Understanding enterprise “users” means observing and learning from people who are extremely distributed and specialized. Many—from CIOs to purchasing agents to call center reps—aren’t end users. At the same time, the enterprise may be hosting an abundance of diffuse, distributed data about these people. That’s why “insight at scale” is one of the upcoming Enterprise UX 2015 conference’s four main themes.

As the theme’s leader, I interviewed my three speakers—Kelly Goto, Chris Chapo, and Christian Rohrer—to get their take on how to do the right research and synthesize research results in enterprise settings.

The UX world struggles conceptually but still defaults to the word "user." How does the notion of "user" shift in an enterprise context (and have you got a better word)?

Christian Rohrer: I think that “user” is actually a good term when we’re referring to “those who use a product or service.” The term, therefore, has some degree of precision in the Enterprise context because it can be contrasted with “customer”—the person that pays for the product or service. As we all know, in the Enterprise setting, the customer and user are rarely the same person, and I think this distinction allows us to become even more sophisticated about the stakeholders involved, including distinguishing between those who approve purchases, those who deploy products in their environment, and those who use them, ideally using personas or a similar tool.

Chris Chapo: As Christian mentions, most people use the words “user” and “customer” interchangeably. In the consumer world, the person who makes the purchase decision is usually the same person who ultimately uses the product. Great products provide functional benefits but also activate on an emotional level with their customers. Research has shown time and time again that this emotion that leads to long-term customer loyalty. The key difference for the enterprise is how to provide functional and emotional benefits to both end-users and purchase decision-makers, not an easy task.

Kelly Goto: In almost all settings, not just in the enterprise space, we say "customer, audience, or visitor" as a default. Most enterprise-level companies also refer to their segments as small, medium, and large businesses by size as in "SMB's." The most relatable term, as Chris mentions, is "customer"—especially when drawing from the traditional CX world, where marketing has championed the Voice of the Customer for so many decades.

Enterprise settings contain smaller businesses and customer groups at such a variety of B2B or B2C levels it is difficult for these groups, much less individuals to define themselves. In this environment, traditional demographic segmentation becomes nuanced into attitude, behavior, and psychographics as a starting point. And contextual research becomes even more important to understand who these people are, how they live, and how to address real needs.

How is big data impacting decision making (and decision makers) in the enterprise specifically?

Chris Chapo: Big data has been one of the biggest buzzwords since data mining in the early 2000s. Many senior decision makers see big data as a panacea without truly understanding the potential upsides and challenges. The common belief is that big data can be summarized by the “Four V’s”: Volume, Velocity, Veracity, and Variety. The biggest opportunity for big data in the enterprise is in capturing a wide variety of data not historically available. For instance, GPS and sensor data can be used to quantitatively understand worker productivity and safety. Clickstream and usage data can be used to highlight workflows in existing enterprise systems. And the opportunity to create data products for users (such as recommender and help systems) moves the enterprise beyond insights towards action.

Christian Rohrer: Big data is extremely seductive to decision-makers because it adds to their perception of self as an “objective” and “data driven” leader. In actual fact, people exhibit a tremendous amount of confirmation bias, only seeing or emphasizing data that supports their existing beliefs, and this may happen completely subconsciously.

Insights in an enterprise context may play out differently because the goals are more complex

In addition, having big data on their side makes decision-makers feel good because it seems scientific, merely because the numbers are large. For enterprise decision-makers, there is a lot riding on their choices, so the more they can portray them as being objective and scientific, the better they feel about the job they are doing.

Kelly Goto: A lot has been mentioned lately about the lure and the overwhelming nature of big data. With so much micro data coming down the pipeline every second, understanding how to parse, prioritize and pattern the data becomes critical. Today, big data is being replaced with smart data and fast data—extracting meaning and usefulness out of a lot of noise. I am personally a huge fan of approaching research from both qualitative and quantitative perspectives and utilizing big data patterning to establish high-level behavior that gives us the 'what,' and then utilizing qualitative methods to get to the 'why.'

The tricky thing with big data is weeding out the noise, and getting to the information that is relevant and actionable. With a mix of research methods and data points, it becomes easier to map insights gathered through more in-depth research back to data patterns for proof points that are essential within larger organizations to gain the support needed to make necessary changes.

Research and design are often sisters-from-another-mister; how do insights play differently in the design context of the enterprise?

Kelly Goto: The first thing is to define “design” and how that plays out as a practice within the enterprise context. Designers are problem solvers, and the question is, what problem are they trying to solve? Research drives design solutions through data points to help decision making along the way. Analytics are helpful when sending intent, and moving the needle right or left. Research, especially UX research, is the key to solving the actual problems customers are having when using a product or service. Taken at a tactical or strategic level, the influence of research on the design process is what drives success or failure at the experience-level.

Chris Chapo: One of the hallmarks of great design thinking is deeply rooted customer empathy. This empathy allows a team to understand the user’s pain points so well that the best solution is often readily apparent. Groups that are most successful are those that combine multiple cross-functional disciplines, such as design, research, and data, into a team focused on a single outcome. This is particularly important for enterprise solutions where there are many times multiple users and stakeholders for any given product.

Christian Rohrer: Insight types vary greatly, in terms of what they can be legitimately used for, so the answer to this really depends on the source of the data and how it was gathered. Formative (aka evaluative) research really helps designers during the design process, because its often qualitative nature reveals the reasons for a given design’s failure and points to an improved solution.

Insights in an enterprise context may play out differently, however, because the goals are more complex (e.g., simultaneously satisfy the customer, the deploying user and the end user) or having access to these types of customers and users is often much more difficult. That leaves the designers in the dark and with complex, sometimes conflicting goals. The better research can be at overcoming these barriers, the more effective design can be.

UX Magazine is a sponsor of Enterprise UX 2015, May 13-15 in San Antonio, Texas. If you want to learn more about delivering useful, delightful, and humane experiences to people who work enterprises, register now! (Image of elephant's eye courtesy Shutterstock.)

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