Including Recommendations in User Interfaces to Enhance Motivation
Published: March 23, 2009
Motivation is an important factor in any kind of online interaction or transaction. People need a little encouragement when they’re not really convinced they should take any action or are uncertain about what action to take next. As users perform tasks online, they need to understand what’s happening and expect you to help them move forward. This article discusses the responsibility of a user interface to provide recommendations along a user’s path of interaction.
You can provide recommendations in any context. Applied effectively, this design approach can help users greatly. In contexts as diverse as desktop and Web applications, ecommerce sites, and other Web sites, providing recommendations
- motivates users to take the next step and complete their tasks successfully
- helps customers make purchasing decisions
- increases users’ comfort levels
- builds customer loyalty and brand recognition
Recommending Courses of Action in Desktop Applications
You can provide recommendations in operating systems and desktop applications—for example, when users download applications or set browser preferences. The Windows operating system offers some good examples of recommendations, as shown in Figure 1, where an icon indicates the best choice. The icons in this user interface let users scan their choices and make a quick decision.
Figure 1—Icons indicating recommendations
Making Recommendations in Web Applications
Provide recommendations when users need to make decisions in Web applications. For example, on an ecommerce site, providing recommendations can enhance your revenues by building service and support into the purchasing process.
Domain Analysis: Where Should You Use Recommendations?
It is especially important to provide recommendations when people need information to complete their tasks. Some scenarios where recommendations are helpful include the following:
- Scenario 1: Looking for information—When people come to a Web site looking for information, they need guidance along the way to help them find the information they need.
- Scenario 2: Shopping—When people come to a Web site to buy something, they might not be clear about exactly what they want or need. You can use recommendations to help them quickly and easily determine their needs. For example, when customers choose items to purchase, based on their choices, you can make recommendations about other products that would likely be of interest to them. They may buy additional products that you’ve recommended.
- Scenario 3: Making decisions—People sometimes need help making decisions online. You can help people see the pros and cons of different options.
A comparative analysis of Web sites and applications in various domains helped my company to understand how users interact with the recommendations those sites offer and what they thought about them. We studied recommendations in the following domains:
- entertainment and media
- health and fitness
- real estate
- social networking
- Web search
More and more people are using blogs. In addition to any recommendations a blog’s author might make in a blog post, blogs offer features that let bloggers and their readers make recommendations. They can recommend
- particular blog posts
No best practice seems to exist yet in this domain. Most recommendations in blogs let you know what the blogger is reading. For example, Avinash Kaushik’s Web analytics blog Occam’s Razor lists the author’s favorite blogs, as Figure 2 shows.
Figure 2—List of a blogger’s favorite blogs
On an ecommerce Web site like Amazon, there are three typical scenarios in which a user interface needs to make recommendations, as follows:
- A shopper enters a Web site not knowing what to buy.
- A potential shopper comes from a competitor’s Web site, trying to determine whether he wants to make a purchase on your site.
- A loyal customer visits your Web site and knows exactly what she wants to purchase.
In all three cases, providing recommendations helps move your customers forward toward making a purchase. Recommendations can promote your products and services. If you make recommendations based on your customers’ subjective preferences, they can also increase your revenues, as they do on Amazon. Figure 3 shows recommendations on Amazon.
Figure 3—Recommendations on Amazon
Similar to Amazon, eBay includes many different forms of recommendations to help users locate products and make purchasing decisions. As shown in Figure 4, they include faceted search along the left side of the page. They also categorize items and present them on separate tabs, and featured items appear first in the search results. All of these recommendations help guide users who are looking to buy something, but are not sure about what they want.
Entertainment and Media Sites
Entertainment and media Web sites typically include user-generated content and let visitors upload and download media. In this domain, recommendations help motivate users to explore the content.
Figure 5 shows YouTube, which displays promoted, featured, and most-viewed videos, which are common and prominent forms of recommendation. When users sign in to the Web site, YouTube personalizes the home page and displays search results that are in tune with the user’s prior search and viewing activity on the site. Such customizations are excellent examples of recommendations.
Figure 5—YouTube includes promoted and featured videos
Finance Web sites and applications use recommendations to sell their products or services and generate revenue.
Figure 6 shows the Citibank site, which helps customers narrow down their credit card options, based on their specified requirements, then makes a recommendation and describes the recommended card’s benefits. This form of recommendation is a simple and effective technique.
Figure 6—Citibank recommends credit cards
Figure 7 shows how Bank of America provides financial solutions to their customers and prospects. They offer customized recommendations after asking customers a few questions about their current financial status.
Figure 7—Bank of America recommends business solutions
Health and Fitness Sites
In addition to seeking general information about health and fitness, users in this domain are often looking for specific advice or counsel regarding a particular problem. For example, as shown in Figure 8, Health Check Systems provides a tool that lets you calculate your body mass index (BMI)—a metric that defines a body’s health—and provides advice about a recommended course of action. If it also provided links to recommended articles, the user experience would be a complete one.
