TMW #034 | Misconceptions about data, the imminence of artificial intelligence, and experimentation practice

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Welcome to TMW #034! This week I dive into our misconceptions about the future ramifications of data collection, the imminent changes AI will introduce for marketers, and a number of perspectives on experimentation - why you don’t need AB testing, quasi-analytics, influencing product backlogs and how NYTs go about it.

👇 Here’s everything you’ve missed in marketing and tech this week.

🎤 New MSoM Episode: Peep Laja on technology saturation and strategic positioning.

Peep the founder and CEO of CXL, Wynter, and also the founder and board member of the newly rebranded Speero agency, previously the CXL agency in Austin Texas. We talk about the state of the Martech products, ways to compete with strategic messaging, finding unsold problems, and creative imagination in the tech industry.

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✍ Commentary

⚠ What are we getting wrong about data? I have something to confess - I have a superficial understanding of data. I don’t mean how data works like storage, enrichments, security, I mean the overarching value, risk and importance of how companies approach the future role data will play for consumers and society at large. Like most people in the digital space, I’ve understood data’s place in the economy as the new oil, or the new sand, both analogies to explain that data is vital to value creation for companies. And it’s true - without a strategy on how to harness customers data, you’ll be left behind. This is because data capability has become a core tenant for almost all customer experiences. But here’s the thing, as we’ve been focused on collecting and using data to advance the goals of our organisations, few of us are thinking about the existential risks this poses to our customers, companies and markets. Enter a novel concept that was originally presented in 2015, on the Idle Words blog, but has resurfaced recently as privacy and security conversations have increasingly become mainstream.

Here’s the idea - data not as oil, but nuclear energy. At the heart of the idea is that data is immensely powerful, but also has the potential to be very harmful. For example, the waste that nuclear fuel produces poses risks that last longer than any human society can collectively manage responsibly. I’m talking about thousands of years. Data works in the same way. What we collect and store on customers; who they are, their interests, purchasing habits, online behaviour, content they share can be stored far longer than the lifespan of any one person or company. Like what Kurt Vonnegut once said - “Another flaw in the human character is that everybody wants to build and nobody wants to do maintenance.” Most leaders have limited foresight on what the future of data collection entails (no-one can predict how regulations might change), or what might be socially acceptable a decade or a hundred years from now. This presents risks that many who devise data strategies tend to forget in the midst of creating value for the companies they serve. We’ve seen countless data hacks, like Cambridge Analytica and other scandals putting people and their data at risk, but how many of these do we need to have until consumers become completely spooked to even use their email address to buy something? The future of data is in our hands, and the decisions we make today will have consequences generations from now. Thinking about data in value categories exclusively is now not good enough, we need broader and more nuanced approaches to safeguard whichever future the data we collect will create. Here’s the original 2015 piece with advice on data collection policies, consumer transparency, and some very interesting slides. Link

🚥  Artificial Intelligence - Change is imminent for marketers. I first heard of the term “Machine Learning” during an early morning walk to work in 2015. Since then artificial intelligence has only started to make an impact on how people do marketing, manage customer experiences and run advertising. Certainly, some aspects of AI/ML have become a commodity, like product recommendation engines for the retail sector, thanks to leading companies like Amazon and Ebay (try to find an ecommerce business which doesn’t have product recommendations in their emails).We’re at a stage now where more sophisticated applications of AI are coming. One interesting (and delicious) example being Amazon’s partnership with Dominos which predicts what type of pizza a customer may order - to prepare the food ahead of time. We’re in an emergent phase with AI as we can recognise the immense value of utilising AI, from cost savings to maximising customer spend, yet most of the focus is on the immediate benefits. We need to start thinking about the broader first and second order effects on how we go about customer marketing. Sam Altman, the CEO of OpenAI has released a seminal paper on the outlook of AI over the next time horizon for the technology - 5 and 10 years. He says:

In the next five years, computer programs that can think will read legal documents and give medical advice. In the next decade, they will do assembly-line work and maybe even become companions. And in the decades after that, they will do almost everything, including making new scientific discoveries that will expand our concept of “everything.”

