards. Navigating the ethics of AI is not just a challenge—it’s an ongoing commitment, and Sturdy is setting a new standard for how it should be done.

How many customers will you have to lose before you try Sturdy?

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