Jim Sterne is the author of Artificial Intelligence for Marketing: Practical Applications, a straightforward, non-technical guide that presents a tightly-focused introduction to machine learning, written specifically for marketing professionals. His thirty-five years in sales and marketing is focused on creating and strengthening customer relationships through digital communications.
Enjoy this enlightening interview with Jim!
#1 What does Artificial Intelligence (AI) and Machine Learning (ML) actually mean in terms of marketing?
AI and ML mean more sophisticated processes can be automated to free up marketers to be more creative, more patterns can be revealed indicating new markets or marketplace opportunities, and more time is needed implementing new technologies. Processes like email sorting, lead scoring and personalization can be managed by an algorithm that can improve over time. Finding look-a-likes out in the world becomes more accurate and, as above, improves over time. Communication channels like chatbots and voice response are going to become standard. All of this will be directly applicable to social media where bots are already learning how to talk to people, and social monitoring is becoming more refined.
#2 How do businesses react to these leading-edge technologies in their marketing so far?
Corporate culture is driving adoption – or the lack of it – of AI and ML. Some companies are excited to jump in, try new things and learn through experimentation. Others are reticent to explore or risk temporary failure; they would rather others take the arrows in the back first. This is a truism regardless of the technology under consideration, but AI and ML are a bit harder to understand and therefore, leaving the introverted further behind.
#3 x.ai is a great example of AI implementation. What should we expect from AI in the near future?
In the near future, we should be surprised to find that AI can be applied to any data-rich problem. In the longer term, we will be completely unimpressed by AI efforts that do not knock our socks off. Everybody was thrilled when they used a cash machine at the bank for the first time or could buy things online for the first time. Now, we wonder why all sites are not as good as Amazon. The excitement of the “online-ness” of the experience has worn off and now we demand that it work better. We will soon tire of the chatbot that says, “I’m sorry, I don’t understand your question.”
#4 What should businesses do in order to be prepared to apply AI and ML technologies to gain a competitive advantage in their marketing strategy? Do they need to gather a team of people like data scientists, digital analysts or other specialists?
Big businesses should hire whatever data scientists they can get their hands on. Mid-sized and small businesses need not worry. They will eventually subscribe to an upcoming infrastructure of algorithms, datasets, and functions that can be melded together into applications or bolted onto existing applications. Need a natural language understanding capability? Choose one of these. Need a chatbot to read your website and answer questions? Here are four to compare. Need a better way to test email messages? Here are 20 competitors. Soon, we won’t even be using the terms artificial intelligence or machine learning. They will simply be baked into every interactive process.
There will *always* be a need for analysts; people who can look at the output and focus their knowledge and skills on whether the answer makes sense. Domain knowledge and depth will always be in demand.
#5 How difficult is for businesses to succeed in their Digital and Web Analytics efforts and how important is for them to be able to decrypt customer behaviours? Can you share some advice to newcomers?
Digital and Web Analytics efforts have always been tough because of the need to understand the data, the technology that captures it, the technology that analyzes it and the technology that operates based on insights from it. Decrypting customer behavior has always been the name of the game, along with an ability to explain the gleaned insights and their implications. There will be little change here. AI and ML will be yet another tool in the hands of the analyst, but will never take their place.
Newcomers to Digital and Web Analytics are advised to spend more time than they expect to understand the business. Understanding the data and the above technologies is table stakes. You become valuable when you fully grasp the organization’s goals and the motivations of the key stakeholders. That often means knowing which decisions will either earn people their bonus or get them fired. Domain knowledge is the name of the game from now on.
#6 Concerning social media, what metrics should be evaluated when it comes to social media performance?
Oh, I could write a book! Of course, that was ten years ago. What you should measure depends on how you define success. If you are more interested in awareness, you should measure reach and impressions. If you are more worried about brand affinity, you should measure sentiment.
Jim Sterne #Metric Tip: If you are more interested in awareness, you should measure reach and impressions. Click To Tweet Jim Sterne #Metric Tip: If you are more worried about brand affinity, you should measure sentiment. Click To TweetA few words about Jim Sterne
Short Bio
Jim Sterne focuses his thirty-five years in sales and marketing on creating and strengthening customer relationships through digital communications. He sold business computers to companies that had never owned one in the 1980s, consulted and keynoted about online marketing in the 1990’s, and founded a conference and a professional association around digital analytics in the 2000’s. Following his humorous Devil’s Data Dictionary, Sterne has just published his twelfth book, Artificial Intelligence for Marketing: Practical Applications.” Since 2002, Sterne has remained active producing the eMetrics Summit and since 2004 as cofounder and Board Chair of the Digital Analytics Association.
About the book
Artificial Intelligence for Marketing: Practical Applications is a straightforward, non-technical guide that presents a tightly-focused introduction to machine learning, written specifically for marketing professionals. This book will not teach you to be a data scientist—but it does explain how Artificial Intelligence and Machine Learning will revolutionize your company’s marketing strategy, and teach you how to use it most effectively. Data and analytics have become table stakes in modern marketing, but the field is ever-evolving with data scientists continually developing new algorithms—where does that leave you? How can marketers use the latest data science developments to their advantage? This book walks you through the “need-to-know” aspects of Artificial Intelligence, including natural language processing, speech recognition, and the power of Machine Learning to show you how to make the most of this technology in a practical, tactical way.
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