AI & Machine Learning
In the marketing technology world, Artificial intelligence (AI) is growing by leaps and bounds. Propelling this trend are ingredients like Machine learning and other areas bundled under the AI banner, like deep learning, natural language processing and natural language generation. While enterprises have used some of these technologies since some time, their importance in marketing applications is growing – especially so with machine learning. As established firms like Google, Facebook, Amazon, IBM, Salesforce and startups like Adgorithms, Boomtrain, Cognitiv, Kenshoo, Lattice Engines, Rocket Fuel and numerous others continue to showcase the capabilities of their AI-powered applications, hard to impress marketers are looking towards a future when AI will free up resources, lead to closer, more meaningful connections, and help develop a more well-rounded understanding of customers to deliver what they want, when they want it.
George Zarkadakis, digital lead at global professional services firm Willis Towers Watson and author of In Our Own Image: The History And Future Of Artificial Intelligence agrees. “The attitude of C-[suite] executives should be to add AI as a top strategic priority,” he recently said in an interview. “This time, technology will move faster than ever, and the laggards will pay a hefty price19.”
Nearly every branch of marketing — be it direct, email, mobile, social, search, SEO, a combination of these or something else entirely — requires a series of actions, some of which can become mundane. For example, email marketing requires identification of a product or service to be sold, an audience of potential buyers, development of a list of those potential buyers, creative work for the body of the email, some amount of HTML and CSS coding, scheduling, and post-send analysis. Some companies are already employing AI-type principles to simplify these tasks.
On the cutting edge is Amazon, which employs a series of algorithms to determine what customers are likely to buy or “need” next and email suggestions. Emails are pre-formatted and creative is pulled from a massive database. Little or no human touch is required. This type of automation frees marketers to spend time thinking critically about customer needs and wants15.
When it comes to targeting of programmatic ads, machine learning helps to increase the likelihood a user will click. This might be optimising what product mix to display when retargeting, or what ad copy to use for what demographics17.
As Andrew Ng, Chief Scientist at Baidu Research, told Wired, “Deep learning [is] able to handle more signal for better detection of trends in user behavior. Serving ads is basically running a recommendation engine, which deep learning does well.17”
And Google is now experimenting with a new AI technology called recurrent neural networks (RNN) that is capable of “remembering” bits of information for short periods of time, potentially eliminating the need for specialized ad targeting code in the near future18.
Advertisers will increasingly use mobile to connect with their customers using chatbots, other artificial intelligence-enabled platforms like Apple’s Siri or Amazon’s Alexa and messaging apps, Forrester’s new report “Predictions 2017: Mobile is the Face of Digital,” said16.
Using machine learning and browsing and location-based data, mobile apps will allow brands to determine what customer will like and when — potentially even before they want it.
Content Curation & Recommendation
We’re all familiar with the now-commonplace “If you liked this, you might also like…” content recommendations at the end of many articles on the web. Often these recommendations are based on deep learning and other AI, where machines analyze large chunks of data about people’s behavior on the web to determine what they’re most likely to want to do next. When combined with the power of Big Data, AI has helped startups like Netflix & Spotify build billion dollar companies on the strength of intelligent curation and recommendation in the domains of videos and music respectively.
Content isn’t the only place where marketers are able to leverage deep learning to make recommendations. A visit to any major eCommerce website, including Amazon, demonstrates how AI is used to recommend products based on the products you click and buy18.
Commonly used in the travel industry, dynamic pricing – which sets product prices according to demand and availability – is a growing use of AI in marketing. It’s not just for airfares; if you’ve ever left an item in a shopping cart on Amazon only to find the price changed significantly overnight, you may have experienced dynamic pricing at work18.
Beyond these examples, AI interventions in marketing includes many more areas like Customer Segmentation, Customer Engagement, Image Recognition & Searching, Predictive Customer Service, Smart Search, Social Semantics, Sentiment Analysis etc. But all is not rosy. While a survey conducted by Demandbase, in conjunction with Wakefield Research, revealed that 80% of marketing leaders believed AI will revolutionize marketing by the year 2020, much still remains to be done. Despite so many marketers predicting AI’s increased role in marketing, a mere 26% say they have a very confident understanding of it and even more disconcerting is the fact that only 10% are currently using it. As Aman Naimat, SVP of Technology at Demandbase was quoted as saying, “This data reveals that in order to be successful, marketing leaders need to lead the charge and present opportunities for AI instruction and experience for their teams, to ensure implementing it into their B2B technology stacks is effective”.