Figure 8—Health Check Systems provides a user’s body mass index and offers advice
Real Estate Sites
Whenever people are looking for a new home—whether buying or renting—they’re in need of advice and recommendations. A perfect user experience might provide an online agent that guides users through the process of buying a new home.
Figure 9 shows how Realtor.com presents featured homes to their customers and prospects. However, personal recommendations would provide a more powerful motivation for their customers.
Figure 9—Realtor.com presents featured homes
Cerritos lets users specify their budget, then make affordable recommendations, as shown in Figure 10.
Figure 10—Cerritos lets buyers set their price range
Social Networking Sites
Boomj, shown in Figure 11, is a social networking Web site that helps you connect with people around the world. Like many other social networking sites, it provides recommendations and lets you see some of its members without your even having to sign in. This preview of its members entices people to become a member of the Web site.
The popular social networking site Facebook has a feature called Friends You May Know. This feature lets users see potential friends they can add to their list of friends.
Figure 11—Boomj lets new users see some of their members
In the travel domain, making recommendations when selling tickets online can be extremely helpful—especially when there are opportunities for cross-selling other products or services. Coupling lowest-airfare recommendations with a hotel stay at a cheaper rate motivates users to make a purchase. Figure 12 shows how the Yahoo! Travel Web site promotes a featured package. It also offers users a hotel stay along with the flight.
Figure 12—Yahoo! recommends featured packages, combining a hotel stay and flight
Web Search Sites
Recommendations in the domain of Web search take the form of related content. Most Web search sites like Google and Ask, shown in Figure 13, provide links to related searches.
Figure 13—Ask Web search provides links to related searches
User Analysis: What Do Users Think?
After reviewing each of these Web domains in our comparative analysis, we surveyed and interviewed a hundred people who use these types of Web sites. We wanted to know what users thought about their recommendations and whether they followed them.
The pie chart in Figure 14 shows the percentage of users who follow online recommendations (60%), users who want recommendations even if they do not follow them (34%), and users who are not affected by recommendations at all (6%).
Figure 14—Survey results of users’ responses to online recommendations
Figure 15 shows the domains in which users felt recommendations are a required element of the user experience. Users definitely wanted recommendations during their online buying experience, ranking ecommerce sites the highest among domains that offer recommmendations. Interestingly, users did not expect recommendations or think them necessary for Web searches, ranking that domain the lowest.
Figure 15—Survey results for recommendations by domains
One participant, Maria, told us, “Recommendations make me feel comfortable when I see them being provided to me. I see some Web sites providing them, and I expect to see more of them, since I am sometimes indecisive.” Joydeep adds, “Imagine that you are looking for a train from one station to another on a travel Web site. When a train has no more tickets to issue, it provides you with a list of plausible alternative options. Now, that's something I would love to see. I do not see a lot of Web sites doing this. It’s important for companies to know how you present recommendations to users. If done properly, it could monstrously boost Web site traffic.”
How Do Recommendations Benefit Your Users?
You can provide recommendations in enterprise applications, consumer applications, kiosks, and off-the-shelf products.
- Recommendations help novice users take the next steps in their tasks.
- Recommendations help users make decisions quickly.
- Recommendations make users feel at ease.
Providing recommendations becomes extremely crucial when you are selling content online. When users go online to look for something they want, they are willing to spend plenty of time looking. On the other hand, when users go online to buy something, they need help to motivate them to buy—just like in a bricks-and-mortar store.
User interface designers should provide general recommendations for the typical, anonymous user, but they should also provide customizations and specialized recommendations for registered users and loyal customers.
Packaged recommendations—or featured products and services—often serve both your users and your business best. As shown in Figure 16, Apple sells packaged media in the form of Top Songs, Top Movies, and Top TV Shows, demonstrating how to promote products when users are looking for stuff to buy online.
Figure 16—Apple promotes its media through packaged recommendations
When we guide users along a path, their level of confidence increases. However, not every user will trust you in the very first stages of an interaction. Situations sometimes engender confusion and indecisiveness. Use recommendations consistently and comprehensively in your application or on your Web site to build and ensure trust and customer loyalty for a long time to come.
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Microsoft Corporation. “User Interface Text.” Microsoft Pattern Library, 2008. Retrieved March 20, 2009.
Pontis, Inc. “Contextual Recommendations.” Pontis Integrated Marketing Systems, 2007. Retrieved March 20, 2009.
Schafer, J. Ben, Joseph A. Konstan, and John Riedl. “E-Commerce Recommendation Applications.” University of Minnesota, GroupLens Research Project, 2001. Retrieved March 20, 2009.
Sinha, Rashmi, and Kirsten Swearingen. “Comparing Recommendations Made by Online Systems and Friends.” SIMS, University of California, 2001. Retrieved March 20, 2009.