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All posts tagged “data”
At the UX Poland Conference, presenter Jeff Parks, said: “Without research, businesses cannot make informed decisions.” By analyzing data, entrepreneurs and UX professionals get information that will help them develop more efficient and profitable products and services.
Contextual analytics is a great way to get the most out of big chunks of data. Facebook and Twitter, for example, are excellent sources of data, but detecting patterns in order to make sense of all that available information is incredibly time-consuming and complicated. Going a step further, once we make sense of the data, we need to analyze it in order to create valuable insights, and that relies on having a specific context in mind – otherwise we end up with nothing but generalized conclusions.
In short, applying contextual analytics can help emphasize the individuality of the consumer and his or her behavior.
What is big data?
To start with, big data is called “big” for a reason – we’re talking terabytes and terabytes of information flowing into companies every day. Regular data becomes “big data” when it is large enough that it cannot be easily processed using conventional methods. With this volume, spreadsheets are useless; they lack flexibility and scalability. However, once processed, that big data is very valuable. With the context provided by analytics, big data can highlight key metrics, allowing UX professionals to create tailored solutions for their users.
How is all this relevant to today’s businesses? There are three specific ways in which big data can lead to better products and a better user experience—when viewed with context:
- Big data allows us to expand our knowledge of the customer and develop products and services that are best suited to their needs.
- Big data gives us a deeper understanding of how our customers behave, allowing us to connect with customers on a more meaningful level.
- Big data can help boost marketing activities, since it provides us with a chance to analyze customer behavior on multiple channels and understand when the customer is most likely to buy products or services.
In this article we’ll take a look at how to make the most of analytics, and why context is so important for big data.
A few years ago, one of my customers at UsabilityTools used our Form Tester tool to learn how users behave on his website. The Form Tester tool allows us to analyze the way website visitors interact with online forms. It provides data about each field of the form, identifying steps that cause dropouts.
The first thing that we noticed (thanks to the tool) was a high bounce rate, which indicated that something might be off with the form. In order to analyze the problem, we started to look at different elements of the form. We noticed that the usual response time was about 5 seconds, but one particular field took users over three minutes to fill. Having found a problem, we put it into the context: the field required an ID card number, which meant the user had to leave his desk, look for his wallet and come back to copy the data from the card. That explained the three minute wait, and showed us that we didn’t need to fix a problem with the form, so much as prepare users for needing their credit cards, so that they wouldn’t leave when they encountered this more “strenuous” task.
Contextual analytics is what allows businesses to trace patterns and detect trends like we did. It helps designers to build predictive models and prepare a suitable business strategy. The context is what makes the difference between “big data” and “dumb data.”
What’s wrong with no context?
Most companies in the digital industry already have some web analytics software implemented. But that doesn’t allow them to fully understand the psychological and cultural factors that influence customer lifestyles.
From the perspective of a business, the typical elements of web analytics, such as page views or the bounce rate, which provide data can actually lead to conclusions and mission-critical insights that are simply wrong. It’s easy to see how plain numbers can lie, especially when taken out of their context. Let’s take the “average time on site” metric. Five minutes as the average time looks pretty solid as an average, but when we look at individual visits, we suddenly see that the majority of visitors spend only ten seconds on the site, and the average metric is distorted by a few prolonged visits!
The realization that we can’t blindly trust data has been circulating for a while now – in an article entitled “What Data Can’t Do,” David Brooks of The New York Times points out that the main problem of big data is that it’s “pretty bad at narrative and emergent thinking, and it cannot match the explanatory suppleness of even a mediocre novel.” The best way to deal with this problem is to follow the words of Scott Gnau of Teradata Labs: “big data is a new piece, but it is not the only piece of the data puzzle.” Context and context-derived analytics can unlock the potential stored within big data; by contextualizing the data at hand, we can do things like improve our customer insights and identify the reasons behind common consumer behaviors. From here, businesses can create experiences that actually surprise and delight their users.
House of Cards is a popular political drama featured on Netflix, and perhaps one of the best examples of big data’s influence. Netflix actually uses big data to customize the House of Cards plotline and character twists. As Salon reported, if a user is watching the first episode and pauses it to get a snack, Netflix records the pause and the play. It’s impossible for Netflix to determine the reason why viewers paused the episode, but they can ask and assume – why do people pause at that moment? Is it because it’s shocking, repulsive, captivating or simply boring? Why do so many people rewind to exactly fourteen minutes into the episode? Is it because something is difficult to understand, or is it because the scene was amazing? Finally, why do viewers stopped watching the episode half-way through? The reason could be simple: the show was just bad.
