Speakers
Description
Influencers Matchmaking for Startups B2B-Branding interactivity: Case Studies of Linkedin Data Mining
Marketing in the fourth industrial revolution offers a global opportunity to the rise of micro-influencers. Influencer marketing allows any social media user with particular influence within the social media platform to help amplify and circulate the campaign of the brand to the influencers’ audience. Especially with more business decision makers on social media, social media provides an advertisement opportunity for B2B brands. LinkedIn as a social platform that is primarily used for professional networking offers a suitable environment to promote B2B brands mainly by its functions that are built around helping firms create brands, build relationships and connect with existing and potential customers. However, not only that influencer-marketing for B2B brands is not widely practiced on LinkedIn, it can be challenging to perform with the tremendous amount of data available and lack of effective approach to run a successful micro-influencer campaign.
This research proposed a data analysis approach in clustering and classifying the available data provided on LinkedIn to provide marketers actionable insights on connecting with the right micro-influencers to conduct influencer marketing campaigns. This analysis will leverage the data provided by LinkedIn by using a web scraping method. The data acquired from the user’s activity will then be classified and analyzed to determine potential matches between micro-influencers and brands. The purpose of this study aims to break through the new approach of influencer marketing, particularly for B2B brands on LinkedIn.
To test the hypothesis of the proposed research question, Job to Be Done (JTBD) analysis will be implemented to help understand stakeholders behavior of improving this research. The research identifies and gathers the target users based on the persona, which then used to design the most suitable content marketing and respective incentives to match the influencer marketing concept. To enable the analysis, data from LinkedIn API will be retrieved and analyzed to provide actionable insights for the user to connect with potential brands and influencers. In the end, brand interactivity will be measured through the LinkedIn post performance analytics. The target participants will be B2B brands and all active professional users with various degrees of social media presence on LinkedIn.
The proposal discusses the usage of web scraping and data mining to create a pointer that allows influencer marketing approaches to become more targeted and data oriented on LinkedIn. We conclude with design consideration on creating an influencer management platform for B2B brands on Linkedin that offers effective advertisement opportunities to help brands grow and increase the brand credibility. The proposed analysis will help brands to make decisions through real-time visual reports of the campaign and available pool of micro-influencers.
Keywords: Data mining, influencer marketing, LinkedIn, B2B brands, brand interactivity.
