The Future of Influencer Marketing and the Role of Artificial Intelligence
From the democratization of influence to leveraging influencers to becoming an influencer yourself, the industry of influencer marketing will continue to evolve hand-in-hand with the evolution of social media, media consumption, and technology. Now that we understand The Age of Influence, I would like to address a question that I often get asked after my speeches: What does the future hold for influencer marketing?
When I began working on this volume, it was my intention to write a book that would provide you sound advice many years after the publication. But the business world is being bombarded by a number of new technologies and their acronyms, such as the Internet of Things (IoT), Augmented Reality (AR), Virtual Reality (VR), Blockchain Technology, Crypto Currencies, and Artificial Intelligence (AI). I believe AI will withstand the test of time and help harness the future of influencer marketing.
At some point, influencer marketing for businesses will become part of a numbers game. It may happen quicker than expected, just as social media marketing in general did. The barrier for entry into becoming a social media user, and then developing and yielding influence, is low and has resulted in the emergence of hundreds of thousands of influencers today. This also means that, unlike traditional celebrity endorsements in which a brand may align with one or a handful of celebrities directly, new media segmentation and audience fragmentation force brands to engage with more figures who’ve built unique communities on different channels.
Simply because of the sheer number of potential influencers, there is a need to scale beyond human capabilities. Scaling this identification is essential to better understand many social media users’ influence over a wide range of communities discussing almost anything imaginable. This is where artificial intelligence (AI) comes in. AI is already helping companies make better influencer marketing decisions.
WHY AI IS A NATURAL MATCH FOR INFLUENCER MARKETING
Recently I had a chance to work with Open Influence (OI) (https://openinfluence.com), one of the influencer discovery tool companies recommended in chapter 13, who is also one of the leaders in leveraging AI for influencer marketing. OI was looking for ways to incorporate their knowledge and experience into a scalable solution helping brands match their needs with existing social media users’ communities, work, and culture. OI believed they could help brands unleash the potential for unparalleled ROI in influencer marketing. They recognized that they could achieve all this by incorporating an AI engine into their platform and programming it with unique algorithms for unsurpassed influencer identification.
What OI didn’t realize immediately was how the new platform fundamentally changed the rules of influencer marketing—for good. They combined searching by keyword and not by predetermined category, visually inspecting posts including brand mentions not in text, finding “fake” influencers, and even predicting contextual performance for a specific brand, influencer, and campaign. Eventually, OI realized they had unleashed a platform forever changing influencer marketing—and potentially online advertising in general.
Scalability inherent in this objective solution cannot be valued highly enough. Without AI, image tagging would be subjective, and the friction involved in manually tagging would be prohibitive. To understand the level of work: Look at the number of influencers, multiply that by the number of content posts to tag per influencer, and then multiply that by the number of tags per content piece. Finally, consider the frequency with which you would need to dynamically update the data. It’s an inhuman level of work and detail that simply cannot be done accurately by usual methods (see Figure 17.1). This is why AI was needed. Moving from a database of 5,000 to 100,000 influencers requires an obscene amount of time to properly and dynamically tag influencers and the ever-growing amount of content they publish. This was the challenge to which AI provided a compelling and scalable solution.
HOW AI REVOLUTIONIZES INFLUENCER IDENTIFICATION
Influencer identification continues to be a huge challenge for marketers. The sheer number of existing social media users and the volume of user-generated content presents an obstacle for proper identification. If you have domain expertise, possess experience in influencer marketing, and consider the different ways you could teach an AI-powered platform an objective method of identifying influencers, you will begin to realize how revolutionary AI technology is for influencer marketing today.
Figure 17.1
One of the key challenges is the industry-standard separate categorization of influencers. Brands have a need to identify the niche influencers who perfectly suit their demographic and needs, but currently the categorization is not sufficiently accurate. Influencer tagging rectifies this with searching for influencers not by broad categories but by specific tags based on actual content publishing and community-engagement history.
For instance, if an influencer was to self-categorize based on a preset list of categories, even though their “primary” type of posts were about fashion, they might also say they were into beauty, automobiles, and travel, to be considered for campaigns with a wider variety of companies. That same influencer might post a lot of fashion-related pictures. But those images might also be closely related to parenthood as they feature young children, or to sports as they showcase a new tennis outfit or swimwear at the beach. It’s this kind of niche that perfectly appeals to a handful brands, and the kind of niche that communicates directly to their target demographic. Deep and proper identification in this style would save brands time and energy.
To identify the “correct” influencer, influencers need to be tagged objectively based on the contents of their own images and video rather than potentially arbitrary categories based on text or hashtags. Any companies helping brands identify influencers are responsible for this due diligence in influencer-tagging categorization. It can only be done with recent advancements in image-recognition technology, with which we can select and cross-reference millions of contextual tags identifying influencers rather than a few dozen categories (see Figure 17.2).
Figure 17.2
BETTER UNDERSTANDING CONTEXTUAL ENGAGEMENT
Visually recognizing and dissecting an image is the gateway to uncovering unparalleled information from social media content. If an influencer posts an image with little text description and a generic hashtag like #tbt, it is simply impossible to determine the nature of the content and influence without image-recognition technology. To take this a step further visual media is often comprised of a collection of images, such as when someone is traveling in the mountains with skis mounted on a Mercedes-Benz SUV, potentially indicating three different tags.
