Advertisements

Supply-Demand Curves for Attention

The basic ideas behind the Attention Economy are simple. Such an economy facilitates a marketplace where consumers agree to receives services in exchange for their attention.

Alex Iskold, ReadWriteWeb, The Attention Economy: An Overview

The attention economy. It’s a natural evolution of our ever-growing thirst for information, and the easier means to create it. It’s everywhere, and it’s not going anywhere. The democratization of content production, the endless array of choices for consumption.

In Alex’s post, he listed four attention services, as they relate to e-commerce: alerts, news, search, shopping.  In the world of information, I focus on three use cases for the consumption of information:

  1. Search = you have a specific need now
  2. Serendipity = you happen across useful information
  3. Notifications = you’re tracking specific areas of interest

I’ve previously talked about these three use cases. In a post over on the Connectbeam blog, I wrote a longer post about the supply demand curves for content in the Attention Economy. What are the different ways to increase share of mind for workers’ contributions, in the context of those three consumption use cases.

The chart below is from that post. It charts the content demand curves for search, serendipity and notifications.

micro-economies-of-attention-3-demand-curves-for-content

Following the blue dotted line…

  • For a given quantity of user generated content, people are willing to invest more attention on Search than on Notifications or Serendipity
  • For a given “price” of attention, people will consume more content via Search than for Notifications or Serendipity

Search has always been a primary use case. Google leveraged the power of that attention to dominate online ads.

Serendipity is relatively new entry in the world of consumption. Putting content in front of someone, content that they had not expressed any prior interest in. A lot of the e-commerce recommendation systems are built on this premise, such as Amazon.com’s recommendations. And companies like Aggregate Knowledge put related content in front of readers of media websites.

Notifications are content you have expressed a prior interest in, but don’t have an acute, immediate need for like you do with Search. I use the Enterprise 2.0 Room on FriendFeed for this purpose.

The demand curves above have two important qualities that differentiate them:

  • Where they fall in relation to each other on the X and Y axes
  • Their curves

As you can see with how I’ve drawn them, Search and Notifications are still the best way to command someone’s attention. Search = relevance + need. Notifications = relevance.

Serendipity commands less attention, but it can have the property of not requiring opt-in by a user. Which means you can put a lot of content in front of users, and some percentage of it will be useful. The risk is that a site overdoes it, and dumps too much Serendipitous-type content in front of users. That’s a good way to drive them away because they have to put too much attention on what they’re seeing. Hence the Serendipity curve. If you demand too much attention, you will greatly reduce the amount of content consumed. Aggregate Knowledge typically puts a limited number of recommendations in front of readers.

On the Connectbeam blog post, I connect these subjects to employee adoption of social software. Check it out if that’s an area of interest for you.

*****

See this post on FriendFeed: http://friendfeed.com/search?q=%22Supply-Demand+Curves+for+Attention%22&who=everyone

Advertisements

Improving Search and Discovery: My Explicit Is Your Implicit

Two recent posts on the implicit web provide two different takes. They provide good context for the implicit web.Richard MacManus of ReadWriteWeb asks, Aggregate Knowledge’s Content Discovery – How Good is it, Really? Aggregate Knowledge runs a large-scale wisdom of crowds application, suggesting content for readers of a given article based on what others also viewed. For instance, on the Business Week site, you might be reading an article about the Apple iPod. Next to the article are the articles that readers of the Apple iPod article also viewed. MacManus finds the Aggregate Knowledge recommendations to be not very relevant. The recommended articles had no relationship to Apple or the iPod.

Over at CenterNetworks, Allen Stern writes that Toluu Helps You Like What Your Friends Like. Toluu lets you import your RSS feeds and friends who have also uploaded their RSS feeds. It applies some secret sauce to analyze your friends’ feeds and create recommendations for you. Stern finds the service a bit boring, as all the recommendations based on his friends’ feeds were the same.

In the case of Aggregate Knowledge, the recommendations were based on too wide a pipe. The implicit actions – clicks by everybody – led to irrelevant results because you essentially the most popular items. In the case Toluu, the recommendations were based on too narrow a pipe. The common perspectives of like-minded friends meant the recommendations were too homogeneous.

Both of these companies leverage the activities of others to deliver recommendations. The actions of others are the implicit activities used to improve search and discovery. A great, familiar example of applying implicit activities is Google search. Google analyzes links among websites and clicks in response to search results. Those links and clicks are the implicit actions that fuel its search relevance.

Which leads to an important consideration about implicit activities. You need a lot of explicit activity to have implicit activity.

Huh?

That’s right. Implicit activities don’t exist in a vacuum. They start life as the explicit actions of somebody. This is a point that Harvard’s Andrew McAfee makes in a recent post.

Let’s take this thought a step further. Not all explicit actions are created equal. There are those that occur “in-the-flow” and those that occur “above-the-flow”, a smart concept described by Michael Idinopulos. In-the-flow are those actions that are part of the normal course of consumer activities, while above-the-flow takes an extra step by the user. A couple examples describe this further:

  • In-the-flow: clicks, purchases, bookmarks
  • Above-the-flow: tags, links, import of friends

Above-the-flow actions are hard to elicit from consumers. There needs to be something in it for them. Websites that require a majority of above-the-flow actions will find themselves challenged to grow quickly. They better have something really good to offer (such as Amazon.com’s purchase experience). Otherwise, the website should be able to survive on the participation of just a few users to provide value to the majority (e.g. YouTube).

