Author Archives for Murray Legg

Calculating influence on Webfluential across reach, resonance and relevance

The basis of the ecosystem of influencer marketing, or bloggertising, is high quality content. Behind the content, Webfluential algorithms offer a mathematical solution to marketers asking the question about who is right to talk about their brand, given the age group, location and interests of the target audience. Webfluential offers the quantitative reasoning behind the ranking of digital personalities and publishers best suited to influencer marketing campaigns. Still, a qualitative reasoning needs to be taken into account when considering influencers and is the service typically provided by the creative or digital agency working with a brand. The platform will suggest influencers, but the marketer has the ultimate say in who is contracted.

One of the most common queries marketers and influencers alike have about influencer marketing and the Webfluential platform is about the methodology that ranks these influencers. This post aims to provide clarity to the question, and earn favour from the truths that mathematical algorithms provide.

In much the same way that search engines don’t reveal their weighting of factors within algorithms, they do give guidance on what affects organic search ranking. We intend to follow the same course of action, by providing guidance on the metrics that affect influence – as far as we are able to calculate it.

The starting point of an analysis of an influencer is their reach. Reach, or unique visitors, followers or fans, is the verified number of people interested in or relying on a social channel for news or information. This audience is interrogated to understand their age demographic, location and interests. Depending on the API, this information is considered in various formats, but is typically the information given to you when looking at your Google analytics.

Once the reach of an influencer has been defined, influence is calculated based on the impact with which that news and information shared with their audience is received.  We call this the “audience impact” and is comprised of the relevance and resonance of content that an influencer shares. A large, unengaged audience does not make for an influential digital personality.

Relevance is the measure of how engaged the audience is with content shared by an influencer. On Twitter, it’s the percentage of favourites and @replies relative to the audience size. On a blog, it’s the ratio of new to returning monthly unique visitors, rate of change of time on site and location and interest group demographic change.

Resonance is the ability of content to go viral – or be shared with audiences outside of the immediate audience following an influencer. Retweets and subsequent favourites; high growth in new unique visitors and keeping to the audience demographic and interest group, just growing in scale, are metrics used to calculate resonance. Facebook offers a page impressions versus page impressions (viral) metric that is a great measure of this – which you can see here.

On Webfluential, algorithms run every hour to rescore influencers across reach, resonance and relevance. It’s a process of relativity, where everyone on the platform is scored relative to each other, and ranked accordingly. As the social platforms work to share further insights through their APIs, we will use these to further qualify our calculations. What this does mean, for influencers and marketers, is that the influence score is something that is fluid, and changing on a month to month basis. This encourages influencers to maintain engaged and active profiles, and that the influencers selected for a campaign offer the best value to the marketer at that point in time.

Image credits: http://zachbussey.com/