It's been long ago when first variants of this idea came to my mind. The idea is still very raw, but I think writing a post will advance it. This post is a draft of the conversational advertising platform and its features.
Today traditional Internet advertising is considered as kind of annoying toll. People became resistant to it. But if to forget about banners and sponsored links, and just think about advertisement' initial idea: to spread information about a new products – you feel that now, when Web provided us all with huge brand new conversational abilities, this thing can be done much more effective. It sometimes happens to everyone when you really want to advertise a good product that you use and like to your friends. And your friends sometimes ask you for a recommendation about a product. And we all do this often on Twitter and other social networks. People share information about products they like via conversations on social networks. As social networks become more advanced and ubiquitous, they're getting new tools to manage, filter and guide information streams; this trend is becoming more obvious.
This problem is partly addressed by the advent of collaborative filtering recommendations of the form “people who bought x also bought y” feature (Linden 2003). From “The Dynamics of Viral Marketing”: “These refinements help consumers discover new products and receive more accurate evaluations, but they cannot completely substitute personalized recommendations that one receives from a friend or relative.” A Lucid Marketing survey found that 68% of individuals consulted friends and relatives before purchasing home electronics, more than the half who used search engines to find product information.
Another example. I am really surprised with how our product TwitterTim.es is being spread all over Twitter. We paid nothing for the advertisement, and it’s a pleasure to observe how people from different parts of the world, of different professions, describe the idea of the product in their own words and suggest their friends to try it! And we also employed a couple of simple tricks: (1) including 'via @twttimes' in retweets from newspaper; (2) a big button “Tweet your newspaper” so that you can send the link to your newspaper on Twitter with the message “Check out my fresh twittertim.es newspaper”. It makes sense to press the button regularly because newspaper is becoming “fresh” every 2 hours.
The important thing is that new twittertim.es subscribers are not arriving evenly. Instead, it has always been like waves: when someone influential praises twittertim.es – we have new wave of subscribers. Harnessing, stimulating the right influential nodes in network can dramatically change the speed of propagation of knowledge in social network.
So, if sharing product recommendations via conversations is a natural people's activity, why not to build a conversational advertisement platform upon a social network?
Of course, this idea is going around in different forms currently. John Battelle and his company, Federated Media, is working on aggregating blogs into a conversational media platform where brands can advertise. They are working on estimating people's “engagement”. From FM's white paper: “Engagement” is the term we use to describe all the various ways that consumers interact with your brand and your campaign assets. This includes traditional metrics such as page views, click-through-rates and time spent, but it also includes a new set of metrics unique to conversational media such as posts, trackbacks, votes, RSS subscriptions, comments and more”. See TechCrunch post about FM's 'Conversation as An Ad'-platform here.
Look at Federated Media's latest product: ToyotaConversations. It is a channel that features what is being discussed about Toyota on Twitter, and negative stories are not filtered out. This channel is an attempt to participate in a conversation with consumers and to influence how the brand is viewed.
What does Conversational Advertisement Platform consist of:
Who can advertise: Identifying right influential personalities in a social network. This problem has got a lot of attention among data mining researchers. TunkRank metric was proposed by Daniel Tunkelang for computing global influenceability of a person on Twitter. TunkRank computes some global measure of influence. For any Twitter user it computes its absolute score based on number of followers, mentions, retweets.
Here is another research on calculating influence on Twitter (The Million Follower Fallacy: Audience Size Doesn't Prove Influence on Twitter).
I really think identifying some global influenceability is a wrong way. We need to identify topical communities, and find central, influential personalities in those communities. The paper “TwitterRank: Finding Topic-sensitive Influential Twitterers” - is an attempt to do that. From the paper: “For example, a handphone manufacturer can engage those twitterers influential in topics about IT gadgets to potentially influence more people”.
Whom to advertise: Identifying target audience for a ad. Analyzing of what people tweet can help understand what they need. It’s clearly that its best to receive a product recommendation from your friend – so, returning to the previous point – we can identify the right personality to advertise among person's friends first. Authors of “The Dynamics of Viral Marketing” automatically find network communities (using graph theoretic community finding algorithm) and observe that communities usually centered around a product group such as books, music, or DVDs, but almost all of them shared recommendations for all types of products.
Bidding auction model for social network advertising. Google AdWords is based on generalized second-price auction. This model can be adapted for a social network, substituting Web-page rank with Twitter user rank. Ad.ly already has something like that for Twitter. I'll write a separate post on that soon.
Brand monitoring in social networks. Understanding how information about a brand is spreading over the Twitter, what nodes influence on the brand image. Using sentiment analysis techniques. Identification of blog posts/news articles that influence people's opinions about a brand.
So, in general the platform would allow to find : who, whom and what to advertise.