Better understanding the contextual relationship between items in an image, their relationships with hashtags and other text being used, and different ways audiences engage with contextual relationships, helps you uncover deep insights simply not otherwise possible. Contextual engagement is another example of how image-recognition technology, once mastered, becomes an integral part of any technology platform including AI. A world of opportunities opens up for marketers to derive exponentially greater ROI from influencer marketing.
DEVELOPING ACCURATE LOOKALIKES
Facebook revolutionized social advertising in 2013 by introducing the concept of a “lookalike” audience.1 In Facebook’s words, a lookalike audience is “. . . a way to reach new people likely to be interested in your business because they’re similar to your best existing customers.” Within the scope of influencer marketing, these lookalikes help marketers to more quickly and accurately discover “lookalike” influencers such those influencers they have successfully worked with in the past or want to work with in the future. This can greatly reduce the time for influencer identification while helping to ensure greater success for an influencer marketing campaign.
Facebook lookalike audiences allow you to potentially find new fans and activity close to your current fanbase, visitors to your website, or even email database links. Meanwhile, AI-infused influencer marketing lookalikes allow marketers to begin with ideal influencers and model a campaign of lookalikes based on the content they post within minutes. This is another advantageous by-product of AI.
EXPOSING BOTS AND FAKE PROFILES
How AI identifies and exposes both bots and fake profiles so that marketers can steer clear is also critical. Recent fake-news scandals affecting American elections are a reminder: Every social media site is riddled with fake profiles and bots creating fake engagement and distorting numbers. Ensuring your influencer marketing program does not fall victim to these fraudulent activities is critical to your success. Fortunately, AI provides us unparalleled ways to find fake followers and expose fake engagement.
Inviting a fake profile with tens of thousands, or even potentially millions, of fake followers to be part of your influencer initiatives will add nothing to your ROI. In fact, it is estimated that fake followers in influencer marketing will cost brands more than one billion dollars a year.2 Of course, no one purposefully invites a fake influencer to a program, but how do you know how real influencers are? AI allows us to analyze patterns indicating potential “fakeness” of a profile, for instance, analyzing the quality of the followers themselves as well as trends in follower growth to determine whether or not this influencer is real or the result of systematic fraud, or buying fake followers to potentially create a lucrative business.
Another aspect of fake profiles AI helps identify include influencers with potentially disengaged audiences (though they may be real followers). Many individuals who wish to show signs of having influence try to game the system by buying fake followers, likes, or comments. They could also participate in groups of influencers who comment or engage with each other’s posts, so their content will feature more prominently in newsfeeds. A disengaged audience of an influencer might provide engagement on paper, but this engagement is gamed and has no actual business value to brands. Fortunately, AI can now identify patterns regarding the timing, nature, and source of engagement, so we can minimize the impact of disengaged audiences.
As influencer marketing grows, AI is becoming more and more critical to ensuring success for campaigns and programs. AI can help you accurately identify the “right” influencers while also uncovering potentially negative effects on your influencer marketing ROI. Similarly, AI raises social media content’s value for users. Properly leveraged AI helps you uncover unforeseen yet relevant content.
CONTENT IS THE CURRENCY OF INFLUENCER MARKETING, ENRICHED BY AI
Influencer marketing simply couldn’t exist without the content created by influencers. If content is the currency of influencer marketing, being able to analyze that content is critical for identifying influencers and how influential they are in categories in which they have already been tagged. Visual-recognition technology allows us to better “tag” influencers, extending this further down into the micro level of content.
As the world of social media is increasingly visual, the text-based approach of other AI solutions is simply not effective. Text analysis is inherently limited on a visual platform such as Instagram. Furthermore, influencers don’t post consecutive streams of thought in their image descriptions, often limiting thoughts to just a couple words and perhaps an emoji. Additionally, while marketers wish otherwise, hashtags aren’t always used by influencers in every post, and even when they are, there is no guarantee they are aligned with the images.
This means there is a need for technical analysis with image recognition. Image recognition has typically been done manually, with an account manager looking at posts and subjectively assigning categories. Different account managers undoubtedly have different understandings of what a category like fashion or fitness entails. For example, the type of fashion depicted by Forever 21 will be completely different from Chanel. Similarly, sport drinks and gyms both engage with fitness, but in very different ways.
HOW AI HELPS GUARANTEE RESULTS
AI raises the bar by virtually guaranteeing results for influencer marketing in an unprecedented way. In addition to helping with influencer identification and content analysis, when AI is integrated into a scalable database of influencers and influencer activity, it can also help marketers better invest their influencer marketing in a number of ways.
Finding the right influencers means there is a greater chance they will participate in your program and succeed.
Vetting influencers for inappropriate content is necessary before they embarrass your brand.
Campaign modeling can be used leveraging predictive analytics and past analysis to guarantee metrics parameters such as total interactions and engagement rate prior to even beginning a campaign.
Analyzing posting frequency ensures alignment between how much we can expect an influencer to post on our behalf and the related consumption threshold of their audience for such content.
Proper AI truly helps solve issues affecting influencer marketing today, and holds great potential going forward.