The end of the 2010s didn’t really feel like the end of a real decade. The 2010s? The 10s? What is that? For the previous 10 years, we had scrambled to learn what the hell blockchain is, feared that AI would steal all of our jobs, and daydreamed about augmented reality, driverless Ubers, and using 3D printers to prepare meals in our Google-managed smarthomes.
But we’ve seen it before: the hype, a flurry of activity, and—after the dust settles—a few winners emerging with technology that only somewhat resembles our wild expectations. With the benefit of a few months’ hindsight, we’re predicting that some prominent tech bubbles will never actually expand in the first place—let alone burst. If and when they do, only a few giants will remain. This small asymmetric advantage will quickly accelerate with customer interaction counts, as suggested by complex system modeling, expanding the winners’ advantages even further. This widening gap manifests as what we’re dubbing “data inequality”.
Understanding data inequality—and how to survive it
Data earns data, plain and simple. And data is valuable: products are improved, opportunities are exploited, consumers are further habituated into these products—all of which generates even more valuable data. Data assets are everything these days, driving superior logistics, segmentation, product development, prediction, investment, and more. But without a “data tax” as a means to redistribute data wealth, how on earth do you compete with the biggest servers? Fortunately, all is not lost.
With their growth-at-all-costs mindset, these conglomerates will push their advantage to the point of breaking. So, while most of us have been seduced by new and expansive technology at one time or another, others revolt and fracture the consumer market to present an opportunity (or, at the very least, hope) to the rest of us. In light of this, we introduce three simple strategies to defend against the rise of data inequality in two of the hottest martech areas: personalization and privacy.
The first-party data gap and personalization
Netflix has 15% of web traffic. Let that sink in. When you have that kind of volume, you really get to know what people want to watch. See also: Google’s 86% share of mobile search; Amazon’s 37% slice of the retail pie; MasterCard knowing what you buy; Facebook knowing what you like; and Uber knowing how cities move (and what you like to eat on lazy days).
These juggernauts have the first-party data, user base, financing, and skills to conduct experimentation at scale, which extends actionable first-party data sets. And just because they aren’t direct competitors doesn’t mean that you’re insulated from each giant’s progress. Their experiments train users to expect certain standards. In fact, 80% of Netflix’s shows are chosen via “Netflix recommends”; they’ve achieved masterful metrics when it comes to anticipating what their users want (or at least guiding subscribers towards their suggestions).
Still, no matter how soothing Alexa’s voice can be, sometimes the surveillance, manipulation, and predictive skills of technology can feel intrusive to many. My informal survey of friends suggests that for every seven people who love personalization, one thinks it’s creepy.
Given the rising expectation of personalization in some segments—and the resistance in others—which type of strategy should you pursue knowing you’re likely competing against the 1% of big consumer data? These are your three main options in 2020.
Strategy 1: Learn 🎓
Learn from the big guys and see what you can apply. Personalization is expensive though, so proceed intelligently. Begin by firmly quantifying the value of customer segments and conducting a cost-benefit analysis to define how far you should go. A series of A/B tests may reveal a personalization sweet spot that satisfies your customers (and your KPIs) with much less effort than you initially feared.
Strategy 2: Go your own way 🤠
If you can’t apply automation to deliver great personalized experiences, then move in an opposing direction (or at least take your foot off the gas). This requires smart extraction of value from your available data sets. The human touch still works, so find new and creative ways of offering attentive service to your customer base. Replace AI-based personalization with intelligently deployed customer representatives. Analyze your data and experiment with varying tactics to understand which segments need what degree of human attention, and why.
Strategy 3: Join ’em 🤝
Invest in amassing libraries of creative assets, and use AI tools and technology to discover what combinations drive the best results. It’s a black box, but double down on relationships with Google and Amazon, and apply the latest Adobe Sensei technology to influence customer journeys. If you need help, hire the right agency that has extensive experience at the controls.
The first-party data gap and privacy
The companies with the most popular products have the most money, the biggest computers, the best tricks, and an enormous first-party data advantage. Before you react defensively and attempt to stitch together your own online/offline customer super-profile database, realize that there are not only costs but risks associated with this endeavor.
Those with the largest deposits of customer data also hold the largest liability surrounding consumer privacy. Given regulatory responses like the CCPA and GDPR, doubling down on granular customer data collection might not be the most durable strategy. But you have alternatives available to you in 2020.
Strategy 1: Learn 🎓
The canary in the customer data mine is not a small business, but a technology giant. Regulators aren’t out to entrap local retailers. Collect customer data responsibly and sensibly, and you won’t get sued. Corporate espionage has been around for centuries, and there’s a reason for it. See how the 1% anticipates user preferences, displays product relationships, personalizes experiences, generates offers, and more—and then see what you can use. There is no need to invest in R&D to engineer proprietary solutions that are becoming ubiquitous.
Strategy 2: Go your own way 🤠
Heartless corporations are the villains in many children’s movies. No matter how cool their commercials are, NASDAQ 100 business rarely give us the warm and fuzzies. If you can’t play in their league, rebrand your smaller CRM data as one that respects privacy. But don’t simply go through the motions with a privacy equivalent of greenwashing: commit to a meaningful, considerate way to ensure customer privacy is a company priority. This can include actively sharing user profiles with your customers via email, clearly offering the right to be forgotten, or outright taking less data.
Strategy 3: Join ’em 🤝
If you have aspirations of amassing a first-party data fortune, then you’ll need to invest in the top technology. Commit to a vendor like Google or Adobe and go all in. Build a top-of-the-line technology stack with Google 360, Adobe Experience Cloud, attribution tools like Visual IQ, Salesforce, and more. Their business models rely on the collection, storage, and manipulation of user data, so let them worry about the legal implications of it all—they surely have the all-star legal teams to handle it.
Our top tip? Start small and iterate
In 2020, it’s far from certain that winner-take-all economics will apply to the world of consumer data. If we humbly accept this reality, we can adapt our data strategies to compete in a market dominated by a few top players.
It begins with an honest assessment of your customer data, a willingness to invest in technology, a quantified upside/downside, and an accurate understanding of your competition. As it happens, these are also the attributes that set Proove Intelligence apart.