“Data-Driven Thinking” is written by members of the media community and contains fresh ideas on the digital revolution in media.
Today’s column is written by Ali Manning, co-founder and COO of Chalice Custom Algorithms.
In the future, the best, most successful brands will be the ones that can predict the future.
And there’s no reason why these brands can’t be better at anticipating what consumers want than even the most well-capitalized tech giants.
That might sound far-fetched, but machine learning is poised to make this a reality. Machine learning will transform the practice of marketing while also resetting the relationship between marketers and tech giants along the way.
Where we’ve been
In the coming years, brands will compete on an entirely new playing field, and their ability to win market share will be less about who spends the most on media and more about who can build the most powerful proprietary predictive technologies.
Netflix already predicts what we’re in the mood to see informed by the feedback it gets from its marketing flywheel. There’s no (good) reason this can’t also work for any large brand.
Historically, marketers have competed on what they’re supposed to be good at. For the most part, that’s meant focusing on a product’s USPs (“This toothpaste gets your teeth 50% whiter!), on services (LL Bean, for example, has a very generous return policy), on price (“This product’s a bargain”) or on the stories they can tell about themselves (Nike helps inspire you to achieve; Pepsi makes you feel young).
These factors are still important, of course, but brands have also been racing to accumulate as much consumer data as possible in an effort to get their messages in front of the right targets and, hopefully, keep their customers in the fold.
Brands battle to be the best at targeting and closed-loop marketing. Yet advancements in machine learning promise to upend everything.
Where we’re going
Sarah Rose, SVP of international digital operations, data and platform ops at IPG’s Kinesso, recently wrote in an AdExchanger column that “machine learning is the first step in optimized data science applications.”
In other words, whether via ML, AI or computer vision, machines can do things faster and at a bigger scale than people can.
This is 100% true, and yet the concept can feel rather abstract. It’s not hard to read this language and think, “Hey, doesn’t programmatic ad buying already do this? Is this just about marginally better targeting?”
To truly understand the potential impact, we need to think outside of digital ad boxes and consider what ML has already done to transform industries such as finance, medicine and sports.
Take medicine as a shining example, where we’re already seeing custom cancer therapies based on genomics.
In a similar vein, brands can build their own custom, predictive technology that incorporates thousands of variables and fully drives decisioning.
You might ask, “What about creative?” And the answer is, creative is still going to matter – a lot. Maybe even more. There’s no reason why the combination of creative based on sophisticated predictive models and testing with new proprietary ML technology can’t be just as effective, if not more, than the biggest players in ad tech.
ML in action
Here are a few hypothetical examples of what this could mean in practice.
Imagine a challenger wireless brand has developed improved network coverage in certain regions. The brand has a chance to grow market share, but only if it’s able to inform specific customer segments in specific areas. ML can help bolster this brand’s performance when targeting consumers by factoring in a set of custom variables related to location, income and current device type.
To be clear, this is about more than simply running ads within certain geolocations. I’m talking about building an ad bidding strategy for 40,000+ ZIP codes while overlaying a customer’s income bracket for each.This is not the kind of work one can dump on a bunch of junior staffers who are good with spreadsheets.
Now imagine another wireless brand, this time the national leader. This company is less focused on driving share, because its best path to growth is upselling current customers into bigger service packages and expanded family plans. In this case, the brand can use a different set of custom variables to target existing customers, such as each person’s existing plan, how long they’ve been a subscriber and how many devices they have in their home. All of this information can be plugged into machine learning software to drive far more relevant and profitable results.
The DL on ML
This future isn’t all that far away.
Marketers have been soaking up so much data to the point they feel like they don’t know what to do with it. And that’s because they don’t – yet. But as ML technology takes hold, its predictive power will grow exponentially based on variables that can be put into a model.
It also promises to unearth dozens of needle-moving variables that humans might never see.
ML tools keep getting smarter and more potent the more you use them. This sets the scene to allow brands to compete on whose tools can learn the fastest rather than shelf space or share of voice.
And think about this: Once brands own their own ML, they’ll know more about their own customers than a one-size-fits-all walled garden or a brand’s own in-house tech.
Plus, thanks to advancements in data storage and players like Snowflake, what once took weeks and cost millions can now be done in a few hours at a reasonable cost.
This has the potential to dramatically change the dynamic between marketers and the duopoly.
It’s not that brands won’t continue to advertise on these platforms – they may even advertise more. Rather, marketers won’t feel that their own customer data and campaign data is walled off. They’ll have their own rich understanding of their customers and what moves them, which will give them more leverage.
ML doesn’t just promise to change your business – it promises to redefine what business you’re in. That’s a future I think most CMOs would sign up for.
Follow Chalice Custom Algorithms (@ChaliceCustom) and AdExchanger (@AdExchanger) on Twitter.
