Now you have 90% of most of the current social ecommerce capabilities - let’s move on.

The information architects are experts at understanding how an individual interacts with a computer. The information architect worries about how a user uses a program, how easily information is found, how the user feels when using a program. Information architecture is a mature discipline. People can get a degree in the discipline from the University of Baltimore or Kent State University or the University of Michigan or Bentley college or perhaps a masters from the Illinois Institute of Technology. There are dozens of options. Information Architecture is mature, and it is this discipline should hold the answers to social shopping. If the ecommerce information architect 1.0 understands how a customer interacts with a site, then the information architect 2.0 should understand how a group of customers interact with a social site. But the current social shopping ideas are best guesses at functionality, undisciplined. An information architect will tell you that all features of a web site should be directly related to the desired user behavior - form follows function. This same perspective should be applied to social shopping. First what is the desired behavior of the group, and then all social features should be matched and measured against this desired behavior. Retailers are masters at understanding and guiding group behavior for in-store shopping, but their on-line counterparts are not so in-tune.

Done right, social shopping has tremendous monetization. Think about the wisdom of crowds. Amazon built an empire on this social personalization technique. What is the behavior of crowds in a social shopping setting? Some of the principals of social networks seem to be:

• You are left alone and are anonymous to most people in the same social site, but you know you are not alone. This creates buzz, but not annoyance
• A small percentage of people author social content, most of the rest are consumers. For more on this topic take a look at Forrester’s Social Technographics report
• If the social network is not transparent and open, people will reject it
• Any feeling of belonging or exclusivity is a good thing
• On-line social activity is a generational phenomenon. People who grew up with the internet value their an on-line identity, people who have not grown up with the Internet fear an on-line identity - what is a better definition of a generation then enthusiasm from one generation and fear, uncertainty and ambivalence from the previous generation

Once these and other core social shopping behaviors are understood, then all site features should map directly to them. Today a retailer is not going to add functionality that does not directly drive conversion, decrease abandonment, increase traffic or define brand. So why add social capabilities that does not monetize or directly map to pre-defined social behavior metrics? The value of the network must be derived to appropriately prioritize features. Social metrics should be defined and measured such as the:

• Velocity of social network growth
• Value of customers measured by how they help grow the network
• Network conversion rate defined as how many people invited ultimately participate and buy.

These values are measurable, and all A/B testing should drive defined value such as these metrics. The existing analytics engines do not measure this social dimension, so features are being added and measured in an old and socially inaccurate way. Similarly usability testing tests how an individual interacts with a computer, it does not simulate a social experience. Using these 1.0 filters blur 2.0 capabilities. You can’t evaluate 2.0 features using 1.0 tools. Social shopping is a new dimension - a dimension 1.0 tools do not recognize, measure or appreciate. Using 1.0 ecommerce information architecture and product management tools for a 2.0 social shopping site will create a site that is suboptimal in both ecommerce and social capabilities. 1 plus 1 will equal .5.

Measuring social success and predicting social experiences is not science fiction. Social behavior is predictable. Do you remember the game of life? Not the board game, the algorithm created by John Conway in 1970 that describes how dynamic systems interact. It is a very simple model, the rules are:

• Each cell with one or no neighbors dies, as if by loneliness.
• Each cell with four or more neighbors dies, as if by overpopulation.
• Each cell with two or three neighbors survives.
• Each cell with three neighbors becomes populated.

Here is an example of the game.

The game is a simple model that describes how dynamic systems interact, like a population, or a virus, or a social graph. A social graph is a term that describes the network of connections that exist through which people communicate and share information.

A small social graph that feels lonely to its participants will wither. A social graph that is over-populated will also wither because it is not exclusive or personal enough for its participants. Healthy social graphs grow. But to a retailer, this is still not enough - a retailer needs a healthy network that will also buy.

Imagine an analytics engine that looks like the game of life. It would visualize the value of the network by showing the network graph and highlighting ‘valuable’ that are either helping to grow the network or are buying. For example, just because the network grows does not mean that is valuable because new members may not purchase, like virtual loitering, or registered members may not log in. A truly valuable customer is not only loyal for their purchases, but they also are evangelists that help to grow the network. We need analytical tools to show this social network value. For example image an analytics tool that visualizes social graphs analytics that looks like the following diagram:

This sample social network is very simple. Here adjacent cells represent user relationships known by the retailer. The highlighted cells are ‘active’ customers, where active is either being social, which the red cells represent by inviting or communicating with others or have done some other type of social activity such as ‘digg’ing or ‘del.icio.us’ing a product. Green cells represent users that are buying. The graph can be filtered by time, or average order size, or most growth, or most withering, or whatever. Most ecommerce sites already have the data, we just need the analytics engines to show the social activity in a valuable way and understand the rules that social behavior follows. The graph could also be played forward a few steps to predict future value, and actual results measured against it. Then any A/B test can be predicted and measured against 2.0 metrics. The information architects and product managers could then predict and measure the success of their designs not only against traffic, AOS and conversion, but also against defined social metrics. Without this perspective, we are just social guessing, and function is following form, and what could be more backwards than this? And as an aside, we should stop all the talk about Web 3.0 until the 2.0 ideas are well understood, commoditized, and “Social Information Architecture” has become standard information architecture curriculum - which is obviously still a ways away.

Adam Michelson is the Director of ecommerce at Optaros, Inc.

