The 3 Strategic Goals of Machine Learning for Marketing

Machine learning strategies among automation, optimization, and augmentation goals.

Artificial Intelligence (AI) and its subset Machine Learning (ML) enable marketers to amp up automation, optimize processes, and augment workers in ways that make our lives as employees, customers, and family members a whole lot better.

Thanks to AI and ML marketing activities are already partially automated for routine tasks, optimized for non-routine functions, and augmented for complex tasks. 

“Definitely automation, optimization, and augmentation are the goals of AI. However, automation represents a key priority for many managers. If there’s something I can automate, that’s where I want to start.” 

Jim Sterne, Emeritus Director of the Digital Analytics Association

As managers become more familiar with cognitive technologies, they increasingly experiment with business solutions that combine AI automation, optimization, and augmentation. So, how do we define these three strategic goals of AI?

Automation

Every business carries some inefficiencies that can be replaced by high-performing algorithms. Companies have the opportunity not to automate internal processes invisible to consumers, such as segmentation and targeting; they can also deploy solutions that delight customers with proactive and automated services.

Several companies are innovating by pushing the boundaries of customer-facing automation. Vodafone empowers users with AI-fueled self-service solutions. The chatbot ” Tobi ” provides personalized recommendations with a + 100% conversion rate Compared to the Vodafone website and answers key requests with at 80% rate resolution.

Content automation is increasingly responsible for the creation, curation, and distribution of brand messages. Bol.com is using Google’s automated bidding features in display and video advertising. The automated bid system outperforms the currently optimized campaign with a 38% improvement in customer acquisition costs. 

Optimization 

Enterprises are using artificial intelligence algorithms to optimize processes that reduce overhead, decrease turnaround time, and improve output. Every marketer can identify countless opportunities to infuse AI into the brand building process to maximize practices of consumer acquisition and retention.

An example of AI-optimized experience is offered by Olay ‘s Skin Advisor, a deep learning powered app, that aims to determine the “skin age” and recommend the best product among hundreds of different variations. After the introduction of the Personalized Skin Advisor, Olay reported that he had completed his conversion rate while engaging with 4 million consumers. 

“I have seen a fair amount of cat food advertising in my life and I do not own a cat or plan to own one. Every single penny invested in having me watching an ad for anything related to cat food was wasted. ” 

German Ramirez, Founding Partner of The Relevance House

Lowe’s rolled out a retail service robot called LoweBot to help customers by answering simple questions in 70 languages while employees focus on added-value services. Because of its ability to effectively navigate the store, LoweBot can scan the shelves in search of incorrect prices, misplaced products, and out-of-stock items.

Augmentation 

Algorithms can help teams operate in a traditional way to get more out of their marketing effort by adding layers of intelligence. In some organizations, AI augments rather than automates activities and processes. An increasing number of organizations believe in the coexistence of machines and humans.

Capgemini found that 86% of those managers implement AI solutions at large. Salesforce ‘s Einstein leverages rules-based and predictive models to provide agents with contextual recommendations and offers for customers. Thesis “next best actions” suggested to employees, such as “give free shipping” or “offer zero percent financing” lead to higher customer loyalty and upselling opportunities.

The service provider Botmind that helps to deliver the same live chat. Whenever there are extensive unstructured dialogues, they will immediately transfer the matter to an individual. This hybrid process results in higher consumer satisfaction and significant cost reduction. 

“Augmentation is the main goal and it’s what all our technology is about. AI is not about replacing people but enabling all marketers to work better, faster and smarter.”

Javier Guillo’s Lopez, Digital Business Development Watson at IBM 

 

Conclusion

Designing an AI strategy requires managers to systematically evaluate marketing needs in terms of automation, optimization, and augmentation in relation to the perceived benefits of prediction, anticipation, and personalization.

This article is based on the independent research report “The Rise of Machine Learning in Marketing” developed by Alex Mari, Research Associate at the University of Zurich. 

Alex Mari is a Researcher at the Chair for Marketing and Market Research at the University of Zurich where he studies the impact of AI and Machine Learning on consumer-brand relationships. He is the former Director of Digital Marketing at Sonova Group, P&G, and CEO of two technology-driven startups. He teaches digital & AI marketing in business schools, and he privately advises companies.

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