Thursday, 5 March 2009

CHANGING THE LANGUAGE OF ADVERTISING TO REFLECT MOVE TOWARDS MANAGING COMMUNITIES


Some of you who have followed my blog for a while will remember I had a debate with Mary Beth Kemp of Forrester on her paper about the Connected Agency (paper is here). In her paper she says "Today's agencies fail to help marketers engage with consumers, who, as a result, are becoming less brand-loyal and more trusting of each other. To turn the tide, marketers will move to the Connected Agency — one that shifts: from making messages to nurturing consumer connections; from delivering push to creating pull interactions; and from orchestrating campaigns to facilitating conversations. Over the next five years, traditional agencies will make this shift; they will start by connecting with consumer communities and will eventually become an integral part of them."

This prediction is becoming true with the increasing ubiquity of social media, and its growing presence in budgets. But with this change will come a change in the language we use for creative and media strategy in the act of community management.

As you can see in the diagram below the relationships between people in a community can be graphed and analysed. So in addition to talking about cost per thousand, impressions, and cost per click we will now have to add the lexicon of social network analysis to the language we use today in advertising.


Here are a few social network analysis metrics taken from Wikipedia:
Betweenness
The extent to which a nodes lies between other nodes in the network. This measure takes into account the connectivity of the node's neighbors, giving a higher value for nodes which bridge clusters. The measure reflects the number of people who a person is connecting indirectly through their direct links.
Bridge
An edge is said to be a bridge if deleting it would cause its endpoints to lie in different components of a graph.
Centrality
This measure gives a rough indication of the social power of a node based on how well they "connect" the network. "Betweenness", "Closeness", and "Degree" are all measures of centrality.
Centralization
The difference between the number of links for each node divided by maximum possible sum of differences. A centralized network will have many of its links dispersed around one or a few nodes, while a decentralized network is one in which there is little variation between the number of links each node possesses.
Closeness
The degree an individual is near all other individuals in a network (directly or indirectly). It reflects the ability to access information through the "grapevine" of network members. Thus, closeness is the inverse of the sum of the shortest distances between each individual and every other person in the network.
Clustering coefficient
A measure of the likelihood that two associates of a node are associates themselves. A higher clustering coefficient indicates a greater 'cliquishness'.
Cohesion
The degree to which actors are connected directly to each other by cohesive bonds. Groups are identified as ‘cliques’ if every individual is directly tied to every other individual, ‘social circles’ if there is less stringency of direct contact, which is imprecise, or as structurally cohesive blocks if precision is wanted.[18]
Degree
The count of the number of ties to other actors in the network. This may also be known as the "geodesic distance". See also degree (graph theory).
(Individual-level) Density
The degree a respondent's ties know one another/ proportion of ties among an individual's nominees. Network or global-level density is the proportion of ties in a network relative to the total number possible (sparse versus dense networks).
Flow betweenness centrality
The degree that a node contributes to sum of maximum flow between all pairs of nodes (not that node).
Eigenvector centrality
A measure of the importance of a node in a network. It assigns relative scores to all nodes in the network based on the principle that connections to nodes having a high score contribute more to the score of the node in question.
Local Bridge
An edge is a local bridge if its endpoints share no common neighbors. Unlike a bridge, a local bridge is contained in a cycle.
Path Length
The distances between pairs of nodes in the network. Average path-length is the average of these distances between all pairs of nodes.
Prestige
In a directed graph prestige is the term used to describe a node's centrality. "Degree Prestige", "Proximity Prestige", and "Status Prestige" are all measures of Prestige. See also degree (graph theory).
Radiality
Degree an individual’s network reaches out into the network and provides novel information and influence.
Reach
The degree any member of a network can reach other members of the network.
Structural cohesion
The minimum number of members who, if removed from a group, would disconnect the group.[19]
Structural equivalence
Refers to the extent to which nodes have a common set of linkages to other nodes in the system. The nodes don’t need to have any ties to each other to be structurally equivalent.
Structural hole
Static holes that can be strategically filled by connecting one or more links to link together other points. Linked to ideas of social capital: if you link to two people who are not linked you can control their communication.

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