So with all that in mind, let’s look at a few companies with actual or potential uses of the explicit-implicit duality:

Google Search

In an interview with VentureBeat, Google VP Marissa Mayer talks about two different forms of social search:

  1. Users label search results and share labels with friends. This labeling becomes the implicit activity that helps improve search results for others. This model is way too above-the-flow. Labeling? Sharing with friends? After experimenting with this, Mayer states that “overall the annotation model needs to evolve.” Not surprising.
  2. Google looks at your in-the-flow activity of emailing friends (via Gmail). It then marries the search histories of your most frequent email contacts to subtly alter the search result rankings. All of this implicit activity is derived from in-the-flow activities. For searches on specific topics, the more narrow implicit activity pipe of just your Gmail contacts is an interesting idea.

ThisNext

ThisNext is a platform for users to build out their own product recommendations. They find products on the web, grab an image, and rate and write about the product. Power users emerge as style mavens. The site is open to non-members for searching and browsing of products.

ThisNext probably relies a bit too heavily on above-the-flow activities. It takes a lot of work to find products, add them to your list of products and provide reviews. It also suffers from being a bit too wide a pipe in that there’s a lot of people whose recommendations I wouldn’t trust. How do I know who to trust on ThisNext?

Amazon Grapevine

Amazon, on the other hand, has a leg up in this sort of model. First, its recommendations are built on a high level of in-the-flow activities – users purchasing things they need. This is the “people who bought this also bought that” recommendation model. Rather than depend on the product whims of individuals, it uses good ol’ sales numbers (plus some secret sauce as well) for recommendations. This is a form of collaborative filtering.

Amazon Grapevine is a way of setting the pipe for implicit activities. The explicit activity is the review or rating. These activities are fed to your friends on Facebook. One possibility for Amazon down the road is to use the built-up reviews and ratings of your friends to influence the recommendations it provides on its website. Such a model would require some above-the-flow actions – add the Grapevine application, maintain your account and connections on Facebook. But these aren’t that onerous; the Facebook social network continues to be an explicit activity that has high value for individuals.

Yahoo Search

Yahoo bought the bookmarking and tag service del.icio.us back in 2005. It’s hard to know what, if anything, they’ve done with that service. But one intriguing possibility was hinted at in this TechCrunch post. The del.icio.us activity associated with a given web page is integrated into the search results. Yahoo search results would be ranked not just on links and previous clicks, but also on the number of times the web page had been bookmarked on del.icio.us. And, the tags associated to the website would be displayed, giving additional context to the site and enabling a user to click on the tags to see what other sites share similar characteristics.

This takes an above-the-flow activity performed by a relative few – bookmarking and tagging on del.icio.us – and turns it into implicit activity that helps a larger number of users. But with the Microsoft bid, who knows whether something like this could happen.

The use of implicit activity is a powerful basis to help users find content. Just don’t burden your users with too much of the wrong kind of explicit activity to get there. Two factors to consider in the use of implicit activity:

  1. How wide is the pipe of implicit activities?
  2. How much above-the-flow vs. in-the-flow activity is required?

Facebook Beacon Is Dead. Long Live Amazon Grapevine.

Amazon has just come out with two new Facebook apps, as reported by Erick Schonfeld on TechCrunch. One is Amazon Giver, which lets friends share wish lists. The other is Amazon Grapevine, which lets you broadcast your activities on Amazon back to the Facebook newsfeed.

Pardon me…but isn’t that the basis of Facebook Beacon? Well, sort of. There are a few differences.

Amazon made this completely opt-in, which differs from the opt-out philosophy of Beacon. Also, product purchases are not included in Grapevine, but they were an important part of Beacon.

Personally, Beacon doesn’t bother me that much. I did not experience the early versions of Beacon with the too-fast notice that popped up on e-tailers’ sites. No accidentally revealing an engagement ring purchase. But there are times a purchase says something about you.

In fact, I think the idea of sharing your purchases with your friends has a lot of interesting potential. I can think of three different reasons people would share purchase information with friends and check out what their friends have purchased:

  1. Self-expression
  2. Product discovery
  3. Friends’ reviews

I’ve mapped those reasons to several different retail sectors.

  • Apparel = self-expression
  • Computer Hardware/Software = friends’ reviews
  • Consumer Electronics = friends’ reviews, self-expression
  • Home & Garden = self-expression, friends’ reviews
  • Sporting Goods = self-expression, friends’ reviews
  • Baby Products = product discovery, friends’ reviews

For instance, I think broadcasting your Apparel purchases is more a form of self-expression. People’s fashion tastes are an extension of themselves. Participation in some sort of Beacon-like program for Consumer Electronics, on the other hand, would be a chance to provide reviews to friends and read the reviews of your friends. And Baby Products would have a lot of discovery and reviews. See what your friends have purchased for their infants. Anyone who is a first-time parent knows the challenges of figuring out what to buy.

But, Beacon is still controversial, and Amazon doesn’t go as far as broadcasting purchases. So for now, we broadcast our ratings and reviews. This is pretty good. I can learn a lot from that.

The only problem is, the opportunities to share this way are still quite limited. Not too many e-tailers are doing this yet. However, Amazon has a rich history of driving innovation in e-tail. It was the early leader in e-tail. It was among the first to set up an affiliate program (Amazon Associates). It pioneered product recommendations.

So now it’s experimenting with the sharing of product-related information on social networks. Probably won’t be long before other e-tailers get on board.