The importance of market research is undoubtedly getting more relevant every year, pretty in the same proportion that competition and users’ expectations grow. The insights generated by active approaches, such as quantitative and qualitative interviews, or by the passive ones, like product analytics, are essential for any business to succeed, understanding gaps and business opportunities in the market. No wonder why $94 billion was spent globally in Market Research in 2020 according to ESOMAR, being 30% of that with research technologies. But, as this industry turns 101 years, is there something new that technology and data can bring to the table?
I deeply believe so. Even though market and user research delivers amazing results and insights about consumers and trends, especially when it comes to qualitative user interviews, it is also a process that takes time, requires high investments, and has a limited frequency – showing a photo, not a film.
When thinking about the overarching objective of listening to consumers and understanding what they expect, technology can be an ally of professionals from different customer-related areas, such as Customer Experience, Product, and UX Research, just to name a few. That is possible as these solutions provide them with a way to expand and enhance traditional research tools and frameworks while allowing them to exponentially increase their ability to listen to the voice of the customer and translate that into actionable insights, with the added benefit of faster and broader coverage and an ongoing monitoring of the pulse of the market.
As we watch the acceleration of digital transformation, one key change is how we moved from a lack of data to an excess of data (some call it information overload), where the existence of too much siloed information but too little tools and time to extract value out of them in a useful manner turns 80% of data generated inside companies into dark data. That is especially true when it comes to customer opinions: unstructured text in form of support tickets, NPS survey answers, win/loss CRM analyses, and much more – including user interviews.
For Product and UX Research teams, that means having to manage data from several different sources, reviewing and re-classifying data – and a lot of times not having the time to really read and understand it all. For CX teams, it means manually tagging a lot of conversations to help generate insights to product teams, but struggling to get a good response or a way to respond to your customer suggestion or request. For companies, it means data silos, inefficiencies, and unhappy – and churned – clients.
Qualitative product analytics tools can be the answer to that: they can make it easier to consolidate, categorize, make sense, and share insights about these opinions that were previously disregarded, allowing these teams to collaborate and get a deep understanding of people’s desires while also making it easier to close the feedback loop. We call these Feedback Analytics Tools. These solutions, combined to the already existing Product Analytics solutions (that look into customer behavior) and qualitative interviews, become a powerhouse to implement an authentic customer-centric and data-driven culture as they enable companies to centralize all different kinds of customer feedback.
They do so by giving access to a constant flow of data from public sources (e.g., social, communities, reviews, etc.) and combining it with some of the internal data sources mentioned to create a feedback river and accelerate analysis with automatic feedback categorization, making it easier for Product and CX executives to collaborate.
The benefits of using Feedback Analytics Tools for product-related teams
Combining unstructured data from public sources and private sources with other research & insights materials can help product-related teams – such as Product, Marketing, UX, Sales, and CX – establish a culture of agility, identify growth opportunities, anticipate consumers’ needs, and forecast demand, with a full picture of the user’s experience versus when you rely on a single source of feedback.
When you invite consumers to participate in a focus group or to answer a questionnaire, for example, you are mostly assessing what they recollect from past experiences, scoped according to the questions you ask. Most of the time, it will also be a photograph in time. The same is true for other sources: a product review gives you an idea of their experience with the product, a comment in a discussion forum shows you what their interests and decision-making process are, and so on. So there’s real power in combining data sources.
More than that, having access to consumer-generated data from support tickets, consumer reviews, micro-surveys, etc., opens the possibility of understanding organically what really matters to people, in their own words, and in the context of each channel.
And the benefits go beyond that. Product-related teams who implement a qualitative product analytics solution are more likely to identify unknown needs from the consumers as they get unbiased and non-stimulated feedback. They also reduce the chance of making blind or purely opinionated decisions and can easily identify trends, building an early-warning system that allows them to identify a trending aspect and easily segment it and fire a qualitative survey or interview from it. Finally, they reduce their time spent in operational tasks at the same time they expand their reach by listening to more customers.
These tools can also bring innovation and depth to the current user interview/research approach by helping these teams to design research hypotheses that could be validated later on a large scale or even complementing reactive research methods with a more proactive approach.
More data, less manual work, better insights
Working with huge amounts of data is still a challenge for some leaders who claim they cannot afford to wait as decisions must be made fast, especially when a lot needs to be cleaned to make sense. At the same time, surveys and market research can take time. Using supporting technology means constantly having access to data in real-time and solving the time barrier.
According to MIT research, 79% of business executives want to use data to make better decisions. A better understanding of customers, better products and services, more efficient operations, reduced costs, and new business investments are some values business leaders expect to get from data strategy.
This new approach to analyzing customer opinions can help companies become both data-driven and customer-centric, creating and developing a continuous voice-of-the-customer program that is capable of bringing Product and CX teams together to collaborate more effectively in delivering value to customers in the form of a great feature.