Council Post: Three ways AI powers robust customer engagement strategies
Consumer behaviour is in constant flux in the rapidly digitising world. Hence, it is crucial for brands to innovate faster to keep the customers hooked. Artificial intelligence can play an effective role in improving customer engagement metrics. Brands can use data and analytics to connect with customers, identify their needs and deliver the goods. According to Inkwood Research, AI in marketing was valued at USD 11.73 billion in 2020, and is expected to reach USD 82.70 billion by 2028, growing at a CAGR of 26.58% during the forecast period. Source: Inkwood Research AI is the answer to the three Ws of customer engagement. Who: Identify which customer cohorts need to be engaged or retainedWhat: The kind of communication that has to be sent to each cohortWhen: The best time to send this communication and on which channel for the most optimum results Who: Identify customer cohorts Organisations can use AI and machine learning to study customer data to get insights and predict customer behaviour. You can identify customers who are likely to make a purchase or a flight risk. Based on such insights, the company can create targeted campaigns to push customers towards the desired action. Brands can also use AI and ML to gather real-time customer behaviour data to build recency, frequency, and monetary scores. Recency: The number of days since the customer’s last purchaseFrequency: The number of orders placed in a given time periodMonetary value: The total amount of money spent by the customer over a given time period What: Crafting relevant message Messaging is key in building the right experience for the consumer. Usually, a message has three key components: Text (email subject lines, push notification text, etc), Media (image, video, gif, etc) Call to action Companies can optimise the messaging via rigorous A/B and multivariate testing. Marketers can test their campaigns, their various elements, measure the revenue impact, and optimise for it. They can compare results of A/B and multivariate testing with a ‘control group’ to decide the best way forward. When: Communicate the right way Brands leverage AI/ML to understand customer preferences and automatically pick the most optimum combination of time and channel for the highest ROI. Timing and the right channel is everything when it comes to delivering the message to the customers. Source: MoEngage Brands use analytics and machine learning to parse the past behaviour of customers to figure out the best time to engage. This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill out the form here.
AI is the answer to the three Ws of customer engagement. 1) Who: Identify which customer cohorts need to be engaged or retained 2) What: The kind of communication that has to be sent to each cohort 3) When: The best time to send this communication and on which channel for the most optimum results
Brands leverage AI/ML to understand customer preferences and automatically pick the most optimum combination of time and channel for the highest ROI.
["AI (Artificial Intelligence)", "customer analytics", "customer engagement", "customer experience", "Customer segmentation"]
["data science", "Go", "artificial intelligence", "machine learning", "Customer segmentation", "AI", "R", "ML", "RAG", "customer experience", "Aim", "customer engagement", "analytics", "customer analytics", "AI (Artificial Intelligence)"]
["AI", "artificial intelligence", "machine learning", "ML", "data science", "analytics", "Aim", "RAG", "R", "Go"]
Nalin Goel is the Senior Vice President, Product at MoEngage Inc. Nalin established the vision for Machine Intelligence at MoEngage and has been instrumental in setting up the Customer Insights and Data Sciences team. He led the launch of MoEngage's Analytics Suite.