Social media is an integral part of our lives. Every consumer you want to target is either making a purchase, checking into a restaurant or planning a holiday or hiring a cab online. Facebook alone has over 1 billion users and on any given day twitter sees over 500 million tweets! Reports suggest that 73% of millennials feel that it is their responsibility to help friends and family make smart purchase decisions.
Consumers are strongly opinionated and the way they are influencing each other online is creating a whole new type of marketing, which we now call ‘Social Influence’! There is a very clear and binding relationship between social influencers, the brand and the buying decision.
Traditionally, Brand awareness was done through a combination of targeted ads and celebrity endorsements. However, today a new trend that is fast emerging is identifying influencers—who are already talking about your brand and to use them to promote products and services within their social circles. Though this technique is cost-effective and highly targeted, the challenge is to identify these key influencers that talk specifically about topics related to your brand.
What we need is a reliable and transparent quantitative method. A good indicator of a valuable influencer is his/her ‘Klout Score’. This score is based on overall social activity of an individual and not on specific topics (e.g., Obama has a Klout score of 94 on 100. This is his score regardless of which topic he is talking about).
But, we need to have an Influence scoring algorithm that will address the shortcoming of Klout by scoring the influencer by content sources and specific topics. This approach gives content marketing companies the maximum flexibility to discover and engage with social influencers, and through them promote goods and services.
THE INFLUENCE SCORING SYSTEM
The influence score can be computed through a statistical technique called Principal Component Analysis (PCA). Simply put, PCA is a technique, which enables a high number of variables/dimensions (i.e., reach and engagement metrics such as, likes, followers, re-tweets, favorites, etc.) to be described adequately by a smaller set of dominant variables/dimensions (Specific topics/subjects that we are looking at) without any loss of information.
With PCA you can compute the influence score for a specific social media site and category/topic combination. Here is a step-by-step example to spot social influencers on twitter:
Step 1: Get the metadata (e.g., Barrack Obama—Followers: 55 million, Following: 650k, Tweets: 13.1k).
Step 2: Collect metadata for a particular set period (e.g., it can tweets for the last week, or month or a particular period maybe 3 months, etc.)
Step 3: Merge the collected metadata with ‘Post’ level metadata as shown below:
Twitter Dataset used for Politicians
Step 4: Compress the dimensionality (i.e., Facebook likes, tweets, etc.) and compute weightages using PCA
Step 5: Get down to the granular level of data. This will help you understand exactly what the data represents and why the individual you have chosen ranks as a social influencer for your brand.
Step 6: Explore the granular level of data individually for all members for the set category and rank them as shown in the table below:
Result
PCA helps you derive the ‘distance’ values and transpose to a scaled score from 1–100. In the above tables the Klout score is shown for comparison purposes only. By repeating the above process for different social media sites and categories/topics, we could compute the Influence Source, by each topic.
Whatever your niche, you can engage with highly ranked influencers to create/promote brand awareness and foster loyalty by giving your audience exactly what they expect from your brand.
