AI-supported market research for a more efficient approach

While conventional quantitative surveys, for example surveys, limit the type of feedback through predefined questions and limited response options, qualitative methods such as video interviews can do the opposite – but they produce so much information that it becomes very time-consuming to analyze it.

Not to mention that few researchers have the opportunity to watch feedback videos manually and repeatedly to create a comprehensive overview of the complex thoughts and opinions of individual users.

Thanks to advances in the field of artificial intelligence (AI), market researchers now have a wide range of tools and technologies at their disposal that do much of this work for them. This also benefits experience management (XM) in particular. Experience management is a comprehensive approach that aims to collect feedback from customers, employees and other stakeholders via various channels. Companies then use the know-how and insights gained to design and deliver the best customer, employee, product and brand experiences. In other words, they use the XM approach to design new experiences – based on holistic market research that uncovers new customer and employee needs. If the market or needs change, the customer, employee, product and brand experience will also adapt accordingly. When used correctly, this not only results in more efficient working methods, but also deeper insights into the entire experience management area in companies.

Specifically, an ideal use case for the integration of AI in market research is as follows: With the help of smart, AI-supported tools, researchers can analyze an entire library of video clips at lightning speed and immediately create a summary of the most important findings. Thanks to AI, key topics and verbatim quotes can also be extracted directly. Experts can thus ensure that each summary is relevant to their specific research case. Whether the video footage is 20 minutes or several hours long, AI technology transforms a tedious and time-consuming task into an easy-to-use source of in-depth insights – in seconds.

Root cause analysis

AI can not only summarize how participants in a qualitative survey feel about a product or experience, but also discover areas of opportunity and potential for improvement. For example, the latest tools already allow researchers to ask follow-up questions to a previous evaluation to the AI, such as “What are the top five pain points that customers highlighted in their reviews?”. For example, perhaps reviewers are unhappy with the way a product is constructed. Modern AI-powered tools allow for far-reaching insights, so that with no less than a double-click on the feedback, professionals can find out that the main complaint concerns, for example, the position of the navigation elements of a website or product, or that a company’s product is not stable enough. These features offer an unprecedented level of detail and provide direct insights that can be acted upon quickly within the organization.

Emotional insights

Although the added facial expressions, tone of voice and nuances of video feedback can provide a wealth of information, there are still cases where a traditional survey is the most effective means of capturing feedback. But even these surveys can contain a large amount of unstructured data, such as open-ended responses, which in the past required a lot of manual labor. With the development of powerful AI-powered text analytics tools, there is no longer a need to analyze every survey response in detail because AI instead reveals the truly nuanced, human statements. Rather than simply categorizing feedback as positive or negative, AI technology can uncover comments that specifically relate to three core elements, namely actionability, effort and emotion, including emotional intensity – without asking a single question to evaluate effort or explicitly asking someone how they feel about the product or service.

Let’s take a closer look at each of these dimensions:

• The analysis shows concrete activity indicators that help business decision-makers to derive and take direct action to improve the customer or employee experience. These include requests for help (e.g. “My payment is not being processed, I need help as soon as possible.”) and suggestions for improvement (e.g. “It would be great to have a more accessible option.”).
• Individual effort (effort analysis) highlights points of friction based on how someone describes the experience (e.g. “It was an absolute nightmare getting help from your customer service.” as opposed to “It was a breeze getting my new debit card.”).
• Emotions and emotional intensity not only help to understand what respondents are feeling, but also how intense these feelings are. However, it is only when taken together that these two indicators are of crucial importance in recognizing polarizing experiences and developing empathetic responses, for example in customer service, sales or support.

Intelligent research

In an environment of tight budgets and capacities, an often untapped resource for strategic insights is a company’s existing digital research library. From market and product data to brand values and customer and employee feedback. Many companies have years of studies and insights that are virtually untapped. Until now, searching through this data has meant tying up a lot of resources over a long period of time. AI, on the other hand, can now help to access this dormant wealth of knowledge without a great deal of personnel effort and display all relevant findings that companies have already collected on a specific topic or question in a targeted manner. This also means that studies that would otherwise have been undiscovered or forgotten can be put to good use again. This allows market researchers to build on previous investments instead of having to make them again.

However, AI insights are heavily dependent on the way in which AI is used to generate high-quality answers. It will be crucial to refine superficial answers into qualitative answers in terms of content and to provide the resulting instructions to the departments that interact with the customer.