By Sriram D and Anchit Mondal
Anuj, a marketing team lead a popular B2C online fintech company is confused as he prepares the annual marketing budget. What budget should he allocate for traditional marketing and how much to allocate to digital? Should he listen to his boss and spend money on Artificial Intelligence and Machine Learning (AI-ML)? This is a conundrum that most marketers are facing today.
While traditional marketing (e.g., print / TV ads, OOH) are effective in creating awareness, they are less effective in retaining customers. Targeted offerings communicated using digital marketing tools (e.g., mobile apps, mails) are more effective. Additionally, customer engagement through social media handles and digital platforms help understand customers and retain them. AI-ML has been a relatively new phenomenon in marketing, but has already proven to be effective when used properly.
AI-ML is being used effectively in other functions and marketing will definitely not be left behind!
AI refers to the development of computer systems that can perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. ML on the other hand, is a subset of AI that focuses on the development of algorithms that can learn from historic data and predict / suggest decisions. ML algorithms can be trained to perform a wide range of tasks, including image and speech recognition, natural language processing, and predictive modeling.
In operations, AI/ML techniques help to identify bottlenecks, streamline workforce, automate repetitive tasks leading to improved efficiency and reduced costs.
AI-ML models evaluate creditworthiness, giving lenders the ability to more accurately determine loan approvals and interest rates in the finance domain. Additionally, AI-ML algorithms enable real-time market data analysis, trend identification, and trade execution allowing quicker and more effective trading choices.
Specific applications of AI-ML in marketing include implementation of Chatbots, predictive analytics, personalization and ad optimization
Chatbots
Chatbots are an example of how AI is shaping digital marketing. They are AI-powered virtual assistants that can handle customer inquiries, provide support, and even process orders. Chatbots have demonstrated saving businesses time and money by automating tasks that would otherwise require human intervention. They also provide customers with instant responses, which improves the overall customer experience. Sephora, the cosmetics retailer, has integrated AI chatbot into its marketing strategy. Sephora’s chatbot, called the Sephora Virtual Artist, uses facial recognition and augmented reality to help customers try on makeup virtually. Since the pandemic, many organisations have invested in chatbots to handle customer queries.
Predictive Analytics
Another key benefit of AI in digital marketing is its ability to analyse large amounts of data and provide insights that can be used to optimise marketing campaigns. Predictive analytics uses ML algorithms to identify patterns and make predictions about customer behaviour, such as purchase intent and likelihood to churn. With this information, businesses tailor their marketing efforts to better target and retain their customers. Coca-Cola has used AI to improve its marketing campaigns by using predictive analytics.
Personalization
Personalization is a crucial aspect of digital marketing. AI algorithms analyse customer data, such as browsing history and purchase behaviour, to create personalised experiences for individual customers. This includes personalised product recommendations, targeted advertising, and customised email campaigns. By delivering more personalised content, businesses increase customer engagement and loyalty. Netflix has been a pioneer in the use of AI for personalization. By analysing viewers’ watching habits, ratings, and searches, Netflix’s algorithm generates personalised recommendations for each user.
Ad Optimization
Ad Optimization refers to the process of improving the performance of online advertising campaigns by selecting the best ad-creativity, targeting, and bidding strategies. AI plays a crucial role in ad optimization by using machine learning algorithms to analyse vast amounts of data and predict the best ad combinations to achieve a specific campaign objective. McDonald’s has used AI to optimise its ad campaigns. By analysing customer data, McDonald’s AI-powered tool, called Dynamic Yield, can create personalised offers and menu suggestions based on factors like weather, time of day, and customer behaviour. For example, if it’s hot outside, the tool might recommend a cold drink. McDonald’s has also used AI to test different combinations of items on its menu boards to see which ones result in the most sales.
On the whole, marketing stands a lot to gain from digital marketing and AI-ML. However, we find many traditional organisations are slow to adopt emerging technologies. A culture of experimentation and judicious use of A-B testing may help organisations better use their marketing budgets and also optimise costs by focusing on right marketing activities. The future is likely to be a learning experience for Anuj and other marketing managers out there.
The authors are professor of marketing at Great Lakes Institute of Management Chennai and MBA student, Great Lakes Institute of Management , Chennai.
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