Let’s get the debate concluded. Influencer marketing is here to stay. And it is only getting bigger by the day. From the new kid on the block who wants to sell his drop-shipped tees to Fortune 500 brands, everyone is betting their money on this form of brand visibility.
However, like any marketing channel, Influencer marketing does come with it’s own perils and hazards. “Influencer” is now considered a profession(!) and with every other selfie-addicted college kid adding the tag to his/her bio, it’s become all the more difficult to identify who holds a pertinent audience and resonates well with the brand’s ideologies and goals.
Marketers are often unaware of the challenges that bug them while identifying the right influencer set for a successful campaign.
So why do you really need data science or analytics? Can’t good old Google help you find the right influencers? Are just follower numbers and engagements enough to justify your spends? Can the use of data science and analytics help marketers optimise and enhance their efforts and steer clear of the potholes?
Generic Influencers don’t make the cut. Relevance is the key.
Influencers come in varied sizes and shapes. Meaning, the key questions like “Why is this person influential?” or “What do the fans follow him/her for?” almost always are overlooked by marketers.
Unless the reason of what makes the person influential is answered, working with the influencer to tap their audience makes little to no sense. Moving beyond the common tags such as “Lifestyle” or “Tech” and analyzing data points like interests and content affinity of the audience will help draft a solid influencer recruitment strategy, directly resulting in a better brand perception.
Numbers DO lie. Analyse deeper.
The internet is flooded with influencers trying to vye the attention of brands. And it is no secret that higher the follower number of the Influencer, the better chance of getting a work order for them. And the follower number is directly proportional to the payout on the sponsored content as well.
This exerts a pressure on the Influencers and with black markets on the internet selling everything from likes, engagements to even comments (!) it is now difficult to tell who has a fake audience comprising of bots and paid followers. The authenticity of the followers are not just the numbers you see on Instagram so a smarter system can help isolate the wrong doers in a jiffy.
Master of One > Jacks of All Trades
An important aspect for brands is, looking at the right content creator or the expert who suits their requirement and the target audience they aim to reach out to. Identifying the most apt subject matter expert would require the filtration of useless bits of information that have no relation to the selection process.
The follower count the influencer has, isn’t the only parameter that you should be looking for. To give an example, almost always when brands are looking to spread their stories around ‘Tech’, be it Mainframe or even Bitcoins, almost always ‘Gadget reviewers’, are sent the Bat-signal.
Marketers need data science to investigate all the elements of content — from what type of image to persona of the influencer to figure out what best suits for that particular brand.
Scale, Scale, Scale!
Finding one right influencer for your brand, is just the beginning. The real challenge is to scale this. What is the key to scale, is to replicate the science or logic applied to find and recruit that one successful influencer, to find a dozen more who can create the same magic of massive reach.
To discover potential new influencers from the most ideal audience pool by analysing large volumes of interactions and content on a social media network — from the follower base to engagements, the only choice of the marketer is to resort to algorithms that can simplify identifying micro-influencers in a particular niche. If you are looking at Google to do that task for you, good luck with that.
(Praanesh Bhuvaneshwar is the co-founder, Qoruz)