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Archive for agosto 2009

Designing per il Social Web

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di Bokardo

  • The Usage Lifecycle describes how far a person has progressed in using your web application, helping to identify the hurdles someone needs to overcome to become regular, passionate users.

Babycenter.com has a really great newsletter. Once you tell the site when you’re expecting, it sends you a weekly newsletter targeted at the specific stage of pregnancy you’re in. At 4.5 months, for example, it tells you that your baby weighs about 10.5 ounces and is 10 inches long. This information is timely and relevant…it knows exactly what stage you’re in and helps you deal with the stresses and questions at that point.

The key to babycenter’s ability to deliver a relevant newsletter is that they know your delivery date. Once they know that, they know *a lot* about what you’re going through, as pregnancy is a well-defined process that is mostly the same for everyone. Nine month cycle. Kid. Simple.

Can people designing products of all sorts take advantage of this lifecycle process? Yes, I think they can. One of the primary ideas in my new book, Designing for the Social Web is a similar kind of lifecycle, what I call the “Usage Lifecycle”. The usage lifecycle isn’t as clear cut as pregnancy is, but it recognizes that people go through a progression as they use software. They go from not knowing much at all (like parents early on in pregnancy) to feeling comfortable with the product (like, say, when parents become grandparents :D ) to finally being passionate users.

Usage Lifecycle

The Stages of the Usage Lifecycle

The stages of the lifecycle are straightforward and simple. You can dive into lots more depth as your application warrants, and you can add stages, but for the most part these five stages apply to almost all software.

  • Unaware This isn’t so much a stage as it is a starting point. Most people are in this stage: completely unaware of your product.
  • Interested These people are interested in your product, but are not yet users. They have lots of questions about how it works and what value it provides.
  • First-time Use These people are using your software for the first time, a crucial moment in their progression.
  • Regular Use These people are those who use your software regularly and perhaps pay for the privilege.
  • Passionate Use These people are the ultimate goal: passionate users who spread their passion and build a community around your software

Note that each of these stages describes people, as opposed to a product or a market. It describes the different types of relationships people have with your software product. Have they used it yet? Have they even heard about it? What questions do they have?

Each of the stages are separated by hurdles. The hurdle between the “unaware” stage and the “interested” stage is “awareness”. At this stage what you need to do is make people aware of your product. How do you get people aware of what you’re doing? How do you get them interested and wanting to know more? How do you begin the conversation of what you do and carry that over into a meaningful relationship?

The lifecycle is particularly relevant to web-based software because the product is inextricable from the service. The product is the service. If a person has a question about what your software does, for example, you can literally build that answer into the software itself. One of my favorite examples at the moment is Tripit.com. Tripit’s design is great at moving people from the “interested” stage to the “first-time use” stage, getting people over the hurdle of “sign-up”.

One of the ways that Tripit does this is by clearly explaining exactly what their service is and does. While this may seem like an easy thing to do, it’s actually quite hard. To boil the essence of your software down into a handy 3-pane “how it works” graphic seems like child’s-play. But only the resulting graphic is simple. Creating the simple thing is the difficult part.

TripIt | How it Works

Another way that Tripit helps people get over the hurdle of sign-up is to make it super easy to sign up in the first place. They have a great feature that lets you simply forward them an email from a recent flight or hotel booking. They take that booking email and auto-create an account for you. No sign-up page to create an account. All you do is send an email.

TripIt | Organize your travel

One of the problems I’ve seen over and over (and I’ve been guilty of this myself) is to recognize the stages while talking to people face to face, answering their questions, but then failing to bake that knowledge into the interface itself. By formalizing this conversation with the usage lifecycle, you can begin to set up a process of describing each stage in-depth, and then creating screens with that exact same information placed right on your web site. Just like Tripit does.

The usage lifecycle isn’t a new idea. It’s very similar to what a good salesman does when they target customers. They find out where the person is in the purchase lifecycle, and then tailor their message to get people moving along toward purchase. They answer the same questions over and over, point out the same features and benefits over and over. The lifecycle for any particular product or service is remarkably stable…it’s only a matter of identifying the lifecycle and designing for it. What babycenter has done with pregnancy, we should all be able to do with the usage lifecycle of our software.

Designing for Social Traction (slide deck)

Here is the slide deck from a talk I gave last week at Delve, a two-day masterclass held in Brooklyn, NY.

The talk is in three parts, with each part focusing on a specific problem in software. Each problem is a major hurdle in what I call the usage lifecycle, or the stages people go through as they use and adopt software over time. These three hurdles come directly out of the work I do with clients…I’ve been focusing almost exclusively on these specific problems…I hope the slides help you focus on them as well.

Annunci

Written by Daniel Casarin

agosto 17, 2009 at 7:41 am

Pubblicato su Strategie

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The Social Data Revolution(s)

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di Andreas Weigend

In 2009, more data will be generated by individuals than in the entire history of mankind through 2008. Information overload is more serious than ever. What are the implications for marketing?

