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It would seem like I’d say no to that question immediately because who wouldn’t be better at understanding how another human being might influence others than a human being who leads the marketing department for a company? After all, we know ourselves best, right?
Well, the only problem is that machines are capable of taking in significant amounts of data in a relatively short time – maybe even minutes – that might take us years to process. In that time, machines can also learn from all that they are digesting and become more effective at identifying patterns or making connections that we’d never see. This could prove really helpful when, as a CMO, you are struggling to figure out where to put your energy and expertise.
So, I’d then have to shrug my shoulders and say, I guess machines could be better at matching people up, in terms of who could influence who. However, it has yet to be proven, so maybe there’s still hope for CMOs. Or, maybe the answer is to team up with machines to solve today’s marketing dilemmas.
Watson, I Presume
That is until IBM’s Watson started teaming up with various brands and their marketing teams to serve as a matchmaker on everything from diamond rings and tires to vehicles and vacations. This AI-powered machine is busy learning everything about everyone and then disseminating it into digestible bites for companies, so they make decisions that have the customer thinking that brands can now read their minds.
Brands can now, more quickly, leverage the power of influencer marketing, thanks to AI, which helps find those social media moguls that everyone looks to for answers on what they should do, how they should dress, and what they have to buy. Although companies have popped up that offer brands a way to search a database of social media influencers, others like Influential, in partnership with IBM Watson, have taken it to the next level.
By automating the process and using machine learning, it’s no longer about finding some influencer that has power over many people; it’s about matching them with those that they could influence the most in relation to the brand’s product or service.
Laser-Like Focus
This means that you’ll be able to have a laser-like focus to hit the bull’s eye on the influencer target, thanks to artificial intelligence. Being able to find the right influencer for each person is a game changer and something that could be truly impossible for humans to do on our own.
Influential was built by an influencer who recognized the shortcomings of platforms that were based on human intelligence. Ryan Detert and his partner, Dan Steele, worked with a team of developers to create Influential, which now has a network of over 15,000 of the most highly engaged influencers across numerous social media channels. However, adding IBM Watson as a Developer Partner in 2015 really changed what Influential could do, in relation to how well it performed its influencer and brand matchmaking.
In using demographics, contextual relevance, and psychographics, with the help of IBM Watson, Influential could generate much more personalized findings, thanks to the significant data that identified personality, interests, and emotional character, by analyzing certain factors and studying their social feeds. IBM Watson’s Personality Insights API and Natural Language Processing (NLP) can then take this data from social media and index each person, based on the Big 5 Personality traits. The Big Five is a long-used psychological theory that is based on the idea that there is a set of 52 specific characteristics that comprise a human’s personality traits. These traits include adventurousness, hedonism, curiosity and gregariousness, just to name a few.
Better Matches Equals Higher Success Rate
Influential uses IBM Watson to simultaneously analyze the brand or company that is looking for an influencer for a particular campaign, as well as an influencer to locate patterns that might make them a good match for each other. What this has done has created better matches that yield a higher success rate from an influencer marketing campaign.
Two real-world examples prove that point. The first is IBM Watson and Influential’s partnership with Mazda North American Operations. They are in the midst of a campaign in which Mazda’s existing customer insights will be analyzed by IBM Watson to select the most relevant influencers to participate in a social media campaign for the new Mazda CX-5 at SXSW 2017.
Another recent example is Influential’s partnership with Condé Nast that allows brands advertising with the media company’s properties like Vogue, Vanity Fair, and The New Yorker to tap into the data to determine which social media celebrities might work for a specific campaign. In an article in AdWeek, this AI-powered matchmaking service was described in detail:
“For example, if a brand wants to find somebody who’s adventurous, Watson—which can analyze the last 20,000 words and emojis an influencer has published—helps weed out those that are more prone to doing vlogs from their couch. If a brand wants to pitch an action film, they might input keywords like “action” and “explosion” to see which influencers have used them the most. Once the data analysis is complete, Watson then picks out five, 10 or a few dozen candidates for humans to choose from.”
While the results are still being gathered from these partnerships, the brands involved see promise and potential in looking to machines over humans to make the type of matches that result in greater engagement, loyalty and purchase decisions.
This article originally appeared on Forbes.
