AiThority Interview with Ian Randolph, Head of Product and R&D at Tailify

Hi Ian, please tell us a little bit about your current role at Tailify and how you started here.

I’m privileged to serve as Head of Product and R&D – a dream role for me, encompassing leadership of the diverse functions required to advance the science of influence: engineering, data science, research psychology, design and product. I joined Tailify in 2019 after a decade of building technology at the intersection of data science and behavioural science, including most notably a tour at SCL, the parent company of Cambridge Analytica, where I was involved in predicting population-scale behaviour through social media monitoring and analytics. The arc of my career bends towards deploying the power of what amounts to mass mind-reading technology for the good of all mankind.

What is the most exciting aspect of building an influencer marketing platform? Could you please tell us how Tailify is different from other MarTech solutions for influencer marketing?

Most platforms are data-rich but insight-poor. Typical metrics like following and engagement tell you what people did, but not why they did it. As a result, you’re still largely left guessing when it comes to deciding which influencers will perform for your brand and which will flop, and that leads to the inconsistent results we typically see across influencer campaigns today. 

Tailify’s platform actually tells you who has the potential to perform for your brand using AI trained on millions of data points – including, most uniquely, an influencer’s psychological profile. For instance, we’ve proven over thousands of collaborations that influencers who share a brand’s core values tend to perform much better than those who don’t. Our platform rigorously measures the values of a brand and then, from our 1m+ strong database, surfaces influencers who believe in what the brand believes in. And values alignment is the tip of the iceberg when it comes to the performance predictors our platform considers.

In short, we believe we’ve cracked the code of influence, and our platform has taught that code to AI, which you can then use to select influencers. And the AI acts like your personal behavioural psychologist, but one who has virtually infinite time to process all the content an influencer has ever created.

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What kind of challenges do you face in your R&D workflows? How do you solve these?

As with many data science and data engineering teams, model productionisation – the process of turning a prototype into something that works in a live environment – is a bottleneck, and great machine learning engineers are rare. Luckily, we have a tight team that works together towards the single goal of shipping value. In the highly interdisciplinary world of R&D, great teams beat great individuals, and we’ve invested a lot in keeping our team together through pandemics, wars and all manner of hurdles the world has thrown at us these last two years.

Could you shed some light on how data science and IT trends are changing the scenario for Influencer Marketing platforms?

Influencer marketing platforms are at the point on the analytics maturity curve where data science features are becoming differentiators. What this industry lacks is predictability of outcomes, yet many are still focused on process, resulting in more teams simply losing money on bad influencers and doing so faster. Increasing availability of off-the-shelf machine learning solutions will make the quality of these AI features a key competitive advantage.

How do AI and data science techniques improve influencer marketing outcomes? How do you measure the success / effectiveness of your Influencer Marketing solutions?

AI is improving outcomes at every stage of the influencer marketing process, including:

• Performance prediction at the influencer selection stage
• Communication optimisation at the outreach stage
• Price prediction at the negotiation stage
• Content optimisation at the briefing and feedback stage

AI’s impact even extends to measuring success itself, which beyond essential techniques like engagement and conversion tracking also includes earned media value calculations, untracked majority estimation and share of influence uplift – all of which get at the important stuff that can’t be directly measured.

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Content is king in any marketing campaign. How is influencer marketing content driving brand engagements with high-value customers? What is Tailify’s role in driving this engagement for users?

If content is king, context is queen in the influence economy. There is a world of difference between a piece of content seen on the side of a bus and that same content seen on my favourite influencer’s feed. That context decides whether I will filter out the content as yet another intrusion or accept it as true on trust. If brands are not reaching customers through influencers, through trusted individuals with a following, they aren’t likely to make inroads into customers’ minds.

This ‘trust wall effect’ is even greater for high-value customers who often rely more upon their networks as cognitive proxies for decision-making due to the high number of decisions they make and their relative time poverty. 

Tailify’s role is to connect brands with the influencers who have the trust brands need to move their target audience.

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Your take on the future of data science and analytics in influencer marketing: where do you see the whole martech industry heading in 2022-2023?

A looming recession will accelerate the maturation of influencer marketing as a channel that delivers measurable, consistent ROI for brands. Tech that can demonstrate an impact on bottom-line results will thrive and shift energies towards making each campaign deliver, instead of just delivering more campaigns.

At the same time, marketers are starting to catch on that influencers are not ad space and cannot be won with the old playbooks of paid media. Marketers need people analytics, not just performance analytics, to discover who is an authentic match for their brand and how best to collaborate with them to create win-win results. Platforms that can offer reliable human insights to support these relational decisions and interactions will be in great demand, as consumers shift all their remaining attention from paid-for media to trusted individuals.

These two trends towards measurable results and people insights, if fully realised, will open up the influence economy – a world in which people, rather than platforms, are the trusted conduit a product or message must travel through to reach consumers’ minds. In this world, we’ll also see a shift in spend in brands from primarily paid to primarily earned voices – i.e. influencers – leading to at least a 10x growth in the influence industry and a shift in revenue towards tens of millions of creators, birthing the largest industry in the world by employment. It’s a future we’re very much looking forward to at Tailify and one we believe we’ll see great strides toward in the next year.

Thank you, Ian! That was fun and we hope to see you back on AiThority.com soon.

[To share your insights with us, please write to sghosh@martechseries.com]