Launchmetrics’ proprietary Machine Learning algorithm provides brands with a unified currency to measure the value of all marketing activities across Voices, Channels, and Markets by assigning a monetary amount to every post, interaction, and article. Finely tuned to specificities of Fashion, Luxury, and Beauty, the algorithm was trained on actual media rates and 5+ years of FLB specific campaign data. It analyzes more than 100 quantitative and qualitative attributes including audience engagement, industry relevance, source authority, and content quality, to create a highly accurate method of measurement. MIV offers a unified way to calculate how brand equity is being created and which strategies create the most ROI.
The MIV formula changes regularly to follow the industry evolution and stay relevant with new use cases. Our goal is to provide you with a benchmark metric well balanced between stability and accuracy, to enable you to run analyses while also being the most up to date possible.
Depending on the magnitude of the changes, they can be effective for data moving forward or historical data can be re-computed.
Online
To calculate MIV online, one of the attributes is a media score based on the relevancy of this media for your brand. Media that are FLB focused (Vogue, Cosmopolitan, Elle) and more valuable to your brand have a high score compared to less relevant media for you (generalist media such as the Daily Mail).
While a weight was always applied to FLB media, we have reviewed and boosted a number of FLB medias higher than other generalists. We rebalance the fact that the audience is smaller because it’s much more relevant to your brand by boosting those FLB media. This will allow for more differentiation between FLB and non-FLB MIV.
Expected impact: Growth of very FLB oriented media MIV such as Vogue, and decrease in MIV for generalists media such as the Daily Mail. Small impact on Online and on global MIV for the brand.
Social
(Instagram)
Our algorithm detects giveaways on social media which artificially raise the engagement of a post (number of likes, comments, views and shares) and applies a discount to the engagement account to calculate MIV.
To better detect these giveaway posts and provide accurate MIV we’ve updated the list of words the algorithm uses to flag them.
Expected impact: More giveaways detected on Instagram. Small impact on Social and on global MIV for the brand.
Social
(YouTube)
Brands' MIV coming from YouTube music videos is very high when the brand is mentioned in the lyrics. The reason behind is that music videos have a higher amount of views due to the audience listening to the song and therefore watching the video repeatedly.
To balance this behaviour we don't account views on music videos the same way as for any other influencer or media video.
Expected impact: Lower MIV for Youtube music videos with a high number of views. Small impact on Social and on global MIV for the brand.
Social
(Facebook)
Multinational brands have one global page on facebook from which they can publish content targeted at different regions. In order to avoid counting the full global audience of the page for each local post, we implemented a change in the formula to reflect the true impact of the posts.
The balance between engagement and audience is more tilted towards engagement so the audience doesn’t weigh too much in the formula.
Expected impact: This change will heavily lower Facebook Owned Media MIV for brands that have a global FB page with different regions. Big impact on global MIV for the brand.