According to Levitt (1994), marketing is conceptualized as focusing on the needs and desires of customers. The four components of marketing, including product, place, promotion, and price, form the core of a functional model of a business. In the digital revolution, the product dimension has become more dependent on technology. Artificial Intelligence provides applications in every industry context, including place, promotion, and price. In addition, segmentation, targeting, and positioning are essential components of marketing to precisely define the target client. 61% of AI applications deliver improved targeting, and 55% agree they can better identify quality users/audience, according to Statista’s 2019 report on AI’s impact on digital marketing. Embedded with modern neural network algorithms, Artificial Intelligence is able to comprehend clients significantly better, which is essential for organizations to optimize their internal processes. The front-end customer-centered research data analysis feeds product innovation, dynamic pricing, customized promotion, and market dimensions. The macro research components in market research also depend upon the hidden patterns in product and consumption data.
Artificial intelligence has enough competences with classification models such as support vector machines, decision tree modelling, random forest, ANN, and others to detect patterns that could assist organizations in the development of new products or the identification of new markets. With the digital revolution, marketers today have access to massive amounts of big data containing consumer insights, such as consumption data, reviews on social media platforms, product development, and so on, and AI has the potential to analyze this data with relevant results.
The following are some noteworthy applications of AI in marketing:
1. Recommendation Models: In today’s digital age, product/service delivery is highly dependent on recommending appropriate products/services to potential buyers. Recommendation models work in both polarizations, depending on individual personal preferences and product cross-selling associations. With embedded recommendation models, organisations have reported increased sales and a better consumer buying experience.
2. Chatbot: Pre-purchase and post-purchase CRM is a critical component that influences customers’ points of purchase. Chatbots offer efficient 24x7 customer assistance presence to improve the customer experience with the firm. Chatbots embedded with target functionality support provide flexibility to customer relationship management while decreasing response rate to customers.
3. Retention Rate of the Customers: The difficulty in the digital age is that clients frequently switch to other brands. Predictive retention rate segmentation allows organizations to discover which customers are on the verge of quitting their product/ service ahead of time. With such categories identified, organizations can conduct retargeting campaigns to find the gaps that are causing customers to abandon their product/service.
4. Customer Profiling: As previously discussed, segmentation is critical in identifying clients who have the potential to purchase the product/service. Demand forecast, sales prediction, customer clustering, and RFM analysis all help organizations gain a deeper understanding of their customers.
5. Programmatic Advertising: Bidding processes on the most competitive keywords are included in digital marketing campaigns conducted on any channel, including Twitter, Facebook, and others. Real-time bidding, in which customers bid on keywords at the same moment they intend to make a purchase in the digital environment, is one of the features offered by programmatic advertising. The organization reached the consensus that, rather than pouring money into campaigns in which the end customers are not specified and can be substituted with bots, programmatic programming advertising would be more effective in providing real-time client identification.
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