Up to 85% of brand generated social media posts are wasted. With digital advertising approaching 50% of all advertising expenditure, we need to go beyond clicks and likes as a measure of ROI on social media expenditure. Through machine learning, a brand can now see how many posts support their intended position, how many are off target, and how many are simply ambiguous. This presentation demonstrates how the AI tool works, highlights the role of archetypal alignment across touch points, delivers a practical framework where brands can specifically identify the nature of their optimal social media imagery, and concludes that understanding archetypal codes is the key to optimising ROI in Social Media.
In this research we focused on conversion which is the most immediate KPI: Does a sponsored vid edited by an influencer, engender more traffic (more impression) on the (sponsoring) brand website? Does it have an impact (immediate or not) on the shopping behaviour of the audience? Do influencers increase the impression rate? This is the main and simple question we wanted to address in this research.
In this research we focused on conversion which is the most immediate KPI: Does a sponsored vid edited by an influencer, engender more traffic (more impression) on the (sponsoring) brand website? Does it have an impact (immediate or not) on the shopping behaviour of the audience? Do influencers increase the impression rate? This is the main and simple question we wanted to address in this research.
In a world where there's increasing fragmentation and companies are drowning in data, having practical business solutions that connect and make sense of big data as well as humanise data, is essential. We believe collaboration is imperative and have developed a thriving partner ecosystem so that our data works harder for our clients, across more business applications. Through specialist algorithms that anonymise and aggregate data, we are able to connect our market insights and segments to large media owners and platforms such as Google, Facebook, Sensis (Digital/Yellow page business directory), Australia Post and Australia's market leading telco Telstra.
Whozini.com, a start-up from USA, wanted to launch a social media mobile app. Being techie, they already had a product in mind and wanted to directly go into MPV Phase. The challenge was to show the value and contribution of an exploratory phase before testing. They also wanted to do research mostly digitally and in the most cost-effective way. For this, we created an MR Mobile App for 360 degree NPD & Innovation work. The result? Whoozini.com won the Innovation Award in one of the biggest start-up events and has subsequently launched the app in the USA & SEA regions!
In this paper we describe the experiment we conducted using solely pictures, including key learnings from such a methodology, and its implications for future market research projects. We also discuss the need for market research to adapt to new real-life communications methods, such as augmenting pictures using social media apps.
How to extract valuable insights from a noisy medium such as Twitter? Our approach is to use artificial intelligence to extract semantic features from the data. Once these features are extracted we can use machine learning techniques to extract valuable insights.?Our approach can be used to visualize how ideas are entangled inside a community's conversation as well as to identify the main themes in a corpus. Finally, it can be used to classify and track the evolution of specific topics in a Twitter stream. We'll provide guidelines and examples for the utilization of this methodology in a market-research context. Additionally, we introduce some applications of the proposed methodology to analyze two big 2016 social events: the U.S. presidential election and the Brexit referendum.