Social Media Business Smarts at Dell

This week I was at DellWorld moderating a panel featuring two Dell social media managers:

  • Richa Verma, who is the Director of Social Media for Applications and BPO Services
  • Richi Dave, who is the Executive Director for Business Digital Marketing.

You can see some of the things that we spoke about above. Dell is doing some great things in social media marketing and it was fun to talk to both Richa and Richi about what they are doing.


Dr. Dobbs: Doing a Website Usability Survey Right

surveysA well-constructed usability survey can reveal what your users actually want. Here are tips from an experienced designer on putting together a solid survey, and online tools to help.

A site developer needs to know when to listen to their users, and one of the best ways is to survey their likes and dislikes. But putting together solid surveys isn’t always intuitive. Luckily, there are user experience experts such as Caroline Jarrett and various online tools to help.

Surveys now are cheap and easy to do. I receive requests to answer various surveys almost one per week, and I am sure you do as well. But before you start down the survey path, figure out first how you are going to get respondents, and how to make them trust you. Make sure your request looks legit, and that you aren’t just collecting email addresses for spamming them later with other requests. Include some form of reward up front so the respondent is motivated to help you.

Jarrett’s presentation (and upcoming book) are full of great ideas on improving your surveys. Your survey should ask about a recent and vivid experience, because people tend to forget things over time. Ask one question at a time, rather than putting them together into a compound and confusing question. Don’t worry too much about the number of individual points on your rating scale: whether to choose five or seven or an even number, your respondents don’t much care. Do put effort into the “other” category to make it easier to answer or to collect responses that you hadn’t thought of. Keep the preamble information short and sweet, so your respondents don’t have to page through a lot of preliminary screens before they start answering your questions. And don’t collect your demographic data up front, but at the end when a respondent is more likely to fill out this sort of information.

Spend some time perfecting your questions, and testing them quickly to see if you are going in the right direction. This is called pre-testing. It also helps to review all your questions before you field your survey, and make sure that you really need all of them in the final survey. Sometimes you won’t know what to do with the information that you collect on a particular question, so drop it from your survey for this round: you can always field another survey in the future once you figure out what to do with this additional question.

Take a moment and go online and search for “worst survey questions ever.” That will open your eyes to some of the worst examples and things to avoid.



Getting Web Navigation Right

When putting together your website pages and menu structure, you have to decide how to organize the content and figure out what goes where and under which menu and topic labels. In the past this was mostly an ad hoc process that involved a few people sitting around a table guessing at how your content should be organized. But now there are online tools that can help you test your website to see if your choices were the right ones, or if there is some other organizational structure that would make more sense for your visitors.

There are two broad categories of tools that go under the names of card sort and tree testing. Both can be useful in picking the right way to organize your content. Both types of tools are inexpensive, costing a few hundred dollars per study in most cases, and can be done very quickly online with just a Web browser. The way it works is pretty straightforward: first, you recruit a sample of end users for your research and collect their email addresses that you will use to send out invitations. For card sorts, you collect anywhere from 30 to 50 different content labels or particular item names that you want to include in the organization. They should be as granular as possible, to make the process easier. Then you set up your test on one of these card sort sites:

  • OptimalSort
  • UserZoom (which also can be used for tree tests)

There are two different types of card sorts, open (meaning you ask your participants to specify the categories that you want your items sorted into) and closed (meaning you provide the category names).

You run your test for a period of time and then collect the results. Once you have a recommended list of categories and their groupings, the next step is doing a tree test on as complete (as possible of the) navigation structure of your proposed website. Do your users agree with the selections that you have made? Or do the proposed categories accurately reflect the content that are included in each one? Or do your users get lost navigating your menus? This element of your research gives users the tasks of finding the particular content on your site, and measuring their success.

Tree testing sites include:

  • Treejack
  • C-inspector

The tree testing results have all sorts of interesting information. The example above shows that, even though most people correctly found the information they were looking for under “Home Internet Plans,” almost half initially selected the wrong menu choice to get there. And more than a quarter who made a correct first selection made an incorrect second selection. More than half of their customers (some unknowingly) got lost within 2 clicks when looking for home broadband service. And when you lose people, you always risk not getting them back.

