We live in a quantitative world. Quants rule Wall Street. With Big Data, AI and Machine Learning, corporate decisions are increasingly driven by quantitative data.
Is this a fad or a permanent trend? Where does qualitative data fit into this landscape? Is it a technology of the past, eclipsed by newer methods?
At Silicon Valley Research Group, we continue to value the power of qualitative data. And while we are, and always have been, a market research company providing both custom qualitative and quantitative research to companies in the technology sector, the new world of AI and deep learning is not causing us to abandon qualitative methods and data. In fact, we now embrace this aspect even more, as we have come to realize the critical role qualitative data plays in an increasingly quantitatively driven world. In fact, we take the position that relying solely on auto-generated quant data is dangerous and augmentation with qualitative data is valuable insurance against decision making based on erroneous conclusions. As while the quantitative portion of our business has experience significant growth as expected, as our recent clients will attest, we always augment our research designs with a qualitative phase or component. Here are the top reasons for doing so.
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While decision makers are comfortable with correlations found in data, causality is implied in conclusions from quantitative data. Qualitative data helps verify and eliminate erroneous correlations where causality does not exist. Uncovering the correlation enables you to derive tactical advantage from that correlation such as promotions that lead to increased short-term sales; understanding the causality behind the correlation leads to strategic advantage-uncovering underlying motivations and emotional triggers for a behavior resulting in revamping customer experience, creating new offering/revenue streams, etc.
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Qualitative data provides a deep lens into the customer journey. Customer anthropology on how customers integrate your products into their lifestyles, habits and practices provide invaluable insights into product and customer experience design. Quantitative data misses these important components. Quantitative data focuses on data around customers’ consumption of a company’s products. Conclusions from such data can be one-dimensional and miss salient points on customer preferences for design and experience. Customers don’t just buy your product, they integrate them into their respective lives and an over-reliance on quant data misses this vital component.
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Qualitative data also performs a vital function in exploring “white space” opportunities not otherwise apparent of previously considered. Starbucks observed a spike in in-store consumption mid-morning driven by young mothers meeting up with babies in tow in strollers, requiring smaller stores with narrower aisles to rearrange furniture to accommodate strollers to create a more satisfying customer experience for this market segment.
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More recently, there has been a trend among companies to capture an additional metric along with NPS (Net Promoter Score). The CES or Customer Effort Score quantitatively measures the level of effort a customer needs to expend to solve problems relating to their consumption of your product or service. The measurement is taken at the customer’s touch point with your company’s customer service and support personnel. Here again, qualitative data comes to the rescue to complete the picture and comprehend the private struggles customers have with your product (opening packaging, for example) that will not show up in the Customer Effort Score.
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Observational data enables companies to hear what customers say-emotional attachments or conflicts with aspects of your product, unarticulated needs, workarounds they engage in while using your product-till a competitor comes around with an improved design that solves for the workaround! As Theodore Levitt famously said in a Harvard Business Review article, “people don’t want a 1.4-inch drill, they want a 1.4-inch hole”
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Qualitative data performs functions at the start of the product development better than most quantitative methods. While quantitative data may have been responsible for initial identification of the gap or need being explored, qualitative methods, such as focus groups or in-home and in-workplace ethnographies are best suited for translating needs into product concepts.
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While qualitative data does not have the “cool factor” of spreadsheets and Tableau style dramatic data visualizations, it can be very impactful in the boardroom in presentations to executive decision makers. Independently obtained customer quotes, verbatims and video testimonials provide important color and context that executives seldom experience first hand.
Alan Nazarelli is President and CEO of Silicon Valley Research Group, a global market research and strategy development firm focused on the needs of technology companies.
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