Learn how to play the branding game, says Kubi Springer – empower your team with a strong strategy and the skills to implement it. Here are her key branding lessons to help you maximise your ROI and make your brand as exciting as a World Cup victory.
The mainstream understanding of Artificial Intelligence (AI) is still pretty haphazard. When people think of it, they often conjure up images of The Terminator and have a tendency to think negatively. In reality though, AI is helping us to optimise our everyday lives and now permeates almost every industry.
One industry in particular that has embraced AI is marketing. To begin with, the use of AI was typically limited to email, advertising or direct marketing, but over time its sphere of influence has broadened. AI is now used in a wide variety of common marketing practices from data analytics and reporting to decision-making and outcome prediction. As a result, AI has become one of the most disruptive forces in the industry . . . but the best is yet to come.
At the moment, the primary function of AI in marketing is rooted in data. Robots are able to trawl and analyse the personal details of millions of customers at super-fast speed and potentially without human bias. Initially, this use of personal data was received with scepticism, but the modern-day consumer has grown more receptive to personalised advertising, provided there is a value exchange.
With the rise in programmatic advertising – predicted to reach 84.5% of all ad spend in 2019 – brands are increasingly able to leverage data to open up a one-to-one dialogue with the consumer. This model of relevance at scale means that consumers receive more targeted content, while marketers generate better ROI on their ad spend.
Personalisation is nothing new, though. It’s been the industry buzzword for some time now and anyone with access to platforms like Facebook, Google and Twitter can use their enormous datasets to put together a pretty good targeted advertising campaign. In order to stand out from the competition, marketers are therefore having to shift towards an age of hyper-personalisation in which brands mine more than just basic personal information.
Facial recognition initially promised much, yet it has actually delivered little to consumer audiences. But, with the launch of the iPhone X and Samsung’s Galaxy S8, the technology has become more relevant. In government and business the use of the technology is more widespread and marketers are now exploring new ways to use it.
Amazon is about to launch AWS Deeplens which will put video recognition and machine learning in the hands of a much broader audience and should rapidly increase the number of apps incorporating this technology.
For example, as many as 59% of UK fashion retailers now support facial recognition software in their stores. This technology can be used to quickly and accurately identify customers and send real-time push notifications, such as discount offers and welcome messages to a shopper’s device.
Beacon technology is a primitive version of this marketing method and has already produced promising results. McDonald’s tested Apple’s iBeacons in locations across Columbus, Georgia in the US. The beacons sent promotional offers to customers with an enabled smartphone app and saw sales of McChicken Sandwiches and Chicken McNuggets increase by 8%.
But could marketing go a step further still? Companies like Emotient Inc, Affectiva Inc and Eyeris have developed technology that uses algorithms to analyse people’s faces and identify emotions. Elsewhere, Affect Tag has designed wearable sensors with the ability to measure biological signals and study emotional activity, intensity and cognitive load. Emotional surveillance technology has even been trialled in some Wall Street investment banks to monitor the physiology of the traders and suggest they take a break when they’re at their most emotionally sensitive. Add these techniques to facial recognition technology and marketers could be one step closer to a future of hyper-personalisation.
Striking up a conversation with consumers
At the moment, 88% of consumer conversations take place in private messaging apps, leaving brands in the dark. However, chatbots are now helping to bring these discussions out into the open. These bots follow complex decision trees that use reference data to match users to a solution. With the use of AI and machine learning, marketers are now able to transform these conversational interfaces from home assistants and customer care providers into advertising hubs.
Sephora is a great example. The makeup brand used messaging app Kik to instigate automated discussions with consumers, including a short quiz on makeup brand preferences. At the same time, image-based applications provided users with specific insights tailored to their facial shape. Combined with basic personal details, Sephora was then able to provide users with personalised product suggestions. This innovative use of chatbots is already making an impact, with its Facebook messenger bot, Sephora Reservation Assistant, delivering an 11% increase in booking rates and a rise in in-store sales.
As consumer-facing AI-powered systems become more reliable and powerful and their capacity to make useful product recommendations improves, consumers will increasingly outsource their purchasing decisions to robots. After all, why would a consumer spend hours scouring the internet for the perfect holiday when a piece of software that understands all your preferences can do it for you in seconds?
AI and modern marketing: automation vs creativity
Marketers are also finding increasingly creative ways of using AI to improve the efficiency of day-to-day tasks. For some time now, marketing agencies have been using technology to translate analytical data into detailed reports with actionable insights. But what if it could also be used in production and decision-making processes?
One interesting idea to emerge is the belief that prescriptive analytics could revolutionise the role of decision-making executives. Prescriptive analytics uses data from machine learning to make decisions, with little to no human interaction needed. Although most marketing organisations are still in the predictive stage of using this technology, we might not be far off a future where prescriptive technology is commonly used to automate campaign strategies, even potentially deciding far-reaching spending decisions beyond that of today’s programmatic platforms.
AI is also helping to reduce the cost of production for agencies, including content creation. In fact, Gartner predicted that 20% of all business content produced in 2018 will be authored by machines.. Natural-language writing holds huge potential in optimising the work of content creators to make it more impactful. These AI-generated narratives are designed to read as though written by humans, with rules and formats established by the brand to best serve its target audience.
However, while AI is likely to take on more and more of the everyday office tasks, the human touch is still as important as ever. Over the next few years, the key battleground for marketing will be in finding the right balance between human and robot interaction.
Take Shimon for example, a four-armed marimba-playing robot developed by Georgia Tech. It is the first robot to use deep learning to perform original compositions, which it produces based on large datasets from famous musicians. Despite being an incredible feat of technological engineering, the music it produces still leaves much to be desired.
Shimon is a shining example of why AI’s increasing influence on marketing is placing greater emphasis on the importance of creative roles. Automation is clearly helping to reduce the cost of business, and analytical software is helping to improve strategy and content. However, while technology may be accomplished at performing repetitive tasks, its ability to think creatively and create marketing with emotional resonance is still in the future. As such, when it comes to getting the best out of AI, marketers will need to augment their current practices with technology, rather than substitute them, but the technology can definitely help humans see concepts and ideas that ultimately create better output.
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