Instead, using a combination of qualitative and quantitative methods to identify both what and Whyaccording to two Kellogg School professors, it’s what makes a strategy analysis a useful tool for change.
“Each one has something powerful to offer,” he says Joel Shapiro, clinical associate professor of data analytics at Kellogg. “Quantitative analysis helps you spot broader trends, while qualitative analysis investigates human motivation, but the insights are difficult to scale.”
David Sonthal, clinical associate professor of innovation and entrepreneurship at Kellogg, says the real value is in how these two approaches complement each other. “When you combine data analytics with a deeper understanding of a customer’s motivations and experience, that’s how you create better products and services.”
How exactly is this done? How should companies avoid the misconception that more data provides all the answers and instead combine “qualitative” and “quantitative” to find better solutions to important business problems?
Determine where to focus
When looking for new approaches to a long-standing challenge, collecting and analyzing data such as sales figures or conversions can lead to surprisingly fruitful insights.
“This is one place where quant can really help – just knowing where focusing planning efforts is extremely valuable,” says Schonthal. “Data can act as a source of inspiration, not just a source of validation.”
Say, for example, that a university has a retention problem with its non-traditional student population. A quantitative analysis can determine that women who live far from campus and have young children are at the greatest risk of dropping out. This information is useful—to an extent—in that it identifies who is at risk and where to focus.
But knowing who is leaving is not the same as knowing Why they do, which would help the school know how to solve the retention problem. At first glance, these data may suggest that offering childcare may be an appropriate strategy to enhance retention. But the numbers alone can’t explain whether the retention problem is due to a lack of childcare, poor public transport options, too much homework or something else entirely.
Likewise, data analytics can also make it easier for businesses to avoid tackling the wrong problems, chasing the wrong opportunities, or getting lost in minutiae. If the analysis reveals that new mothers make up a very small proportion of the student body, for example, this can inform a university’s decisions about how much time and effort to invest in recruitment, childcare or curriculum design .
For example, Netflix established the $1 million “Netflix Prize” with the intention of improving its movie recommendation algorithm by 10%. Research teams around the world spent years before achieving the goal – with an algorithm so complex that Netflix has never implemented it. Once the company added user profiles to customer accounts, the accuracy of recommendations increased by well over 10 percent.
“If Netflix had thought about the user interface and the algorithm holistically, rather than as separate functions,” says Schonthal, they would have invested in designing something smart, not in extracting the last few digits from the recommendation algorithm.
Capture the underlying motivations
As companies harness the power of data analytics, however, it helps to remember that even if they find an interesting trend or relationship in the data, they may not fully understand how the variables are related or how that relationship will change over time.
“All predictions are based on past relationships,” says Shapiro. “But the environment is constantly changing. What’s true for Amazon shoppers today may be true tomorrow, but for how long? It’s hard to say. So a business needs to ask itself, “What are all the reasons this might not be true tomorrow or next year?”
Understanding the possible reasons Why One trend that may exist is where more qualitative data methods can often help companies.
Let’s say you work in the financial services industry. You know that banking has changed dramatically over the past two decades, with ATMs, online banking and apps displacing most cash registers. However, a quantitative analysis shows that your bank is still struggling to get customers to sign up “eBanking” accounts..
While data can reveal that eBanking accounts are unpopular, they may not tell you why customers resist eBanking. Is it a lack of trust? Are customers turned off by website design? And just because the analytics show that app users seem happier with online banking than desktop users, that doesn’t mean redesigning the website is the solution. It could just be that app users are more comfortable with all types of e-commerce and online services.
Qualitative analysis – in the form of focus groups, surveys and customer observation – can provide some insight here by examining customer motivations.
What might this look like in practice? Take, for another example, IDEO—where Schonthal also works as Senior Director of Business Planning. The company recently brought together a team of data scientists and designers to help a large travel company reinvent its sales and customer service processes.
An analysis of the travel company’s sales force data found that although every salesperson was working at the same commission rate, few consistently outperformed their peers by a wide margin. However, it was still unclear Why this was happening — and how it could be reproduced.
Through interviews and observation, IDEO learned something interesting: these high performers often ignored the interaction tools and recommendations the company provided. Instead, they used non-approved methods to help build stronger personal relationships with customers, such as connecting with customers on social media and through text messages. This highly personal, somewhat informal approach to customer communication has paid off in the form of substantially increased sales, much higher employee satisfaction, and greater customer loyalty—often for both the company and the sales associates themselves.
Scale your knowledge
However, information derived from individual interviews and observations will not be useful unless a company can determine how applicable it is to most customers. The most effective tool for tracking how people behave on a large scale is quantitative analysis.
“You use ‘quant’ to figure out what happened,” says Shapiro. “You use ‘qual’ to understand why. Then, at some point, you should explicitly test your hypotheses about people’s motivations — to see if they extend to cost-effective solutions.”
This is where analytics comes back into the picture. Returning to quantitative analysis, companies can measure how a potential change might affect revenue, savings, costs, or any other value driver.
In IDEO’s work with the travel company, for example, even after the team learned about the unusual approaches used by some of the most successful salespeople, they had to figure out whether those approaches could help lower-performing sales team members . Can these methods help? anyone improvement, or was it something that only high performers can achieve?
“It’s always a process of triangulating what you learn in qualitative research with the factors indicated by the data,” says Shapiro. “When ‘qualitative’ and ‘quantitative’ are presented as stand-alone methods of analysis, they can lead to bad assumptions. Ultimately, the two should be connected in this dynamic, continuous process of using data to solve problems.”