A next-gen strategy for strong ecosystems
Cord-cutting and mobile ubiquity, coupled with an increasing distrust towards brand messaging, are fuelling the growth of influencers, marketing led by social media influencers including micro-influencers.
With followers between 1,000 and 10,000, micro-influencers have created a niche owing to their relevant and reliable content and commitment towards audience interests. From being an industry dominated by handpicked celebrities, the consumer packaged goods (CPG) industry is now proliferated with a digitally empowered general populace.
Given the vast scope of the influencer marketing field, we look at how a combination of machine learning algorithms along with distinct social media key performance indicators (KPIs) can help to identify authentic influencers.
Influencer marketing driving transformation 4.0
Case in point, while celebrities just deliver scripted lines in commercials, influencers are believed to be more engaged with the products they endorse on social media. However, there are numerous risks involved in adopting the influencer marketing model, as this endangers the brand to a higher level of scrutiny.
That said, what a brand achieves with honest feedback is trust among its customers. Also, influencers can take the creative and storytelling beyond commercials. Influencer marketing as a strategy is still nascent and brands are yet to identify best practices to reward, compensate and onboard influencers.
Usually, CPG brands engage influencer marketing agencies to execute their campaigns. Agencies possess a database of influencers, and this may present a challenge to brand marketers as it limits their choice. Additionally, the process of selection lacks transparency and governance.
Another method that brands have started to explore is the opt-in influencer networks, where they can build long-term brand advocacy by onboarding influencers who do not switch brands frequently. However, this is not a flawless approach either; it's often difficult to appoint an influencer based on their network size as there is no way to tell if their network growth is organic. A spate of fraudulent practices have made it difficult for brands to measure the RoI on influencer marketing campaigns.
The need of the hour is a comprehensive machine learning framework which helps brands right from onboarding influencers to measuring campaign outcomes.
Building a robust framework
While campaign objectives determine the weight on each KPI, the proposed framework leverages natural language processing (NLP) and image recognition techniques to validate the legitimacy of the content curated by the influencer. Below are some of the KPIs that should be considered before arriving at the influencer index score.
The framework uses an end-to-end comprehensive algorithm armed with several social media KPIs to arrive at an influencer index score. The score ensures that shortlisted profiles have undergone a stringent validation process that checks parameters like engagement pods, bot-generated comments, followers-to engagement ratio, and anomalies like an abrupt increase in followers.
A framework for strong relation with influencers
Though social media channels such as Twitter, Instagram, and Facebook have taken measures to identify fake accounts and have passed a mandate to call out sponsored posts, these issues are far from resolved. Our proposed framework, equipped with distinct social media KPIs, helps brands to work with transparency and control the process. Brands will no longer be restricted to select influencers from an opaque repository. Consequently, they will be able to build a lasting relationship with influencers that promotes trust amongst consumers.