User experience guru Danielle Cooley has some advice here. “A study result like this shows the team needs to re-examine their information architecture or their navigation labels (or both) to help ensure customers find the information they are seeking. Without a test like this, the team would just be left wondering after the site launch why home broadband sales were down. And, most likely, having to answer to upper management for such a failure.”

Internet Evolution: Understanding Your Research Bias

Oftentimes, when we start out on some research project to understand our site visitors’ behavior, we tend to forget that we bring to these projects our own biases and preconceived notions. I recently attended a seminar by user experience grandmaster Danielle Cooley where she spent some time exploring this topic.

Here are some of her tips so that you can understand your own biases and do a better job when you have to conduct your own research and usability projects.

Cooley breaks down bias into eight different dimensions:

  • Selection bias. This is a very well known bias. The people that opt in for a particular research activity are inherently different from the general population, or those that choose not to participate. Make sure you understand the particular segment of the population that you are examining.
  • Acquiescence bias. Respondents tend to want to agree with the interviewer, because they want to be liked and earn their stipend for taking the survey. Cooley urges interviewers not to be too friendly when conducting in-person interviews, just for this reason.
  •  Social desirability bias. People tend to lie when you ask them sensitive questions, such as about sexual preference or drug use. “They also tend to lie or at least alter their answers when asked about something where their answers would tend to make them look bad,” Cooley says. She uses as an example a survey that asked Bostonians if they walked up a long flight of stairs from the deepest metro station or took the escalator. The results were way off the actual observed practice, because of course most people won’t attempt to climb 200 steps!
  • Central tendency bias. When people are surveyed with a range of answers (from 1 to 10, from approve to disapprove, etc.), they often tend to reply with something in the middle of the range. This can be mitigated by using questions that present both sides of an issue, for example: “Do you prefer or avoid websites that ask for your email address?” with a range of answers that go from “always prefer” to “no opinion” to “always avoid.” The slide above shows an example of a very poorly worded question too.
  •  Confirmation bias. “People tend to believe that a particular set of information supports their existing beliefs or biases,” she says. Cooley cites one political study where both Democrats and Republicans confirmed separate and opposing implications from the same survey results!
  • Reverse fundamental attribution errors. “Traditionally, people blame external circumstances for their own negative behaviors but attribute others’ negative behaviors to particular personality flaws,” says Cooley. But in user experience research, we see the subjects blame themselves for being unable to navigate a particular website or complete a given task, no matter how poor the site design.
  •   Hawthorne Effects. When subjects know that they are being observed, they often change their behavior. The name comes from site of a phone company manufacturing plant in Hawthorne Ill. that was used for several behavioral studies in the 1920s. “Just remember, we aren’t doing peer-reviewed science research here. We are just trying to figure out how to build a better website,” she says.
  •   Evaluator effects. Just because you know all the above biases, doesn’t mean that the survey or research project that you attempt won’t come out differently from one that someone else will attempt.

Knowing these biases is a first step in improving your own field research, and good luck with your own projects.

Modern Infrastructure: The promise of SDN

Software defined networks are seemingly everywhere these days, offering the promise of having a virtual network infrastructure that can be provisioned as easily as spinning up a new virtual server or storage network. But SDNs are also hard to find outside of a few marquee customers who have dedicated lots of operational resources to set them up and manage them.

In my story for Techtarget’s Modern Infrastructure ezine, I look at the history of SDN, where things stand today, some of the bigger obstacles and how you can begin to plan for them in your own data center.

Slashdot: Segregate your data owners by personae


Positing particular personae (say that slowly) isn’t something new when it comes to website design: The FutureNow guys have been doing it for more than five years, and there are a number of other content engagement “experts” that have their own ways at better segmenting and understanding your ultimate audience. The process of using particular personae can be a way to develop websites that can deliver higher click-through rates and improved customer experience. All well and good, but what about improving the internal data access experience too?

That was the subject of a session at the Teradata Users Conference in Washington DC in October. I heard about how you can use personae to segregate and better target your data owners and data users. It is an intriguing concept, and one worth more exploration.

(An example of virtual data marts at eBay, more explanation below.)


