Analysis: YouTube’s Influencer Marketing Phenomenon
If you’ve not yet seen a YouTube microinfluencer unbox a mystery slime box, or a nanoinfluencer review squishies from a Dollar Store haul, let’s face it… you’ve lost a step. But we can help!
Welcome to YouTube influencer marketing in America, where adults and kids alike make millions of dollars recording themselves as they play with toys, put on makeup and push carts around Costco.
What follows is a report on the activity of a subset of influencers (and aspiring influencers) who produce two of the most notable staples in a YouTube influencer’s repertoire: “unboxing videos” and “haul videos.” More than a thousand of these videos are produced each week, and top influencers count their total views in billions. Before we reveal our findings, let’s define some terms:
Haul video: Video in which a person discusses a number of products they've purchased, typically on a bulk shopping spree, known as a “haul.”
Unboxing video: Video in which a person opens a package with a product in it, then reviews and/or uses the product.
Macroinfluencer: Person with a million or more subscribers on YouTube.
Microinfluencer: Person with between 10,000 and hundreds of thousands of subscribers.
Nanoinfluencer: Person with less than 10,000 subscribers, and as few as 1,000.
In October 2018, we analyzed the 3,000 most recently uploaded “haul” and “unbox” videos on YouTube. Our analysis begins by looking at what these two types of videos focus on, in terms of product categories and brands.
One primary characteristic of these videos is they rarely conclude with negative impressions of products or brands. While that’s certainly changing as consumers become savvier to the sponsorship dynamics, most videos default to enthusiasm and open-mindedness, or at worst, indifference.
What’s exciting for brands right now is the potential return they can get working with nanoinfluencers. There’s a built-in authenticity that doesn’t necessarily come with bigger YouTube personalities, and without larger audiences, nanoinfluencers can’t demand much money from the brands (if any). When deployed en masse, brands can aggregate influence that’s cheap and authentic. Let’s take a closer look at the influencers, in all shapes and sizes.
One of the most fascinating parts of this story is the potential money to be earned by influencers and microinfluencers. With help from SocialBlade, which offers a cost-per-1,000-impressions (CPM) calculator, we draw relative comparisons between different caliber influencers (and aspiring influencers). Keep in mind though, for the most popular channels, revenue is largely generated by sponsorship deals with brands, most of which are not made public.
Remember, while this content may be new to some, and the production quality and breadth of coverage has evolved significantly in recent years, neither of these types of videos are overnight trends, nor is the involvement of brands. According to Yahoo Finance, the first unboxing video featured a Nokia cellphone in 2006, and J.C. Penney was sponsoring haulers as early as 2010. What is relatively new is the emergence of nanoinfluencer-based strategies, which will undoubtedly cause this market to continue to grow in coming years.
Methodology: In October 2018, we conducted an analysis of more than 1,500 YouTube (YT) user channels and 3,000 YouTube videos, featuring either the word “haul” or “unbox.” Using tools Python and ScrapeBox, data was scraped both from YT and social analytics site SocialBlade (SB). Various YT search filters were employed, including “Today,” “This week,” “This month,” and “View count.”
Data collected from YT includes—at the video level—video title, username, channel ID, upload date; and at the channel level, first video date. Data from SB includes uploads, subscribers, total views, country, and estimated yearly earnings.
Categories were created and assigned by our team, as were descriptions of Top Earners. In order to scrape a representative sample size, we conducted several strict “This week” and “This month” searches for each term, each time ensuring the search algorithm did not default to a “Relevance” view or deliver most popular results.
Earnings data was scraped from SB, which uses this formula to calculate their per-channel ranges. For our report of “Top Earners,” we used the upper limit of the range for each channel, and for all other earnings reports we used the middle point of the range.