• Dropdown cart – This is the cart that Gap seems to get the credit for popularizing. Here the cart drops down from the top-right of the page. This cart allows the shopper to continue shopping or to checkout. It is minimally disruptive, and allows the customer to keep shopping, not taking them away from the page they are on. The hope is to increase the average size of order by allowing the customers to keep adding items and not confuse them by interrupting their shopping experience. Many retailers redirect the customer directly to a large cart page, and then onto checkout. This is to drive conversion, but at the expense at the average order size. This RIA technique aims to strike a balance, where the customer chooses to checkout or keep shopping. A more advanced cart can show tax and shipping information in the cart to lower abandonment in checkout so the customer is not surprised when they get to checkout. Inventory information can also be shown if it is relevant to the shopping experience.

• Non-wizard checkout – Calling it one-click checkout is a bit Amazon-proprietary and is not what we are specifically after. What we want is a one-page checkout, not a multi-step wizard-type checkout. This is to deter abandonment. The trick is to fit a fair number of checkout questions on just one page. RIA helps. The RIA technique here is to only ask the questions that must be asked. Then allow customers to select additional questions to answer on-demand. For example, RueLaLa.com makes is easy to access store address or add or change an address in the context of the checkout page. Also the checkout page does not show both shipping and billing address fields if they are the same for the customer. Just one is shown, and if the customer selects an option that shipping and billing addresses are different then the other set of fields are presented. The same technique can be used for gift messaging, gift wrap options and changing the shipping type. Additional information, such as the return policy can slide in or a layer can be used to overlay the checkout page to deliver this information upon request. These techniques do allow the essential information to be presented in an above-the-fold page, and these other elements can be presented using RIA techniques on-demand so the page expands on when the customer requires it to.

• Quick-pick modal – This is a modal screen that displays when the customer hovers over a “quick pick” or “quick look” button image displayed over product images on the product catalog page. This modal then appears over the catalog page and displays the critical product information. This page uses some RIA techniques to fit a surprising amount of information on this page, potentially as much as a product details page. Some retailers have considered removing their product details page entirely because their quick-pick modal displays so much product information. The modal allows customers to research products quicker to find what they are looking for, and is also easier to find other items they may be interested in, because the quick-pick modal seems like such a minimal commitment to click on and read about product. This modal is meant to increase conversion and the average order size. A quick technical note: this modal should not be a pop-up, or browser pop-up blockers may prevent it from appearing, and gives a more jarring experience. Instead this should be a layer that has the appearance of a modal pop-up, but is integrated more seamlessly into the page, and will not be blocked by popup blockers. The same technique should be used for the cart. Additionally this is not technically a Web page, but it can act like one by coding your analytics correctly on the modal, and allowing the modal its own URL, so the page is bookmarkable and sharable by URL.

• Inventory integration – The RIA technique required for inventory is to show the customer how different product options selected impacts inventory availability. For example, there may be plenty of size 6 shoes in black, but only two left in red. But there are plenty of red shoes in size 6 ½. To show this effectively, the inventory messaging should changes real-time as product options are selected. So in this example, if the customer chooses a red shoe, it would show that only two are left in size 6. However if size 6 is selected first, then it would show only two left in red. RIA helps here by allowing clever inventory code to run to get the inventory of the specific options (usually size and color) that are available, and present them in an efficient way. This does not have to be a Kayak slider-type of control or a Blue Nile configurator, it can just be buttons and drop downs, but the goal is to display options and messaging that reflect what the customer has done in real-time. This is to drive down abandonment as the customer can more easily choose the items that are available. Other options such as shipping can change too in real-time as the user makes selections to further reduce customer surprises at checkout.

• Increased SEO and Loyalty – Embedding items of interest in your ecommerce site can help drive your brand and increase loyalty. It can also create links to help with SEO. A couple of cool examples include how Alternative apparel clothing includes product imagery of celebrities wearing their looks. Their site also advertises event listings and includes music that promote their brand and the community they are trying to reach.

• The use of Multimedia – Creating a more dynamic interaction with the customer is a key benefit of the use of multimedia in ecommerce. On-line customers have the obvious disadvantage of not having a tactile experience with the product to make buying decisions. Multimedia can help fill this void by allowing the customer to see the product ‘in-action’. For example within the Novara bikes section of REI’s site, there are videos of the various bikes in real-life settings. This helps to brand the product and allows the customer a more engaging experience.

Another good example of multimedia is Martin + Osa’s shop by outfit section. Here if the customer keeps their cursor over a specific outfit, the modal spins and twirls to show the look off. The customer gets a better idea of the overall look, including how the fabrics fit and move with the body. Also the models walk in and out of frame as the customer filters various looks. A nice effect to show brand, and informative for the customer to get a better view and feel for the outfit.

The following table shows some of the other popular RIA capabilities and their business benefits:

These specific RIA capabilities are gaining traction. These capabilities do not require an architectural overhaul to implement them. They are user interface widgets that can be added to almost any existing Web site. If your organization’s current ecommerce infrastructure is good, then you can just include AJAX JavaScript libraries such as Prototype.js, Script.aculo.us, Modalbox, Validation, Prototips or Prototabs to build these widgets. Flash and Flex are not required for this level of RIA. You should be able to bring these capabilities to life relatively quickly on your existing ecommerce site, perhaps in a couple of months, well in advance of next holiday.

Adam Michelson is the Director of ecommerce at Optaros, Inc.