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Pamela Pavliscak

Data-Informed Product Design

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Use data from a variety of sources to inform your design—analytics, A/B tests, social media sentiment, customer service logs, sales data, surveys, interviews, usability tests, contextual research, and other studies.
Include numbers and context. Whether you call them quantitative and qualitative, studies and nonstudies, or Big Data and thick data, you need the numbers and the context to tell the real story.
Ensure that data is sensitive to the complexity of the human experience. Use averages sparingly, infer with caution, corroborate liberally.
Use data to track changes over time, explore new patterns, and dig deeper on problems
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No process is one-size-fits-all, though. Depending on the goal, different combinations of data sources might be more actionable.
For acquisitions, you might want to pair analytics and competitive data from a source such as Alexa or SimilarWeb. To understand content strategy, combining specialized analytics from Chartbeat with intercepts might be the way to go. Understanding the recommendation cycle might require a combination of NPS scoring with interviews and social listening. The key is to create a multidimensional pictu
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Bringing in more sources of data can reduce bias, but all data has some kind of bias.
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There is no perfect data. You always must ask where data comes from, what methods were used to gather and analyze it, and what cognitive biases you might bring to its interpretation.
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Big Data looks backward, A/B tests seem to focus on the small stuff, and analytics just skim the surface. There is some truth to all of these observations, but the core issue is not in the data itself; rather, it is in how it’s being used.
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Big Data on its own is often not enough to drive innovation. It can lack context. And, more important, it can lack empathy—a strong tradition in the design community.
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Data can reveal patterns and trends to drive innovation.
We can use data to incrementally improve the product experience.
Data can measure success, whether tracking across time, across versions, or against competitors.
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UX is about more than just ease of use, of course. It is about motivations, attitudes, expectations, behavioral patterns, and constraints. It is about the types of interactions people have, how they feel about an experience, and what actions they expect to take. UX also comprehends more than just the few moments of a single site visit or one-time use of an application; it is about the cross-channel user journey, too. This is new territory for metrics.
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Many experts will argue that Big Data, for all its power, can leave big questions unanswered, such as why people take action or why they don’t. Big Data is still mostly about the what, and less about the why, these same experts will argue. What they mean is that Big Data has a harder time understanding the reality of lived human experience in all its complicated glory, and understanding this reality of lived human experience is where the real insights lay.
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If people are highly aware that they are being studied, the data is inherently different than if they do not.
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Quantitative includes data from tools such as Google Analytics, in-house studies such as intercept surveys, and even third-party benchmarks such as Forrester CX scores or Net Promoter Scores (NPS).
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Analysis of thick data tends to be less systematic and more intuitive. Some researchers transcribe and code the interviews and artifacts, using that as a way to formulate themes, although much design research can bypass this step. This doesn’t make the research itself less relevant, but it might make it seem less credible in some organizations.
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Big Data is the archeology of user experience or the study of the traces that people leave behind, thick data is more like anthropology, exploring lives as they are being lived online.
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When it comes to data, we tend to think in dichotomies: quantitative and qualitative, objective and subjective, abstract and sensory, messy and curated, business and user experience, science and story. Thinking about the key differences can help us to sort out how it fits together, but it can also set up unproductive oppositions. Using data for design does not have to be an either/or; instead, it should be yes, and...
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There are strategic decisions about who will use the product, how it will fit into these peoples’ lives, or why they would start using your product at all. There also are tactical decisions about what language speaks to the people using the product, what catches their attention in a positive way, and how to make the experience easier.
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Will the people you’ve identified actually use your product? Will they keep using it? Will they recommend it?
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Christian Rudder writes in Dataclysm: Who We Are (Crown), “It’s like looking at Earth from space; you lose the detail, but you get to see something familiar in a totally new way.” Data yields a new perspective. When data is used in the right way, it can reveal to us new things about people, and people are at the core of designing any product.
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The choice is not really data or intuition, but data and intuition. Then, the question becomes something else altogether. Which data? How do we balance data with design practices? And whose data should we use anyway? These questions are the crux of what this report addresses.
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there are plenty of great books on how to derive consumer insights from data (Data-ism by Eric Lohr [Harper Business] or Data Smart by John W. Foreman [Wiley]) and even more books on using user experience (UX) research to inform design (It’s Our Research by Tomer Sharon [Morgan Kaufmann], Just Enough Research by Erika Hall [A Book Apart], Don’t Make Me Think by Steve Krug [New Riders]), but there is not much information on how to bring it all together to design products and services.
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