By Emma Rush, CEO, Chemistry (part of Publicis UK)
Content streaming service Netflix is frequently cited as the ultimate modern media company, deploying the ultimate in modern marketing techniques. Its algorithms are used to gauge the success of shows, target relevant creative, package programming for specific people, and even to acquire new programming. The marketing industry could learn a lot from this.
There is no question that the algorithmic route offers brands a pathway to a profitable future. More than this, in the emerging climate of ad-blocking and extreme data-privacy, it will provide many brands with a lifeline for survival. Three quarters of people are frustrated with websites when ad content has nothing to do with their own interests and, as more of our online activity moves to mobile, advertising eating into users’ mobile data plans is likely to become an even more emotive issue. But, in our creative world, are there limits to this approach?
Better personalisation using machine learning
To make the future work for us, we need to stop thinking of simple cause and effect scenarios, and far more in terms of machine learning. And, for the programs to learn, they have to receive far more inputs than those from search and other basic online interactions.
Much of today’s thinking hasn’t significantly evolved from the work that focused on building propensity models 15 years ago. For example, in the first years of this century, Capital One built its credit card business by changing the way it profiled customers and their credit scores compared to other providers.
Moving on from this, we need to learn from Netflix and do a much better job of making communications feel both more personal and more personally relevant.
An understanding of machine learning will help us to achieve this. For example, Retention Science analyses behavioral, transactional and demographic data, and then applies machine learning and predictive algorithms to profile customers and predict behaviors, such as likelihood to purchase and churn. Its case studies suggest that technology has significantly improved the customer retention rates for The Dollar Shave Club, eSalon and The Honest Company.
Build new models of working
Brands, agencies, and their partners need to find a new way of working. Like Netflix, we need to operate in inter-dependent, multi-discipline, teams where the leadership shifts seamlessly to the relevant specialism at any one time. One person will never hold all the cards, have all the knowledge.
Collaboration and the ability to lead and be led will be key attributes. Dana Ardi argues in her book, The Fall of the Alphas, that the traditional ‘Alpha’ model of the top-down, authoritarian, organizational, landscape will be replaced by collaboration, connectivity, and power sharing. Beta organisations and teams will become the new paradigm. Team players, sage advisors, network experts, and communications facilitators will come to the fore. Savvy managers will need to learn to lead through influence and collaboration rather than authority and competition.
Combine AI with HI
The Netflix model points the way towards marketing innovation being driven through artificial intelligence. Marketing and agency teams spend acres of time each month reviewing analytics, creating performance reports, writing and scheduling social media updates, determining blog post topics, copywriting, curating content, building strategy, and allocating resources. Machines could perform much of this heavy lifting, leaving teams of people to enhance algorithm-based recommendations and content. This approach will require everybody involved, including the creative department, to have a hands-on knowledge of data.
AI and Human Inteligence (HI) need to work hand-in-hand. As we saw with the recent triumph of Google’s AI over the world’s best Go player, pure algorithmic thinking is about winning a game, or finding a problem to a solution. But profitable, long-term relationships are not built on winning and losing, they’re built on partnerships and collaboration in which both parties win. Nor are they solely built on rational benefits and offers, but also on emotional ones. We buy from the people and brands we like, the ones we think care about us, the ones we feel understand us.
Invest in real-time creativity
That’s why our favourite shows work. Netflix data didn’t create Breaking Bad and there was no data that could have predicted such a storyline would work. That was down to the show’s creator Vince Gilligan and his desire to rewrite the traditional ‘rules’ of a TV series. What Netflix is very good at, and what brands can learn from, is how to set up systems overlaid with human creativity. Systems that identify, in real-time, the various ways people engage with programmes, allowing brands to make faster and more confident decisions about what to show, to whom, and when.
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