This is a paper I recently submitted in my Strategic Information Systems Planning course at UFred as part of my MBA studies.
As big data continues to change the marketing industry, industry leaders are looking to artificial intelligence (AI) to reshape their future strategies. AI simulates human thinking and behaviour, such as the ability to reason and learn. Its ultimate goal is to build a system that can mimic human intelligence (Baltzan, 2019). Machine learning is a subfield of AI, which is leading the charge in the application of this technology.
Big Data
AI is having an indisputable impact on the marketing industry. Today, many marketing professionals are attempting to leverage its capabilities in order to capitalize on big data to deliver targeted marketing strategies and campaigns. Big data is the collection of intricate and large quantities of structured and unstructured data (Baltzan, 2019).
Marketers are utilizing AI to process big data in an effort to adapt their activities to customer preferences. The buyer insights marketers are able to extract are being used to create unique and personalized customer experiences in real-time. One of the leaders in this sphere is Amazon. AI is the driving force behind Amazon’s recommendation engine, which is responsible for 35% of the company’s revenue (Morgan, 2018). Amazon is able to use pull data from a variety of sources to create a specific list of recommended products for their customers.
Machine Learning
The most impactful application of AI within the marketing industry is being demonstrated through machine learning. According to Baltzan (2019), machine learning is a subfield of artificial intelligence that allows computers to understand and learn concepts, which enables them to act without human direction. These capabilities are invaluable when interpreting big data and identifying trends, making predictions, or crafting responses while deciphering customer actions.
Machine learning is helping to shape the future of the marketing industry in many ways. Looking ahead, marketers will continue to utilize this technology to enhance the user experience, optimize content, improve personalization, and develop customer touchpoints through chatbots.
User Experience
User experience (UX) is an area of focus for many marketers as attention shifts to the importance of a customer’s relationship with a business. Machine learning is used to improve the customer’s experience using a company’s online platforms. UX leverages machine learning to create an environment that guides users through a desired series of actions (Yang, 2017). This is most prevalent in e-commerce scenarios where it is possible to make personalized purchase recommendations, manage inventory levels, and provide customers with around the clock support.
Content Optimization
Machine learning also assists marketers to determine their most effective content based on customers’ behaviour. This allows marketers to understand the best content topics and formats for their target audience. For example, according to Serpwatch.io (2019), 40% of millennials list video content as their most trusted content format. Businesses that are looking to target this demographic will leverage this data to optimize their content through the insights provided by AI.
Machine learning also has the ability to select content to keep customers engaged. YouTube utilizes this technology to suggest additional videos users may be interested in watching based on their previous viewing habits. This functionality can be applied to any website where additional content can be suggested based on a visitor’s interactions.
Personalization
Increasingly, customers are expecting businesses to provide personalized communication. In fact, according to McGinnis (2016), 52% of customers will consider switching business providers if they do not feel they are receiving enough tailored messaging. Amazon has achieved incredible success through personalization, which is driven through machine learning. They leverage their customer data to customize each individual’s unique shopping experience. All of these efforts work to make the customer feel important, assist them through their buyer’s journey, and drive brand loyalty (Morgan, 2018).
Email marketing is another area that has benefited from the personalization offered by machine learning. This technology can create personalized email marketing campaigns based on the data collected. Personalization enables marketers to deliver relevant content and messaging that is all being driven based on customer preferences. This capability helps marketers reach the right customers at the right time with the perfect message.
Chatbots
Perhaps the biggest future trend in marketing today is the machine learning application of chatbots. A chatbot is a tool that can converse with a customer, interpret their intent, and respond based on pre-determined rules and data (Kaczorowska-Spychalska, 2019).
Chatbots are offering organizations many customer service advantages by being able to solve a customer’s problem through a filtering questioning scheme that will quickly deliver a solution. Additionally, these chatbots are not limited to a company’s website. Many organizations are adapting to the communication preferences of their customers and utilizing chatbots to communicate through apps like WhatsApp and Facebook Messenger.
As the marketing industry continues to develop, marketers are becoming increasingly dependent on AI to help them optimize their use of big data in order to develop strategies. Machine learning is one of the most used applications of AI in the marketing industry. As we look to the future, marketers will continue to build on the development of machine learning to build customer-focused tools that are driven by improved user experience, content optimization, increased customer personalization, and the application of chatbots.
References
Baltzan, P. (2019). Business Driven Information Systems. New York, NY: McGraw Hill Education.
Kaczorowski-Spychalska, D. (2019). How chatbots influence marketing. Management: The Journal of University of Zielona Gora. Vol. 23, No. 1.
McGinnis, D. (2016). Customers Are Willing to Swap More Data for Personalized Marketing. Salesforce.com. Retrieved from: https://www.salesforce.com/blog/2016/11/swap-data-for-personalized-marketing.html
Morgan, B. (2018). How Amazon Has Reorganized Around Artificial Intelligence And Machine Learning. Forbes.com. Retrieved from: https://www.forbes.com/sites/blakemorgan/2018/07/16/how-amazon-has-re-organized-around-artificial-intelligence-and-machine-learning/#4c1755567361
Serpwatch. (2019). Content Marketing Statistics That Defined the Industry in 2019. Serpwatch.io. Retrieved from: https://serpwatch.io/blog/content-marketing-statistics/
Yang, Q. (2017). The Role of Design in Creating Machine-Learning-Enhanced User Experience. Human-Computer Interaction Institute, Carnegie Mellon University.