I love marketing. I find it to be a fascinating intersection of creativity and business savvy, and it has been an area where creative people have worked and thrived.
However, as Bob Dylan sang, ‘the times, they are a-changing.' Digital channels now require and provide vast volumes of information. To optimize the efficiency of the marketing efforts, marketing organizations are required to collect a lot of data about the customer behavior and how they interact with the marketing content. Marketers also need more content to be produced and disseminated to an increasing number channels.
It is difficult to create, manage, analyze, and optimize huge amounts of content manually. It can’t be done using creativity alone. Even the best analytics tools and content management systems cannot help an organization to make the right decisions at the speed of digital—at least not manually. Not only does Web and eCommerce personalization require super-fast number crunching to present the right message in real time, but also significant amounts of granular product information are needed to put the product story together.
Not even with an army of super creative marketers would it be possible to do this manually. Customers require relevant results in real time. It also becomes increasingly difficult to manually produce all the content that is needed to put the right message together for your individual customers, at all touchpoints, for all stages in the buying journey. When you add the requisite analysis and optimization it to the mix, you will find that a new breed of solutions is needed.
Enter Artificial Intelligence (AI) and Machine Learning (ML) to save the day.
How can AI and ML help?
AI can be used to make split-second decisions. Combined with ML, AI can learn how to make better decisions over time. AI can also adapt to changes in buying behavior long before any human has had time to put a report together, analyze it, test a new idea, and iterate until it works. Most of us interact with AI- and ML-powered behavioral recommendation engines every time we shop at Amazon and many other retailers’ sites. AI controls product recommendations, provides optimized guided navigation, and adds inspirational suggestions, all based on customers’ behavioral patterns and buying habits.
This development changes the role of the marketer. AI and ML will take care of most of the merchandising—automatically and in real-time. But a behavioral merchandizing engine needs fuel, a lot of it. This fuel comes in the form of large volumes of high-quality, granular product information. In addition, to be efficient, AI also requires the right content to create a relevant, personalized and compelling product story.
How does the marketing department know if they produce the right content in the right volume? The answer is: by getting feedback on how the content performs in all their channels and then crunching the numbers. Creating too much content adds unnecessary cost, and creating the wrong content might be even more expensive as it is detrimental to sales. Thus, optimizing content production to produce just enough of the right content is crucial to be successful.
Again, AI and ML come to the rescue. Not only can these solutions control what content that the marketing department produces, but also it can create it as well. For example, companies like Automated Insights provide AI/ML-based services that can write product descriptions automatically based on product specification and categorization data.
Get ready now!
AI and ML will not replace marketers anytime soon, but they will dramatically change what marketers do and how they work. These technologies are already a critical component of online merchandising, and are spreading to all other areas of marketing. Companies need to invest in solutions that can help them move at the speed of digital, but also introduce new roles and ways of organizing the marketing department because AI and ML are going to play a significant role in marketing going forward.
Marketing is one of the areas where the use of artificial intelligence (AI) and machine learning (ML) is growing rapidly. This marketing revolution is happening due to many reasons, but mostly because of a growing amount of customer touchpoints, combined with increasing data volumes that make it hard for humans to crunch the numbers. Secondly, micro-moments and fractured buying journeys make it necessary to optimize the marketing message in real time, something that simply cannot be done manually.
AI and ML augment already existing marketing technology, and at the same time, create completely new ways to make marketing more efficient, from real-time personalized merchandising to chatbots that can answer customer questions and take orders. The use of AI and ML within marketing is evolving and is rapidly shifting from early adoption to broad acceptance. Most modern "searchandizing engines," eCommerce platforms, and e-mail marketing tools already use some AI and ML to optimize marketing and sales effectiveness. Voice recognition services like Amazon’s Alexa, Apple's Siri, and Google Home are already assisting us with everyday tasks, including shopping.
Most companies want to sell more products and increase revenue. To do that they need to be more relevant than their competitors when presenting their products to customers in each micro-moment. Relevancy is no longer just about adapting to customer personas; it is about the person. What makes things even more complicated, it is also about the customer’s intent, as some shoppers are prepared to buy and some are only in research mode; some are looking for a birthday gift, and others a solution to a problem. You have to be relevant and tell the right product story to all of them, in their context, in real-time.
The answer to achieving real-time relevancy is not to simply just buy all the new shiny pieces of hyped-up software—especially if you do not have the content in place that can act as the fuel for the AI and ML engines. If you do not already have the content, you need to start by producing it before you can create better customer experiences by reaping the benefits of the new marketing technology. To keep up with new product launches and increasing customer expectations, creating the content is not a one-off thing either. It needs to be an ongoing process that continues to churn out high-quality product stories.
This constant production process of product stories, continuously improving itself to produce more content with higher quality is what I call a "content creation factory." Its sole purpose is to create better customer experiences, fuel all the new initiatives and take advantage of the enormous possibilities that the new AI-powered marketing technology brings. So the time has come to say goodbye to "Product Information Management" because it is no longer enough just to manage information. It needs to evolve into "Product Marketing" as the new purpose is telling better product stories that increase the customer experience by fueling and taking advantage of an AI-powered marketing tech stack.
Johan Boström, Co-founder and Evangelist, inRiver