The session was led by Gayatri Patel, who works in the Analytics Platform Delivery team at eBay and has been around the tech industry for many years. There aren’t too many places that have as much data as eBay has: each day they create 50 TB’s worth and they have more than 100 PB per day that is streamed back and forth from their servers. That is a lot of collectibles being traded at any given point. And something that I didn’t really understand before: eBay is a lot more than a marketplace. They have developed a large collection of their own mobile apps that are specific for buying cars, or fashion items, or concert tickets for their specific audiences. In the past they have had difficulties in trusting their data, because two different metrics would come up with different numbers for the same process, so that often meetings would be consumed with different groups presenting conflicting views on what was actually going on across their network.

Patel has come up with mechanisms to focus her team’s energies on particular use cases to better understand how they consume data, and to supply her end users with the right tools for their particular jobs. To get there, she has worked hard to develop a data-driven culture at eBay, to identify the data decision-makers and how to help them become more productive with the right kinds of data delivered at the right time to the right person.

Let’s look at how she partitions her company of data heavyweights:

  1. First are the business executives who are looking at top-line health and metrics of their particular units and have relatively simple needs. They want to drill down deeper to particular areas or create operational metrics and get more narrow and focused areas of particular data sets. Let’s say they want to see how weather-caused shipping delays from sellers are impacting their business. These folks need dashboards and portals that are one-stop shops where you can see everything at a glance, post your comments and share your thoughts quickly with your business unit team. Patel and her group created personal pages with a “DataHub” portal called Harmony, that makes sure all of their metrics are current and correct, and where the executives can bookmark particular graphs and share them with others.
  1. Second are product managers who are looking to learn more about their customers, and want to do more modeling and find the right algorithms to improve their marketplace experience. “We followed some of our managers around, attended their meetings and tried to understand how they use and don’t use data,” Patel said. Her team came up with what they call the “happy path” or what others have called the “golden path” – the walk that someone takes during their daily job to find the particular dataset and report that will help them do their job and make the best decisions. “Each product team has a slightly different path in how they interact with their data,” she said. “Our search development teams are more technical and data-savvy than the teams who work on eBay Motors, for example.” Her team has to constantly refine their algorithms to make the happy paths more evident and useful and well, happier for this group of users.
  1. Third are data researchers and data scientists. These folks want to go deep and understand how everything fits together, and are looking to make new discoveries about particular eBay data patterns. They want more analysis and are constantly creating ad hoc reports. Patel wanted to make this group more self-sufficient so they can concentrate on finding these new data relationships. Her team created better testing strategies, what she called “Test and Learn,” which has a collection of short behavioral tests that can be quickly deployed, as well as more longitudinal tests that can take place over the many days or weeks of a particular auction item on eBay. “We want to fail fast and early,” she said, which is in vogue now but still is something to consider when building the right data access programs. Patel and her team have developed a centralized testing platform to make it easier to track company-wide testing activities and implement best practices.
  1. Next is your product and engineering teams. They do prototypes of new services and want to measure their results. These teams are creating their own analytics and constantly changing their metrics using methods that aren’t yet in production. For this group, Patel made it easier for anyone to create a “virtual data mart” which can be setup within a few minutes, so that each engineer can build their own apps and create specific views pertinent to their own needs. (A sample screen is shown above.)

eBay has three different enterprise data efforts to help support all of these different kinds of data users. They have a traditional data warehouse on Teradata, three of them in fact. They have a fourth warehouse which is semi-structured and called “singularity” that has more behavioral data for example. Finally, they use Hadoop for unstructured Java and C programs to access. The sizes of these things is staggering: Each of the traditional data warehouses is 8 TB and the other two are 42 and 50 PB respectively.

As you can see, the eBay data landscape is a rich and complex one with a lot of different moving parts and specific large-scale implementations that meet a wide variety of needs. I liked the way that Patel is viewing her data universe, and having these different personae is a great way to set her team’s focus on what kinds of data products they need to deliver for each particular group of users. You may want to try her exercise and see if it works for you, too.

Network World: Web-based conferencing comes of age

As more people telecommute, having a reliable way to connect via desktop video conferencing takes on greater importance. And for employees working in the office, Web-based meetings are a less expensive and less time consuming alternative to business travel.

