Predictive User Experience

Methods of Prediction

There are various predictive models. In this article, I’ll cover decision trees and linear regression.

Decision Trees

Use a decision tree to identify the class, or category, to which a target variable belongs. A decision tree is a graph that uses branching to illustrate every possible outcome of a decision. [1] To explain how a decision tree works, I’ll use the example of a music app’s deciding what song to play next based on certain predictors. A decision tree uses categorical variables for classification. Table 1 shows the training data for this scenario. To keep this example simple, I’ll provide only five predictor variables that influence the decision to play a song.

Table 1—Training data for a music app

Time	Age	Weather	Emotional State	Song Genre
Morning	Young	Summer	Sad	Melody
Evening	Young	Rainy	Delighted	Rock
Evening	Middle aged	Winter	Angry	Jazz
Night	Senior	Rainy	Disgust	Disco
Afternoon	Middle aged	Summer	Sad	Country
Night	Senior	Winter	Disgust	Pop
Morning	Young	Summer	Sad	Melody
Afternoon	Middle aged	Winter	Angry	Rock
Night	Middle aged	Rainy	Delighted	Country
…	…	…	…	…

A classification algorithm derives from this training data and is applied using the following classification rules.

If time == “morning” && age == “young” && weather == “summer” && emotional state == “Sad” then play == “Melody.”

In our fast-paced marketplace, deciding what new product customers would want presents a major challenge to product-development companies. Creating and launching a new product is a big investment, and there is no guarantee that it will be adopted by consumers. You could use a decision tree to help you decide what new product to develop.

Linear Regression

In data mining, regression predicts the value of a target, or numerical variable, by building a model based on one or more predictors. In essence, linear regression shows how variations in an independent variable change the dependent variables. Table 2 shows an example of a linear regression with multiple predictor variables, in which a company is deciding its hiring policy for sales professional and wants to determine whether two independent variables—in this case, a candidate’s years of education and score during the interview process—would predict their ability to generate sales.

Table 2—Predicting a candidate’s ability to generate sales

X1 (Independent variable)	X2 (Independent Variable)	Y (Dependent Variable)
Years of Education	Score in the Interview	Sales generation in a year
12	32	350000
14	35	399765
15	45	429000
16	50	435000
18	65	433000

Analyzing the correlation between each variable resulted in the following output:

rx1, x2 = 0.9682, rx1, y = 0.8799, rx2, y = 0.7722, in which the correlation for these two variables is 0.935.

The formula for multiple regression is:

Y = a + b1 x1 + b2 x2, in which b1 is 34,356.085, b2 is -3,657.213, and a is 60,049.195.

Now, to predict sales generation for a person with 13 years of education and an interview score of 49, you can apply this formula:

Y = 60,049.195+ (34356.085) X1 + (-3,657.213) X2

Y = 685,881.737

Linear regression is a very powerful statistical technique, and you can use it to generate insights on consumer behavior, forecast sales, and analyze survey data. UX researchers use linear regression extensively in analyzing the results of usability testing.

Leveraging Predictive Models in UX Design

Conversions are one measure of an effective user experience. Using predictive analysis, you can improve conversion rates on an ecommerce site. For example, recommendation systems use data such as consumer interest and buying behavior, then apply a predictive model to generate a list of recommended items.

Amazon uses an item-item collaborative filtering algorithm to recommend products and, thus, can show programming books to a software engineer and baby toys to a new mother. In the future, data will drive the user experience. Patterns that derive from data form the basis of a robust predictive engine that can predict a user’s intent by aggregating several predictors that together influence conversions. Quantum Interface, a startup in Austin Texas, claims to have developed the world’s first and only dynamic, predictive user interface by leveraging natural user interface (NUI) principles. This dynamic, predictive user interface uses the directional vectors speed, time, and angle change to anticipate a user’s intent.

The Importance of Prediction

The disruptive nature of new technologies impacts millions. An unanticipated consequence of such disruptions has been the elevation of user expectations to a level that only artificial intelligence can satisfy. A machine can mimic and even surpass the ability of human intelligence to handle intricate details. Such a system can learn about and interpret users’ past behavior to predict their future behavior. Establishing statistical significance is just one factor. Creating a model that can predict future experience is probably the next strategic consideration to look at in defining an intuitive user experience. Let’s consider a few possibilities.

