The application of artificial intelligence (AI) technologies, such as machine learning (ML), can fundamentally transform the field of marketing. AI technologies can be used to optimize, automate, and personalize marketing tasks. AI can also assist marketers to comprehend their customers better, deliver more effective campaigns, and produce more engaging content. This article discusses the importance and applications of AI in marketing and recent developments in this field.
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Importance of AI in Marketing
Using AI capabilities, such as natural language processing (NLP) and data-driven analysis, in marketing can effectively automate critical marketing decisions and deliver meaningful customer insights. Currently, AI techniques are utilized extensively to obtain more accurate results, generate content, and improve customer experiences.
Several ML techniques have been used in marketing, including back propagation neural network (BPNN), restricted Boltzmann machine (RBM), convolutional neural network (CNN), hierarchical attention network (HAN), multilayer perceptron neural network (MPL-NN), artificial neural networks (ANN), large-scale memory storage and retrieval (LAMSTAR), wavelet neural network, nonlinear auto-regressive with exogenous ANN (NARX ANN), Naïve Bayes (NB), support vector machines (SVMs), and decision trees (DT).
AI can enable smarter and faster decision-making, improve the return on investment (ROI) on marketing initiatives, measure key performance indicators (KPIs) more accurately, improve customer relationship management (CRM) capabilities, and effectively extract crucial insights from customer data.
Using advanced AI tools, marketing teams can visualize their marketing efforts and adjust their marketing tactics in real-time. AI marketing platforms can analyze data more quickly compared to humans, efficiently create marketing strategies, and perform sentiment analysis on historical customer data, and recommend actions based on the analysis.
Similarly, AI marketing tools can assist marketers in identifying actionable insights in real-time from the data generated by a marketing campaign and the optimal placement of advertisements depending on customer behavior. Thus, marketers can obtain the maximum ROI from their investment in a marketing campaign using modern AI marketing solutions.
Digital marketing campaigns lead to the generation of substantial amounts of data, which increases the difficulties of properly measuring the success of such marketing initiatives. AI-enhanced dashboards can allow marketers to link the success of their initiatives to specific tactics deployed by them, enabling them to identify the tactics that were effective and the tactics that need improvements.
Marketing teams can improve their CRM programs by automating regular tasks, such as customer data preparation, using AI technologies. AI techniques can also decrease the possibilities of human error, detect at-risk customers, and send more personalized messages.
Currently, several marketers face significant challenges while planning a marketing campaign due to the vast amounts of data available to them. AI can perform predictive analytics to analyze substantial amounts of customer data quickly using efficient and fast ML algorithms and generate insights about the future behavior of customers, identify patterns that are relevant for marketers in large datasets, and recommend more personalized content.
Major AI Applications
Strategy and Planning: AI can assist marketers in strategizing and planning marketing activities through segmentation, targeting, and positioning (STP) and the strategic orientation of the firm. ML and text mining algorithms can be employed to identify profitable customer segments, while a combination of causal forests, ML, and data optimization techniques can effectively identify the target customer base.
Product Management: AI marketing analytics tools can determine the suitability of product designs based on customer requirements to ensure customer satisfaction. Topic modeling can improve product management by facilitating novel designs and innovations.
During product search, preference weight assigned to product attributes assists marketers in comprehending the product recommender system and aligning the marketing strategies for effective product management. Moreover, deep learning can personalize the point of interest recommendation, while AI capabilities can customize product offerings based on customer needs.
Pricing Management: Pricing is a calculation-intensive function as several aspects have to be considered to finalize the price. Additionally, price variation in real-time based on fluctuating demand further increases the complexity of pricing.
AI-based multiarmed bandit algorithm can adjust price dynamically in real-time, while Bayesian inference in ML algorithm can adjust the price points quickly to match the price of the competitor in frequently changing pricing scenarios, such as an e-commerce portal. Similarly, best response pricing algorithms can encapsulate supply networks, competitor strategies, and customer choices to optimize dynamic pricing.
Place Management: Product availability and access are crucial in marketing for higher customer satisfaction. Product distribution is influenced by transportation, warehousing, inventory management, networked relationships, and logistics problems, which are repetitive and mechanical in nature.
AI can be utilized to address these issues with place management. For instance, cobots can be used for packaging, AI-powered drones for delivery, and the Internet of Things (IoT) for order refilling and tracking. Mechanization and standardization of the distribution process lead to convenience for both customers and suppliers.
Additionally, AI can play an important role in customer engagement. For instance, service robots programmed using emotional AI codes can be utilized for surface acting. AI-based automation of the customer service process can also improve productivity and performance.
Promotion Management: Promotion management involves search engine optimization, advertising campaign management, media scheduling, and media planning. Promotion tactics are increasingly transforming from physical to phygital tactics due to digital transformation worldwide, which has increased the proliferation of social media and digital marketing campaigns, with customers deciding the timing, place, and content.
AI offers customization and personalization of messages based on customer likings and profiles, while content analytics can optimize message and value effectiveness. Customer disliking and liking can be tracked in real-time using emotive AI algorithms. Netnography on social media content can provide new avenues for marketers to align their marketing strategies depending on customer liking.
Challenges of AI in Marketing
Training the AI Solutions: AI solutions require a significant amount of training while learning a new task. For instance, marketers have to invest a significant amount of resources and time to teach an AI solution to talk with their customers in an engaging manner. The development of such AI solutions requires a large amount of data about the preferences of customers and employing data scientists who specialize in training AI models.
Ensuring Data Accuracy and Quality: The effectiveness of all AI solutions depends on the quality of data used during their training. Thus, the training data must be representative and accurate to ensure that the decisions and answers generated by the AI solution are of high quality and effective.
Compliance with Privacy Laws: The data privacy laws must be adhered to stringently as AI solutions used in marketing are trained on the personal information of customers. Businesses deploying AI for marketing can risk incurring reputational damage and heavy fines if they do not adhere to consumer data regulations.
Recent Developments
In a paper recently published in the Journal of the Academy of Marketing Science, the authors developed a three-stage framework for strategic marketing planning that incorporated the benefits of AI by including mechanical AI to automate repetitive marketing activities and functions, thinking AI to process data to arrive decisions, and feeling AI to analyze human emotions and interactions.
The framework laid out ways to utilize AI for marketing research, marketing strategy that included STP, and marketing actions. In the marketing research stage, mechanical AI can be utilized to collect data, thinking AI to perform market analysis, and feeling AI to understand customers.
In the STP/marketing strategy stage, the mechanical AI can be utilized for segment recognition/segmentation, thinking AI for segment recommendation/targeting, and feeling AI for segment resonance/positioning, while in the marketing action stage, the mechanical AI can be employed for standardization, thinking AI for personalization, and feeling AI can be used for rationalization. The framework developed by the authors can effectively guide the strategic planning of AI in marketing in an actionable and systematic manner.
References and Further Reading
Verma, S., Sharma, R., Deb, S., Maitra, D. (2021). Artificial intelligence in marketing: Systematic review and future research direction. International Journal of Information Management Data Insights, 1(1), 100002. https://doi.org/10.1016/j.jjimei.2020.100002
Duarte, V., Zuniga-Jara, S., & Contreras, S. (2022). Machine Learning and Marketing: A Literature Review. https://dx.doi.org/10.2139/ssrn.4006436
Huang, M. -H., Rust, R.T. (2021) A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49, 30–50. https://doi.org/10.1007/s11747-020-00749-9
AI in marketing: How to leverage this powerful new technology for your next campaign [Online] Available at https://www.ibm.com/blog/ai-in-marketing/ (Accessed on 08 October 2023)