“Why You Aren’t Getting More from Your Marketing AI.”

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You Aren’t Getting More from Your Marketing AI.”
Artificial intelligence (AI) can supercharge marketing and decision-making capabilities, making it a target for embracement by chief marketing officers (CMOs). However, the need to choose their technologies wisely as the wrong application might not be significant to the companies. Therefore, Eva Ascarza, Michael Ross, and Bruce G.S, author of Why You Aren’t Getting More from Your Marketing AI, explore companies’ frequent mistakes when dealing with AI. The article avers that more than 60% of firms that invest in AI cannot see any profits from its application. Ascarza et al. (2021) argue that most such companies are inclined to make the following errors; they do not ask the right question- making AI solve the wrong problems, unrecognition of the value of being wrong and its cost, and they hardly allow AI capabilities to make more granular decisions. This essay seeks to describe the problems discussed in the article- Alignment, Asymmetry, and Aggregation, success measures, and how AI can be used for decision-making by corporates.
The telecom company’s marketers described in the article utilized AI to predict which consumers were most likely to leave (Ascarza et al., 2021). They used AI predictions to entice the at-risk customers to stay; however, the consumers went anyway. The following are errors that the managers had made;
Alignment: The managers at the Telecom Company failed to ask AI the right question. Figuring out how to utilize marketing finance to reduce churn should have been the uttermost concern for the company’s management. Instead of concentrating on what customers are likely to leave, the company’s marketers should have asked the AI, “what customers could likely respond to a promotion instead of leaving?” On the other hand, a gaming company wanted to increase revenue by encouraging users to spend more. However, they failed even after applying AI. Similarly, the marketing managers should have asked AI how to expand their in-game spending instead of how to increase the engagement of players. Therefore, marketing management failed to focus on the exact specific problem to be addressed.
Asymmetry: Ignorance to exploit the cost of being wrong is a problem for most companies. Ascarza et al. (2021) argue that AI’s predictions should not be necessarily correct and needs recognition by a company’s marketing manager. This is because a wrong forecast can be very detrimental if left unchecked. Therefore, it is essential to acknowledge that the predictions by AI can be wrong; thereby, the role of marketers is to analyze the cost of such errors if they occur (Ascarza et al., 2021). However, such issues are usually assumed, advancing to detrimental mistakes.
Aggregation: While companies can produce a stream of operational and consumer information, which AI could utilize to make high-end decisions, many firms ignore such potential and continue functioning as per the old decision-making models (Ascarza et al., 2021). Therefore, failure to exploit granular predictions is a bigger problem since these firms underestimate the value of utilizing granular predictions. For example, using operational and customer data, asking keywords per channel for decision-making could forecast a lifetime value to customers.
The Telecom company defines success by ensuring that the targeted customers do not jump ship (Ascarza et al., 2021). However, their method of measuring success was not relevant since some customers might have stayed in the company without receiving any promotion. According to Ascarza et al. (2021), what constitutes success is targeting only the consumers with high churn risks that are ductile and not targeting those who are not. This is because it is not a success when an untargeted customer leaves since if a customer is going to leave, not targeting them is a success (Ascarza et al., 2021). On the other hand, an opportunity could have been missed if, by receiving a promotion, a customer could have stayed.
Ascarza et al. (2021) aver that AI tools can be used to make high-frequency predictions. AI can process the information collected by a marketing company to predict and make high-end decisions that could be of lifetime value to customers. To use AI in decision-making, the authors argue that marketing teams should eliminate waste and missed opportunities resulting from predictions generated (Ascarza et al., 2021). Therefore, using AI to tell what incentives would be best for customers can be appropriate. Additionally, Ascarza et al. (2021) contend that marketing teams should leverage granular predictions to make AI predictions more frequently, leading to accurate decision-making.

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