Since starting E-Nor fifteen years ago, we’ve observed the increasing complexity of digital marketing. Some of the technologies have gone by the wayside, but one of them has not, and that’s AI. Artificial Intelligence is everywhere. From Siri, Alexa, and Google Assistant to predictive text and chatbots, we’re becoming used to the benefits of interacting with AI-powered platforms. Google now knows my daily pattern of traveling from home to the office (and from the office to my aikido training :)), and alerts me when there’s a traffic delay so I can take another route.
We are now working with clients who use AI to do things like deliver personalized content and emails, distribute cross-platform upsells, and optimize paid search and programmatic ad spending. As always at Cardinal Path, we like to stay ahead of the curve. After reading “Artificial Intelligence for Marketing: Practical Applications”, by our friend Jim Sterne, I was inspired to help our team put together some of the latest information and resources on AI for marketing.
A Mountain of Data is a Competitive Advantage
Like many marketers, you may be getting questions or requests from the C-suite about using more AI to power marketing. According to a Forrester study, 78% of marketers are planning to adopt or expand AI platform implementations within the next year. And for good reason: all the digital marketing technologies that have been implemented in the last 10-15 years have created a wealth of data that requires a sophisticated intelligence for proper analysis and performance optimization.
Just sitting on a mountain of data does you no good – but if you are, you’re not alone. The study found that 63% of marketers are concerned that they have too much data to effectively analyze and gain actionable insights. With companies like Google and Facebook using AI to increase ad revenue, it’s up to marketers to make sure that interactions delivered from their marketing channels are optimized for conversion. AI is a great way to do that.
Classes of AI
AI can help your company with a lot of things – including meeting and exceeding your marketing KPIs – but first it’s important to understand the terminology. Since the 1950’s, when computers were programmed to play checkers and recognize geometric shapes, AI has become an umbrella term that covers a number of areas. You may be familiar with these:
- Natural language processing – Used in chatbots, predictive text, and social analysis tools
- Computer vision – Often used to identify and tag images
- Machine learning – Analyzes data from different sources, takes action on that information, and learns from the results of those actions over time
- Deep learning – Machine learning that mimics the structure of neurons in a biological brain
How AI Can Help Marketers
Companies currently using AI report its effectiveness: 79% say that AI is bringing new insights and better data analysis to their organizations. For marketers dealing with large historical data sets and real-time visitor data, machine learning has many applications that include:
- Customer Experience/Personalization – AI-powered systems can Interpret intent signals to guide users through a site or app. Use browsing signals, order history and other signals such as weather and seasonality to deliver product recommendations, offers, and emails.
- Customer service – Chatbots can handle many basic requests such as taking orders, offering recommendations, and qualifying leads. Automated phone support can also be vastly improved with smarter AI.
- Customer churn – Machine learning can be used to find high-risk customers based on lack of engagement, and deliver relevant offers through email marketing to re-engage.
- Social analysis – Computer vision and natural language processing can analyze social image and text activity to uncover brand insights and provide recommendations for engagement.
- Audience targeting – Look-alike targeting identifies optimal audiences by using machine learning to analyze data across multiple platforms and create new segments.
- Predictive analytics – AI-powered analytics can be used in a myriad of ways, such as helping to determine which leads are most likely to convert, which products to cross-sell, which images to show, and which emails to send.
The opportunities for AI in marketing are nearly endless, and it’s a career growth opportunity. 64% of organizations implementing AI say that lack of appropriate skills and learning is a key challenge. If you’re worried about your job being taken away by AI, don’t. Four out of five organizations say AI has created a new roles.
Tech Giants Want You To Learn AI
The world of AI is currently a bit like the wild west, but the largest tech companies have begun to lay a foundation for development. Implementing AI at your company will require a good deal of learning, research, and creative thinking, but the good news is that you can build off what others have already done. Most of the tech giants have AI research arms, and some offer developer tools and education. Use the list below to inform your research.
Ethics and Data Governance
It’s hard to talk about AI without considering worst-case scenarios. From “Terminator” to “BladeRunner” to “Westworld,” there’s a deep cultural fear about machines we can’t control. AI for marketing is unlikely to create sentient robots, but it’s still important to consider the ethics of your initiatives, as well as ensure your AI initiatives comply with privacy regulations. Google published its AI principles; you may want to consider putting your company’s AI values in writing to guide your decision-making.
The old saying “garbage in, garbage out” is just as true for AI as it is for big data analytics. Just as humans can learn from misinformation, machines can learn from bad data and create inaccurate or even inflammatory suggestions. Remember Tay, the Microsoft bot that learned offensive language from Twitter?
The Time for AI is Now
As you explore the options for AI in marketing, remember to learn from the best, keep ethics in mind, and make sure your data ducks are in a row. Don’t underestimate the importance of data governance: a Capgemini study found that companies with a central data governance team had better results from their AI initiatives than those who did not.