By looking at the scenes during which these events (pause, rewind, stop) happen, the analytics team puts the events in context, and the results of their analysis are used later in order to improve the future viewing experience. Currently, according to rottentomatoes.com both seasons of House of Cards have received ratings well above 80%—proof that the series is successful. By putting big data in context, Netflix has prepared, and will improve a show which kept people glued to the screens.
House of Cards is an extreme example, but the same principle applies to any experience. Online delivery app Foodler recommends “best bets” to users based on previous items they have ordered from similar restaurants. They could go a step further and analyze their data within the context of time of day, and begin recommending breakfast-, lunch-, or dinner-specific foods at the appropriate times. Similarly, Target uses contextual big data to identify changes in customer behavior—this is how Target famously learned that a customer was pregnant before she had even told her family.
Knowing the why behind the data is what is really valuable. This context explains the psychology behind consumer behavior and consequently influences our ability to develop marketing strategies that successfully reach users at key touch points.
The Role of Context in Prediction
There is every reason to think that getting the hang of accurate models and patterns is key to boosting the analytics decision-making processes in the big data environment. Contextual analytics feed predictive analytics and produce a perspective on how people actually behave, crucial to building great predictive models.
We can use contextual analytics to emphasize data usability and business relevance. This allows us to create models that predict the future behavior of consumers, such as when Amazon recommends additional products.
Contextual analytics can determine, for instance, whether different data observations can be subscribed to a single individual, providing the context for the accurate merging of data to form real associations. For example, an e-commerce owner using predictive analytics will note that many customers purchase shoes on Friday afternoons, but contextual analytics will allow them to see that most of these customers are in office buildings, and are more likely to purchase when waiting for a client or meeting (in the last five minutes and first five minutes of the hour).
|Data driven context||Possible actions|
|Repetitive consumer habits (e.g. buying a certain type of product or purchasing at certain times)||Suggest certain types of products or display offers during the purchase.|
|Other consumer habits (e.g. products purchased by people with similar habits/demographics)||Suggesting products that other consumers picked|
|Context derived from outside data (e.g. determining user’s hobbies based on their mobile apps)||Suggesting products concerning trending topics/people (e.g. recently awarded movies, deceased musicians)|
In a study of contextual analytics, Lisa Sokol and Steve Chen of IBM created another example, involving the traditional scoring system banks use to determine whether a client is eligible for a loan. If the bank uses analytics, they stated, it will see every account from every bank, but it won’t be able to associate all the different accounts from several banks to one person and, in consequence, will have to base its decision on imprecise information. With contextual analytics, on the other hand, the bank is able to see that those several accounts belong to one person and so will have all the necessary information to accurately evaluate the client’s ability to pay back a loan.
By taking advantage of context-driven analytics, we can increase the efficiency of prediction models and make better business decisions as a result.
Recognizing the benefits of using context in big data analytics is only the first step. Once we start gathering it, we’re ready to look for contextual insights and, as a result, create better customer experiences. Here are a few ways to get started.
- Study the data and KPIs that others are using, to better understand which are most relevant to the field.
- Don’t trust average values! Consider the context, whatever the metric is.
- Read more about the contextual analysis of big data:
- Age of Context: Mobile, Sensors, Data and the Future of Privacy by Robert Scoble
- The Human Face of Big Data by Rick Smolan
- Data Crush: How the Information Tidal Wave is Driving New Business Opportunities by Christopher Surdak
- Big Data Geeks blog
- Follow influencers on Twitter who often post useful articles on big data, such as Bob Gourley, Tony Baer or DJ Patil.
Following hard on the heels of its $ 60 million deal with Genentech, personal genetics startup 23andMe has announced an agreement to share its user data and research platform with pharmaceutical giant Pfizer. Although 23andMe is still languishing under FDA restrictions (the company is only permitted to offer ancestry reports and raw genetic data to customers — not medical analysis), its well-organized database of some 640,000 genotyped individuals is proving popular with the medical industry.
