Voice-of-the-customer (VoC) data has come to represent a broad spectrum of data streams, from traditional consumer research, convenience surveys, comment cards, and call center verbatim, all the way through to social media. Too often, similar expectations are placed on these data streams, even though the nature of the information varies enormously. Many times the data is positioned as similar or in competition, and businesses awkwardly apply the same analytic tools and reporting structures on data streams that are wholly divergent. But each data stream offers a unique type of feedback, which should be appropriately applied to your business.
To help organize your data and manage stakeholders' expectations, it is a useful exercise to plot your VOC data along two dimensions—representative to user-initiated, and as either structured (focused) or unstructured (open ended). This outlook helps to quickly organize the tools and processes available.
Use representative data to measure strategy
Of all the types of data collected, representative data, based on random sampling, is the only data that should measure the success and failures of strategies and drive strategic changes. Why? Because representative survey methods give equal opportunity to all opinions, and while a true random sample is a theoretical goal, by paying attention to collection design, you will find that your results will be more reliable and you will be able to have more confidence in the strategies you implement.
Focused representative research is important for clear customer insights around key performance indicators (KPIs)—such as measuring purchase intent, satisfaction, or brand. This is the type of data where statistics apply, and it is from this data that testing, performance tracking, and predictive analytics can be done. From this, programs and strategy can be quantifiably evaluated.
Within representative sampling, if you make the research very open-ended, you can give customers the freedom to add context or opinion. This is exceptionally useful for exploration, as well as understanding needs, desires, and performance gaps. Data mining and pattern recognition tools can help reap the benefits of this data. This approach can serve as an invaluable source of new hypotheses that can drive business innovation.
User-initiated feedback can drive process
User-initiated feedback, such as comment cards, is very different than representative feedback, as it is not random. Research shows that users who are motivated to provide feedback unprompted generate data that is heavily weighted toward the negative. If companies use this data to make strategic changes, the unbalanced nature of a nonrepresentative sample can cause companies to make poor decisions based on the misguided zeal to please a few vocal users.
Although this type of data does not serve the same function as representative sampling, its temporal nature demands quick follow-up, and it is an invaluable source for individualized information with which the business can save, convert, or nurture vocal customers. This type of feedback may generate volumes of data and have the trappings of representative data, but it should be viewed as an advisory service, with an opportunity for businesses to actively engage this important user segment and use it to create studies to test broader strategies.
User-initiated feedback that includes open comments or verbatim data provides the largest net from which to capture as much feedback as possible to try and improve the business. It has the unique challenge of searching for relevance among the noise. Identifying specific problem areas lends itself to data mining and pattern recognition techniques. New big data processing and machine learning techniques derived from search have significantly increased a business' ability to take advantage of this data.
Structured individualized feedback provides more direct focus. The most extreme example is product ratings or likes, which can give a simple comparison and are useful to other users considering the same product or service. However, this can only provide so much insight. Focused feedback through simple links or forms can provide targeted feedback for the business and should be applied to specific applications or processes to categorize issues or needs. Understanding specific issues in the checkout procedure is an example of exceptionally tactical bug-fixing along with a clear opportunity for sales management.
Keep an eye on social data
Social data is an interesting beast. It is by definition individualized unstructured feedback. Managing support issues, fostering loyalty, and nurturing advocacy are key use cases from social data. Because social data occurs in such volume and its penetration is growing, many are trying to figure out whether it can be leveraged as market research. For now, it is best to keep representative tracking in place for those big decisions and important KPIs, while beginning work on contrasting results from social data with standard methods so you can decide what elements can be absorbed. While social data may prove very useful on the unstructured side of representative, it will take more effort to replace the testing and monitoring side.
Embrace multiple data streams for a competitive advantage
Leveraging the right data for the right issue will ensure solid decisions are made. Strategic decisions based on a skewed sample will not help your business grow; for this, you want a representative sample. At the same time, you cannot afford to ignore a clear client problem that is costing you revenue opportunities or customer retention. That's why it is imperative to build in many points of contact to collect the voice of your customer from surveys to comment cards and take the opportunity to know your customers better than your competition. Also, the more aligned you are with when your customers want to give feedback, the more relevant your data will be. So embrace multiple data streams and don't make them compete for attention.
Lane Cochrane is the vice president of research at iPerceptions, a specialist in voice-of-the-customer analytics. Cochrane has more than 15 years of management and business development experience in the market research industry, including a decade spent at The Nielsen Company.