Dipalli is the director of marketing at Incognito Software Systems Inc., with 17 years of experience bringing profitable products to market.
As we sat for our board meeting, there was a hush around what new information I would tell the board members about marketing that they had not heard before. After all, it was all about the forecasts and our logic behind the forecasted numbers. As the only woman on the management team of the company I previously worked with, I started off with the growth we had seen and what we forecasted as marketing’s contribution to the upcoming year. As I uttered a highly aspirational conversion rate from the opportunity to close, a board member questioned, “based on what?”
As someone who is heavily data-driven with a great affinity for storytelling, I moved on to show how prospects were behaving in real time, which predicted their next best action. I did not fully win the argument, but it was sufficient to warrant a waiting game for the performance on the forecasted numbers.
The next term, the results delivered on the forecasted numbers, courtesy of artificial intelligence (AI) and marketing experience.
As marketers, we have to constantly be on top of two things: consumer behavior and market and industry trends. Deep learning, a subset of AI, can make the job of predicting consumer behavior easier. According to Bernard Marr, a bestselling author and futurist, deep learning is a subset of AI that facilitates certain functions in machines that typically require human intelligence.
One can see the influence of AI in most digital marketing strategies. For instance, Body Labs creates 3D body models so that companies can leverage the AI data to do sizing studies or for fashion designers to dress the models with their new clothing designs. In 2018, Amazon patented a “blended reality systems and methods” technology to help turn the digital experience into an immersive shopping experience. In the business-to-business (B2B) world, customer behavior analysis can play a huge role in predicting customer behaviors and shaping their experiences. There are platforms that allow advertisers to retarget prospects based on their browsing history, patterns and the articles they read, while dynamically changing the retargeted content.
If deep learning algorithms get better with more experience, then what role do marketers play in the new scheme of things?
Like any exponential technology, AI is not magic. It needs interpretation, understanding and context. Robust marketing strategies are a function of understanding human psychology, industry and market trends, social needs and wants, and individual experience. According to an article published by the MIT Technology Review, machines do not understand causation; they understand the association. A simplistic example mentioned in the article is that an AI system can tell that clouds make rain more likely to occur, but cannot say that clouds cause rain.
In my opinion, there are still questions that great marketing practitioners and executives ask after using AI as an input mechanism. For example:
• Why did a prospect not buy this enterprise software?
• What policy changes affected the sales of X in Y country?
• What if we introduce this scheme?
• Is it better to position this article in this way because, according to X’s feedback, certain prospects may be affected negatively?
• Is this data source to be trusted? What important information am I missing here?
As marketers, we need to be aware of AI’s limitations and use it as an ally, not a substitute. We need to tell stories that can truly connect on a deep and meaningful level and in a way that resonates with our audience. AI can help you with what data to focus on, but developing insights based on unique experience is where you will truly shine.
Original post: https://www.forbes.com/sites/forbescommunicationscouncil/2020/11/03/ai-and-experience-based-decisions-where-marketers-can-genuinely-shine/