Welcome to this week’s Brilliant Reads, where we’re making strategy a system, and looking at the most common types of Twitter conversations.
Make strategy a system (CMO.com)
In this article for CMO.com, our own Antony Mayfield argues that the word ‘strategy’ has been misused and overused to the extent that it has come to mean everything and nothing at the same time.
He writes that leaders need to rediscover and reinvigorate the potential of strategy in their thinking and the way that their teams behave. This requires more than just adding the word ‘strategic’ to existing structures and plans – it means taking time to reimagine your business and explain it to a whole organisation in a ways that create an imperative for action.
Antony suggests that thinking of strategy as a system, not a plan, can be a “lightbulb moment”. A strategy system evolves, moves and changes, it’s open, and it keeps strategy alive and present as a part of daily work.
Image credit: stefan.erschwendner via Flickr
This piece of research from Pew looked for patterns in discussions on Twitter. It examined thousands of maps of hundreds of subjects and events, and found six distinct network structures:
1. Polarised Crowd: Polarised Crowds are characterised by two big and dense groups, with very little connection in terms of conversation, links or hashtags between them, even though they are discussing the same divisive subjects.
2. Tight Crowd: Tight Crowds are characterised by highly interconnected people, like those that form around conferences, professional topics or hobby groups. They show how networked learning communities function and how sharing and mutual support can be facilitated by social media.
3. Brand Clusters: Brand clusters form around well-known brands/people or other popular subjects/topics. A large number of people may all be tweeting about the same thing, but individually, rather than to each other. The larger the population talking about a brand, the less likely it is that participants are connected to one another.
4. Community Clusters: Some popular topics may develop multiple smaller groups, which often form around a few hubs with their own audience, influencers, and sources of information. These can illustrate diverse angles on a subject based on its relevance to different audiences.
5. Broadcast Network: A distinctive hub and spoke structure forms around news sources, as many people repeat what they tweet. The members of the Broadcast Network audience are often connected only to the hub news source, without connecting to one another.
6. Support Network: When brands/organisations offer customer support on Twitter, it produces a hub and spoke structure, where the hub account replies to many otherwise disconnected users, creating outward spokes. These support network structures can become an important benchmark for evaluating the performance of these institutions.
Image credit: Pew Research via NodeXL
What search data reveals about perceptions of your brand (Think Insights)
This article from Google Think Insights looks at what search data can reveal about public perceptions of brands.
We often think of a “search” as a singular occurrence. In reality, however, people often use a series of queries when they search. This means that each search session contains a combination of terms (or “co-searches”). These can naturally uncover connections between brands, topics and objectives. When you look at in aggregate, this data offers a reflection of how and when consumers are thinking about a brand.
This information can be used in a number of ways – for example informing new partnership opportunities, highlighting natural spokespeople, testing the strength of brand associations or informing future positioning.
BBC’s content is the UK’s most shared (PeerIndex)
The BBC’s content is shared more than that of any other UK website, according to PeerIndex’s UK Media Social Sharing Index. Its content was shared more than 4.2 million times by Twitter users in the UK in January 2014, dwarfing its closest competitor, the Guardian, whose content was shared 2.4 million times.