Everyone, it seems, is seeking market “influencer” status to enhance personal and organizational success. And marketers, especially content marketers, are seeking the influencers, in turn, to more effectively reach their audiences and their goals. For something that has become so universally pursued, the concept of the influencer doesn’t have one definition, though. Depending on the topic or market, and depending on who is doing the defining, who or what constitutes an influencer can be infinitely varied.
When I was alerted that IT Business Edge was on Onalytica’s recently compiled list of its Top 50 Big Data Influencer Brands, I reached out to Arthur Hilhorst, Onalytica’s digital marketing lead. Hilhorst, who compiles many of the firm’s influencer lists along with the data team, said Onalytica defines influencers in a variety of ways:
“An influencer is a person who has influence over a certain target market. This market influence typically stems from an individual's popularity, expertise or reputation. Influencers can be vendors, competitors, media publications, analysts, journalists, thought leaders, bloggers or key consumers.”
Onalytica is a London-based influencer marketing company that offers both influencer identification and influencer relationship marketing services to its clients. It counts among its clients SAP, Fujitsu, Sony, VMware, HP and Microsoft. And it walks the walk, as well, in creating a steady stream of its own influencer lists and other market analysis, much of which you can find in its blog.
Lists like the Top 50 Big Data Influencer Brands mentioned above, says Hilhorst, are created “based on our own interests as a company and we often create lists in sectors where we already have a number of clients or prospects. We also create lists based around certain events: think about VMWorld, CES2015 and IFA (in Europe) to name a few.”
I asked about the advantages and disadvantages of crunching Twitter data, which was the basis of the Big Data list, and how Onalytica decides to combine that data with other sources for better results. Hilhorst says it is often a great starting point for many goals.
“Twitter data is of high quality and even the smaller hashtags can easily contain up to 50k tweets in any given month, driving very engaged and interesting discussions even in niche topics. Data from Twitter is accessible to us through a third-party provider and that allows us to store complete tweets history for further data mining. This data can then provide an individual or a business with great insights into what certain communities are talking about. Once a business or an individual has a good understanding of a discussion, it can then start influencing that discussion with its own messaging and content.
At Onalytica, we build software designed to manage influencer relationships. Our software also allows blog data to be compiled on top of Twitter data. Most influencers are also keen bloggers and combining this data allows clients to gain a deeper understanding into certain influencers in order to better tailor engagement and increase overall brand share of voice among key influencers. We also analyse the clients’ content and match it against their selected influencers to determine which influencers will be most interested in receiving content on a certain topic.”
But it’s not the end-all, be-all, of course:
“Twitter is just one of many social networks out there and does certainly not represent the entire internet. Channels like Google +, LinkedIn and Facebook contain very engaged communities of influencers as well. This is the reason that we always specify that our lists are not the ultimate measure of influence, as we think such a thing does not exist. It’s key for any business wanting to leverage influencer marketing that they clearly define their goals and establish a strategy before worrying about follower count and social scores.”
So how does a company, or a provider like Onalytica, go beyond follow count to influencer criteria that is meaningful?
“When we analyse our data (ranging from 150k to 500k tweets), we focus not only on follower count but we use our own PageRank-based methodology to determine how an influencer or brand is connecting and engaging with the other influencers out there. The influence of a Twitter user is topical (we generally pull data surrounding a hashtag and/or mentions, #BigData, in this case), and our calculations take into account the number and quality (i.e., influence) of contextual references that a user receives.
Next to these data-based criteria, if a company is keen on exploring influencer marketing, it is of high importance that they look at their own needs. Based on what outcome you want to achieve, brand awareness, lead generation or increasing sales, all require very different approaches and it is key to tailor and optimise engagement at all stages of an influencer marketing campaign.”
Kachina Shaw is managing editor for IT Business Edge and has been writing and editing about IT and the business for 15 years. She writes about IT careers, management, technology trends and managing risk. Follow Kachina on Twitter @Kachina and on Google+