The Age of Automation: Where does creativity fit in?

In an increasingly data-driven world, we see the automation of jobs in almost every industry. This is a daunting prospect for some, but evidence shows that rather than taking over our jobs, new technology is forming new opportunities: Smart humans are working alongside smart machines in collaboration. But where does this leave creativity? Is it possible that machines will replace human imagination? How do data science, artificial intelligence, and machine learning fit into with the world of art? These are key questions to investigate as we adapt our company cultures to align with digitalisation and data-driven economies. To do so, let’s explore examples of how creative industries are utilising data and analytics to deliver business outcomes:

The music industry

The first pop song to be created using artificial intelligence (AI) was produced by Flow Machines software, a subsidiary of Sony CSL. Working towards the release of its first entirely AI album, the technology used by Sony carries out analysis of database lead sheets from a range of genres and forms melodies as a result. It doesn’t take long when listening to the the AI-produced track, “Daddy’s Car”, to identify hints of machine learning, and there is still plenty of room for improvement in terms of producing ‘human’ music. That being said, machines can learn extremely quickly, and development is continuous. The augmentative element in this case is clear: A human composer is needed to turn the melodies into songs.

The television industry

Can you imagine being able to predict the perfect television series? Well, Netflix did just that. Its recent series are important success stories for data and analytics. For example, “Orange is the New Black” and “House of Cards” were created by Netflix through the use of data and analytics to find the ideal combination of elements for both to be successful. In these cases, the right choice of actors and director and the best possible combination of genre elements provided the confidence for a $100 million investment on data and analytics from Netflix. This emphasises how machine learning can take the extra leap, understanding a deal of complexity that humans cannot: By using advanced data science techniques, Netflix could identify over 76,000 genre types to describe user tastes. This process that would have been extremely lengthy, if not impossible, when carried out by humans alone.

The marketing industry

A creative function by tradition, marketing is ultimately about storytelling. More and more, marketers are leveraging data to drive outcomes and understand customer decision-making effectively. With the digital footprint that customers leave behind online, businesses can leverage this data and add analytics to learn more deeply about the behaviours and future intentions of customers. Global bank HSBC used behavioural science to help customers reach their financial goals by developing a ‘nudge’ app using automated messaging to save customers £800,000 between Christmas and New Year’s alone. At the end of the day, data makes us better at decision-making and assists us in making sense of an increasingly complex world of customer interactions and transactions. It’s critical that marketers do this to remain competitive. However, it’s important to remember that human creativity is not to be disregarded: It sits alongside automation.

The transformation of customer data

Data gets more complex as technology advances, capturing not only customers’ transactions, but also their interactions and observations. As analysts start to delve into the data, they no longer extract only tens of variables from data, but potentially tens of thousands of variables in an attempt to understand customer behaviour at any given time. This data enables marketers to plan and form meaningful interactions.

It is essential that marketers follow the Google strategy of ‘be there, be useful, be quick’ to capture moments of true value. To demonstrate how organisations are adopting analytics in conjunction with automated, real-time decisioning we can look to advertising agencies who are building billboards that can achieve insight from visual analytics of video footage, to be able to deliver customised advertisements depending on the analytically identified make and model of car driving past.

Having the right content in real time is not enough: Location is also essential in understanding customer decision-making for future marketing teams. Studies reveal that geo-targeted mobile offers, based on customer environment and proximity to retailers, can provide two times more effective conversation rates. If delivered to a commuter on a particularly busy route, the conversion rates of marketing ads are even more effective.

Businesses require deep insight and need analysis of data to be delivered in real time to create moments of real value that improve a customer journey. To achieve this, organisations are coming to realise that they must adopt automation. If they fail to, marketers will simply not be able to make the massive number of decisions necessary in the marketing industry today.

Art versus science?

Creative industries, by adopting the right combination of art and science, can get to know their customers better and remain competitive: This is done through optimising creative skillsets belonging to humans, together with the use of data and analytics, to drive outcomes.

The face of marketing in a digitalised world is constantly transforming as machine technology advances. As demonstrated by the examples in this blog post, the creative industry is using analytics and machine learning to develop existing products and services, as well as create brand new ones. Humans will still drive narrative and innovation: Data science will support heavily, providing intelligent automation at scale.