Web-based conferencing services aren’t new, but they have been getting better, easier to use and less expensive. The options range from one-on-one desktop screen sharing to group video chats to large-scale presentations such as Webinars or “virtual conferences.”

We looked at eight desktop conferencing services, a mix of market leaders and newcomers, including Adobe Connect, Cisco Webex, Citrix GotoMeeting, InterCall Unifed Meeting (in beta with v5), LogMeIn Join.Me Pro, Microsoft Lync 2013 (in beta, and part of Office 365), Skype Premium (now owned by Microsoft), and Professional. Connect and Webex come out on top.

You can read my complete review in Network World here.

How Liberty Mutual built their first mobile app with Mendix

One of the largest insurers in the US was looking to roll out a new mobile app for its group insurance customers. Chris Woodman, an IT manager at the firm, described at Mendix World the process they went through and how Mendix was a key element to their success.

“In 2011, we wanted to develop a mobile app, but we didn’t know what we were getting into, and we had no previous mobile development experience,” he said. “Two months later we had our app deployed.” Mendix awarded the project as the outstanding effort of the year at the conference.

You can read more of my report on Liberty Mutual’s efforts from the Mendix blog here.

There are other entries that I authored during the show, and here are their links. Mendix definitely has an interesting story to tell.

Hanging with the kids at the Microsoft Imagine Cup

For the past several days I have been in Sydney as one of the judges in Microsoft’s annual student software contest called the Imagine Cup. This is the tenth year of the event that brings together several hundred mostly college students from around the world. I got to see dozens of presentations and talk to dozens of other geeky kids. To say that I was in my element is an understatement. It gives me lots of hope for our youth.

The kids had to write apps that were around a theme of “imagine a world where technology solves the world’s toughest problems” and they didn’t shy away from tackling many of them head-on. There were more than five different apps to try to help blind people navigate their neighborhoods, and other apps dealt with poverty, hunger, health, and the environment. Each team had to prepare a pitch video for the initial judging round and then do an in-person presentation and demo of their technology.

This year’s competition was a fifth female contestants, and I got to see several all-women teams from Oman (pictured here), Qatar and Ecuador. That gives me a lot of hope: back when I was in engineering school, the women could be counted on one hand.

Some of the projects were very elaborate, using Kinect sensor data being fed to some cloud-based service and being controlled from a smartphone. Others were fairly traditional software projects. Of course, Microsoft encourages the teams to use the broadest possible selection of its own software tools, and there are different contests for general software development and gaming and phone-based projects. The gaming judges had a tough assignment: they actually had to spend time playing the games. I had to settle for Powerpoint and demos with my teams.

Part of my job as a judge was to make sure that the demos actually were working code. For those of you that have ever demo’ed something to me, you know I like to kick the tires and pull wires to make sure that the stuff is real. I was very pleased with one team, when I noticed they were running it from a local Web server, brought up their code to show me that they had done the work, they just didn’t trust the local Internet connection to give them the bandwidth they needed. That was delicious.

Many of the teams didn’t really understand the business context judging requirement: talking to old hands who have been to prior contests I found out that the Imagine Cup rules see-saw back and forth between technical and business achievements. But then others got some of the business flavor almost instinctively, as you see in this photo of the Omani all-girls team. The red head scarves are coordinated with the logo on their shirts and their logos on their slide deck: all had to do with their blood bank software. I daresay there are few established corporations that could match that level of polish.

And given that kids came from all over the world, we got to listen to some very heavy accented English. I could tell that the best teams had their techie lead speak English. Those that relied on having a marketing person as the “face” (or better yet, voice) of the team had problems when it came time to answer the judges’ technical questions and had to lose time in translating them into the native language and then explaining the answers to us.

You could also see that the countries that have a longer history of educating their kids in English were doing better: Singapore, the Middle East, and parts of Asia. Given that the contest is held in English, this is no surprise. What I found interesting is that almost all of the engineering schools around the world have English classes. Even in China and Germany: one professor told me that “we have to be competitive and English is the universal language of software.”

Here are some pictures from Sydney and here is the link to the video submissions for some of the teams.