Data-Driven UX Research

The user-centered design (UCD) process focuses on gaining a deep understanding of users and typically comprises three main phases: discovery, design, and evaluation. The discovery phase starts with identifying the underlying problem, involving key sources such as internal stakeholders, external customers, the Web analytics team, and data from feedback forms. The team analyzes the data they’ve gathered to draw insights from it. However, what teams sometimes fail to do is to validate those insights—possibly because of a time crunch or budget constraints. Thus, the team may fail to realize the potential of statistical models or to apply them to draw the right inferences.

Let’s look an example. Imagine there were a UX redesign effort for an ecommerce portal to resolve a few key user-interface (UI) issues that users had reported: button visibility, text legibility, information findability, a complicated checkout process, and navigation issues. The best way of analyzing these issues would be by creating a problem matrix. Jeff Sauro and James R. Lewis discuss a UI problem matrix in their book Quantifying the User Experience. A problem matrix lists each UI problem, along with user names and descriptions, and a severity rating that takes into account both the observed problem frequency and the problem’s impact on users. From an analytical point of view, a useful approach to identifying UI problems is connecting them with the users who experienced them, as shown in Table 3.

Table 3—UI problem-matrix diagram

UI Problem	User 1	User 2	User 3	User 4	User 5	User 6	Total	Proportion
Button visibility	x	x			x	x	4	4/6 = 0.67
Text legibility	x						1	1/6= 0.167
Information findability	x	x	x	x	x	x	6	6/6 =1
Complicated checkout process				x	x		2	2/6 = 0.33
Navigation					x		1	1/6=0.167
Total	3	2	1	2	4	2	14	P=0.47

Using the problem matrix, you will be able to identify patterns in the historical dataset as the problem variables change over time and use them to construct predictive models. For example, they may predict that a particular user segment would always face problems comparing products or struggle with the checkout task flow.

Data-Driven User Personas

A general problem with personas is that they are not based on firsthand customer data or, if they are, that the sample size of the data set is not statistically significant. Creating data-driven user personas is a great way of representing target users. Alan Cooper first advanced the idea of creating such personas, creating these models from data he’d gathered by interviewing seven or eight users. Current practice often differs from best practice. To expedite the process, teams often skip interviewing users and instead create personas based on their intuition and empathy, employing their own mental models and stating problems in terms of their own characteristics or motivations, which may be completely different from those of the target user groups. This process leads to bias.

Other big mistakes with personas result from leveraging information that is not relevant to the context and from using only qualitative data. Agents of change are not representative of the typical mental model of users. Plus, mental models vary among different cultural groups and change depending on the user’s emotional state, life-changing events, the attainment of mental maturity, and the observation of new things. In such cases, applying a statistical model can deliver substantial results. Once you apply a predictive model, you can forecast outcomes even if it’s necessary to add new predictor variables to the existing model.

Data-Driven Decision Making Prior to Interaction Design

Interaction design is the most challenging part of the UX design process. The proliferation of new form factors such as mobile devices, tablets, and wearables—with their multi-touch screens and speech input—have further increased the complexity of designing applications. Dealing with such complexities often detracts from an emphasis on meeting the needs of target user groups.

Let’s look at an example how data analysis fits into the process. Envision a Web application for which the dominant problem that a group of users has reported is with navigation. The application uses a hover menu, forcing users to reach a destination through hover tunnels that become quite annoying and challenging for users, especially those with motor disabilities. This challenge would lend itself to running an A/B test with users to gather statistically significant data that would help to determine a new navigation design. It would probably be necessary to find out what navigation pattern—a navigation hub or a fixed navigation bar—would work better for a smartphone. Over time, test results would demonstrate what navigation patterns would be most desirable for particular form factors, which a UX designer could employ in creating future interaction-design solutions.

Measuring Cognitive Changes

The detection of an inconsistency is a strong trigger for a cognitive change. [2] A location change will likely lead users to do different things. Exposure to a different culture might prompt them to learn a new way of doing things. Their perceptions and judgment might take a new direction. Every new learning experience has the potential to change their viewpoint and actions. If we can predict and measure such changes and their impacts on decision making, we can establish an anticipatory design pattern to address them.

Conclusion

History can deliver rich insights and opportunities to think ahead. People can learn from their past mistakes and see a better way forward. Predictive models rely on historical data sets; therefore, it is important to record what is occurring in the present to build the right future. With the advancement of machine learning, data science is delivering the next big leap in the digital world. 

References

[1] Wang, John, editor. Encyclopedia of Business Analytics and Optimization. Hershey, PA: Business Science Reference, 2014.

[2] Johnson-Laird, P.N. “Mental Models and Cognitive Change.” Journal of Cognitive Psychology, March 20, 2013.