The world has witnessed two revolutions in the way consumer data has been solicited and collected. And consumers have changed the way they use the web to converse, shop and transact. As a result, people have elevated their expectations for good, healthy customer relationships and exchanges. And this has put pressure on marketers to forge astute, coherent strategies for how they engage people, what data they gather, and how they use it.

The first data revolution came about when web commerce got going in earnest. It arose from the dream of collecting data from consumer decision-making. With the advent of the web, firms pondered whether it might be worth saving the vast amounts of data that customers were generating through their clicks and searches. For consumers, there was no hiding: after all, there is no online equivalent of discreetly checking out a magazine while a bookstore employee is looking the other way. Amazon.com has pretty much saved all user data from its beginning.

Back then, customers had no choice but to share their intentions with firms. If a technology enthusiast wanted to find out if a website sold a particular surveillance device, there was no shortcut but to type some keywords into a search box and therefore give the company a valuable intention stream. Companies, therefore, had all the power. Many tried too hard to push products and advertisements. The consumer had no voice.

During the first data revolution, successful companies gained power by collecting, aggregating, and analyzing the customer data they collected. However, most companies did not know what to do and ended up burying their data in tombs.

The second data revolution brought about a new dimension to data creation: users started to actively contribute explicit data such as information about themselves, their friends, or about the items they purchased. These data went far beyond the click-and-search data that characterized the first decade of the web.

An early example of user-generated content was Amazon.com’s reviews system. The firm realized that users often trusted recommendations by other users more than promotional material found elsewhere on the web. By enabling users to actively contribute such explicit data, Amazon.com succeeded in leveraging knowledge dormant in its large customer base to help customers with their purchasing decisions.

Later, Wikipedia increased transparency even more by allowing online collaboration. By allowing users to interact and build on top of each other, the site relinquished control over their space. The benefit of allowing such user interaction today is obvious — why spend time on hold with a customer service representative if we can just Google that cryptic error code to see if someone else has already solved the same problem? People learned that by sheer large numbers, an online user community was likely to be more helpful than a representative employed by the company.

Today, the online world has shifted to a model of collaboration and explicit data creation. Successful firms develop systematic ways to encourage and reward users who contribute honest data. A good system does not try to trick customers into revealing demographics or contact information that is useful for the company. Rather, it rewards users with information that is useful to them.

Netflix, for example, allows users to contribute ratings for movies that they have seen. Users have an incentive to contribute accurate data because this will give them better recommendations for new movies. The 1999 “Web 2.0 company” MoodLogic (acquired by All Media Guide, in turn acquired by Macrovision) enabled users to create metadata about their favorite music. Why on earth would they do that? Because they got back playlists, which made it easier for them to discover new music they enjoyed. Such successful companies realized the key feature of a good incentive system: people need to see that they profit from the outcome in some way if they are willing to put in the effort to contribute truthfully.

In the last few years, users have gone a lot further than contributing metadata to movies and music: in fact, they have taken center stage. The center of the universe has shifted from e-business to me-business. Customers are also starting to discover and interact with each other. Knowing that they are not alone has shifted the balance of power from companies back to consumers. And they have begun to demand transparency. Customers are beginning to have a voice. They are realizing that the data they voluntarily contribute can help them and others with making decisions, providing true value. In turn, they want to be treated fairly as individuals by the companies they pay attention and money to.

What are the consequences of this change towards the expectations of consumers? Successful interactions have become genuine communication with near-instantaneous feedback. For example, PayScale allows users to retrieve real-time salary reports based on their job title, location, education, and experience-but only after they have contributed their own data. As the expectations of users change, firms must spend more time developing incentive systems that will entice more users to participate.

Consequently, the online world is beginning to be ruled by the expectations of the users. No longer is it sufficient for a search engine to cough up some hotels across the world when a weary traveler is looking for a good deal in Bangkok! As these consumer expectations shift, companies that want to stay relevant have no choice but to accept the ideas of the consumer revolution as swiftly as possible. For users, switching costs are cheap — firms can no longer think of “customer relationship management” as providing stickiness for the customer (just like fly paper provides stickiness to the fly). Industries such as real estate and automobiles whose business models are built on information asymmetries will quickly lose their revenues to those who increase transparency using data contributed by consumers.

This leaves us several deep questions to ponder, including what the implications are on customer expectations, and what companies can do to address these expectations.

Written by Daniel Casarin

agosto 16, 2009 at 1:41 pm

Pubblicato su Trend

Designing with Psychology in Mind

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di Bokardo

Designing with Psychology in Mind. The event was top notch (as you may have heard) and I’m extremely honored to be among the distinguished speakers.  Jeremy Keith wrote up a great set of notes. Two attendees created tweet tracker called A Feed Apart and another person created a great visualization called A Seat Apart.

Written by Daniel Casarin

agosto 5, 2009 at 7:12 am

Pubblicato su Creatività

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