“The largest dataset of its kind”
In a press statement announcing the deal, 23andMe spelled out the attractions of its genetic resources: “Researchers can now fully benefit from the largest dataset of its kind, running queries in minutes across…
Once our Yelp research was complete, it was time to analyze the findings and determine the major pain points users experienced with Yelp’s current site. We began by watching our UserTesting videos and making note of interesting moments. (UserTesting’s platform allows you to annotate videos and create video clips directly from your dashboard.)
Image Source: Which UX Methods.
As discussed in The Guide to Usability Testing, there are a wide range of user research options ranging from resource-intensive usability lab studies to simple email surveys. Our screen-recorded user tests provided us both attitudinal and behavioral insights since we could hear what users thought (attitudinal) as well as see what they did on screen (behavioral). We’ll explain why qualitative research matters, explain our takeaways, and show how we wove them into the new design.
The Right Approach to Qualitative Analysis
When it comes to qualitative analysis, it’s not enough to just ask users to recount their experiences. As Jakob Nielsen, Partner at the Nielsen Norman Group, points out, the first rule of usability is to never listen only to what users say. The wrong approach would be to create a few designs and then ask users which one they like the most — users haven’t tried the design, so they can only comment on surface features.
The right approach to qualitative analysis, and the one that we are champions of, is to examine user behavior and then ask them the Single Ease Question. This process helps to eliminate cognitive biases and gets to the bottom line of UX analysis: how did the users accomplish their tasks, and how easy or difficult was it? Our screen recording also captured audio (and we encouraged people to think out loud), because otherwise it’s easy to miss why certain behavior occurred. The “why”, after all, is the most important part of user analysis.
Analyzing Qualitative Results
Distinct patterns emerged in our observations of user interactions with Yelp (we explain these patterns in greater detail in User Testing & Design). Overall, we learned that the Search bar was one of the most essential features, and it was easy to use if the users knew exactly what they were looking for (if they knew the name of a business, for example). Other features weren’t as intuitive, though, as you’ll see in our discussion below.
1. The Search function was the primary starting point for any task
All five test participants relied heavily on the Search bar, even for tasks that could easily be completed by browsing through the Categories instead (such as finding an interesting restaurant or bar without being given any specific parameters).
In fact, four out of the five participants went straight to the search bar to find a restaurant. Only one user started browsing through the categories, and she quickly found them “overwhelming” and ended up resorting to the Search bar instead.
The Search bar was the most intuitive feature for users.
Yelp’s categories were “overwhelming” and less helpful than the Search bar.
Note: in our test instructions, we asked users to “find” a restaurant, not to “search for” a restaurant, because we wanted to observe how they would naturally go about this task without biasing them toward a specific function.
Interestingly, when the users were given specific parameters (like the budget, ambiance, and type of restaurant, or the name of an individual business) they almost universally ignored everything on the homepage except for the Search bar. Knowing this, we realized it would be very important to make the Search bar the most prominent feature on the redesigned site.
2. Events were not very noticeable
In one task, we asked the two users without Yelp accounts to find an interesting event in their area this weekend. We wanted to learn whether they would use the Events tab at the top of the page.
The Events tab is easy to miss.
Surprisingly enough, nobody used the Events tab. When asked to find an interesting event in their area this weekend, one test participant used the Search bar while the other navigated through the Arts & Entertainment category in the Best of Yelp section.
We learned that if we wanted users to actually interact with the Events feature on Yelp, we would need to make it easier to find.
3. Bookmarking was frustrating, and no one used Lists
We were curious to see how users would choose to save locations for later reference. In Yelp, there are two ways to do this: users with existing accounts can either bookmark a location or create a list. We simply asked Group 1 (three users with Yelp accounts) to “save” a number of locations to look into later so that our wording wouldn’t mention any features that could bias their actions.
Of the three users who were given this task:
- One saved the businesses using Bookmarks but complained that the process took a long time.
- One started to save businesses using Bookmarks but gave up because it took too long.
- One was not able to figure out how to save businesses and gave up on the task.
The two users who used Bookmarks both remarked that it would be nice to be able to bookmark a business from the search results page, rather than having to go to each business’s page separately, as you can see in the video below.
Click the “Play” button to hear user thoughts on the Bookmarks feature.
It would be nice to allow users to have an easier and more intuitive method of saving businesses to return to later, so we prioritized bookmarking in our redesign.
4. Searching for a specific venue was extremely fast and easy
All five users were given a task to find a specific business to find out if it was open at a certain time. They all successfully completed this task, and rated the task as “Very easy”. As mentioned previously, all five used the search bar to accomplish this task.