As AI becomes more advanced, this is likely to exponentially increase the dynamics created in Moore’s Law to introduce massive amounts of economic disruption, speeding up technological advancements and how they are applied to parts of the market in ways we can’t even begin to understand. While brands are incrementally adopting AI solutions, for most there’s a strategy gap in how to navigate the imminent disruption AI will introduce to the economy more broadly, impacting how brands connect with consumers. Instead, the majority of talk is about how AI can be bundled into point solutions packaged by software vendors, and sold to solve immediate needs. Artificial intelligence will become less about what your brand can do with it, and more about how your brand should respond to it. Link

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📰 Latest Developments

Antitrust. Google, Facebook and Twitter executives faced their third antitrust congress hearing in six months. This time on the role of social media in relation to the storming of the Capitol and the 2020 US election. Conservatives want to reduce regulation for content moderation, liberals want to enforce it. The section 230 debate continues. Analysis

WPP doubling down on data ops. A new $200 million investment into Group M will create a new entity focused on building data and analytics platforms to centralise client data for advertising and marketing. This is an important reaction to increasing limitations and regulations from advertising partners like Google and Facebook. Link

Episerver acquires Zaius. Episerver, even with the acquisition of Optimizely last year still had a missing piece in their offering - a customer data platform. In reaction to the phasing out of 3rd party data, the acquisition comes as more customers are looking to build first party data capabilities. Link

📚 Reading

Buy instead of build. When it comes to building technology capability, particularly around customer experiences, there’s an age old debate - should we build or should we buy? Here’s an argument to suggest that buying is far more effective (and more fun) for most use cases. Many think about the implementation cost, but few look carefully at maintenance. Link

Rethinking tech stacks. Planning technology solutions that fit into each other is challenging, particularly when technology advancements are constantly changing. But the companies that are building technology layers that scale focus on integration up front. Link

🔢 Data & Insights

Retail brands and security risk. With all this talk of digital transformation in the retail sector, there’s one massive elephant in the room - 83% of all US retail brands are exposed to online cybersecurity threats. Link

Ageing population shopping habits. What did the pandemic do to the older generations (55+) and their purchasing habits? IAB UK research suggests that 60% of this demographic increased online shopping and will continue to do so post pandemic. Link

💡 Ideas

How the NYTs AB test their headlines. An interesting practice for experimenting with alternative article headlines within the first 30 minutes of publishing to determine the permanent. It’s about 39% of all articles taken from a large sample. Some have more than 8 variations and evolve as stories develop. Link

Building dashboards the right way. Avinash Kaushik of the TMAI newsletter breaks down the purpose and approach to creating successful business dashboards. He suggests that 80% of the purpose of a dashboard is to recommend actions. A lofty goal indeed. Link

The 30 minute university of planning. Just months ago this didn’t exist. It’s a global community of marketing and advertising mentors and mentees. There’s about 250+ on each side and they facilitate 30 minute, no-strings-attached 1:1 mentoring sessions.  Link

✨ Weird and Wonderful

Swapping COVID for diabetes. Krispy Kreme is promising US residents a free doughnut everyday this year for getting vaccinated. I applaud the attempts to motivate more people to be vaccinated, but a doughnut every day of the year? Maybe not. Link

Harvesting electricity meter data. A power company has been creating a data platform to collect tens of millions of data points that are collected by smart meters. It will be used for analytics and influencing customer behaviour. Link

An open letter to marketers from a fairly normal bloke. A hilarious little parable talking about the experience of the everyday person as they relate to ads, fleshing out the absurdity of what some marketers try to ask their customers to do. Link

Stay Curious,

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Want to share something interesting or be featured in The Martech Weekly? Drop me a line at juan@themartechweekly.com.