Since searching for a specific business is working so well, we decided not to change anything about the way Yelp has designed this functionality.
5. Users relied on photos to determine the ambiance of a restaurant
When asked to find a restaurant with a certain ambiance, none of the five users attempted to use the search bar. Instead, three users looked through photos of the restaurant on Yelp, one visited the restaurant’s website, and the last stated that the price symbols ($ ,$ $ ,$ $ $ ,$ $ $ $ ) was enough to indicate if the restaurant had the right ambiance.
Click the “Play” button to hear user thoughts on determining restaurant ambiance.
This brought up two insights:
- Photos are an essential part of the Yelp experience, and they are critical for users to choose a business.
- Ambiance doesn’t play much of a role in Yelp’s search or filtering functions. We decided that, in the redesign, we could either include a filter for types of ambiances, or we could make it more expressly clear that using the Search bar will search for keywords in reviews as well as the name and type of business, so Search could be used to identify ambiance, menu items, and more.
6. Users relied on filters, but they could be improved
In the task where five users were asked to find a restaurant for a group of 15, three of the five participants used the “good for groups” filter, while one used the “make a reservation” feature and scrolled down until she found a restaurant that could seat the group.
At another point, one user attempted to select two categories to filter his results, but one of his choices disappeared when he clicked the other. (See the video below.)
Click the “Play” button to hear user thoughts on using Filters.
While filters are important, we learned that they could be greatly improved. This finding inspired us to run a card sort on all of Yelp’s current filter options to determine which ones are actually useful to users.
7. The price categories weren’t clear
When users were searching for the restaurant with specific parameters, one of the requirements was to find a restaurant within a $ 20/person budget. Two of the five users were confused by whether their $ 20 restaurant budget would fall into the $ , $ $ , or $ $ $ category. One user stated that she didn’t know what the symbols meant, and another clicked the wrong category. The other three correctly chose the $ $ category.
The definition of the symbols does not display when users select filters; it only displays when the user navigates to a particular restaurant’s page. Since price expectations are highly subjective, it was unclear to users which category they should choose.
With this insight, we decided that in our redesign, we would need to be more explicit about what each dollar symbol indicated.
Design Based on Usability Testing
Once it was time to design, we followed an approach based on the last few steps of the Google Ventures design process. UXPin CEO Marcin Treder first started with many informal sketches before a team decision helped cull it down to the top 2-3 sketches. To prevent design by committee, Marcin had the final say regarding which sketches would progress into wireframing and prototyping with UXPin.
After moving into UXPin, we created a wireframe to incorporate most of the design changes, then added some interactions and animations to turn it into a low-fidelity prototype. Once the animations were smoothed out, we added detail in UXPin for a high-fidelity prototype. Screenshots from the process are shown below.
2. Low Fidelity Wireframes
Image Source: Yelp Redesign.
Image Source: Yelp Redesign.
3. Low Fidelity Prototype (click to interact)
To click through a few interactions and browse the entire design, you can play around with this in the Live Preview.
Image Source: Low Fidelity Yelp Prototype.
4. High Fidelity Prototype
To click through a few interactions and browse the entire design, you can play around with the high fidelity prototype in the Live Preview.
Homepage (click to interact):
Image Source: High-Fidelity Prototype.
Search Results (click to interact):
Image Source: High-Fidelity Prototype.
Watch, Listen, Learn
What users say and what users do should serve as checks and balances during user testing. While you don’t need to necessarily be present during the test, an audiovisual recording is mandatory, otherwise you might miss out on the context of actions. When you combine qualitative analysis with quantitative analysis , you’ll get an even clearer idea of why and how to fix a problem, as well as how many usability problems your design needs to solve.
To learn more about how to incorporate cost-efficient usability testing into your designs, check out the free e-book User Testing & Design. You’ll find 109 pages of screenshots and tips, using the Yelp redesign exercise as an example.
The post Turning Qualitative User Data Into Actionable Design appeared first on Speckyboy Web Design Magazine.
Read more about The 36 best tools for data visualization at CreativeBloq.com
It’s often said that data is the new world currency, and the web is the exchange bureau through which it’s traded. As consumers, we’re positively swimming in data; it’s everywhere from labels on food packaging design to World Health Organisation reports. As a result, for the designer it’s becoming increasingly difficult to present data in a way that stands out from the mass of competing data streams.