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The MEAL lab is for media service innovation and audience engagement in the era of digital convergence and omnichannel marketing to help industry partners retain audiences, embrace differences, and engage audiences fully.

The MEAL lab differentiates itself by basing our research and analytical insight on the Peppers and Rogers (1997) methodology of the “5Is” with machine learning techniques to assist our industry partners to retain audience engagement. The benefits of the 5Is methodology are tremendous.

First and foremost is that this is a systematic methodology to help industry partners evaluate the effectiveness of their customer/audience-oriented activities. For instance:

1. how do they know who their customers are (identification)
2. how they offer value to each customer based on idiosyncratic needs (individualisation)
3. how an interactive dialogue can be created with customers to gain a greater understanding of both their articulated and non-articulated needs (interaction)
4. how all parts of the enterprise coordinate the activities/touchpoints with respect to each customer (integration)
5. how they secure the trust of the customer by following well-thought-out terms and privacy (integrity). 

Grounded on these five pillars, we understand how our industry partners keep their most valuable customers loyal and profitable. Instead of reinventing the wheel, MEAL highlights how machine learning techniques can be amalgamated to re-calibrate their customer retention strategies.

How MEAL Applies Machine Learning to Customer Retention

All businesses have their own unique lifecycle marketing strategy, but the purpose is the same: to engage customers, increase revenue, grow a brand, and ultimately cater for heterogeneous mixture of audience needs/values. Different from the buyer’s journey or conversion funnel, lifecycle marketing considers a customer long after they make a purchase. The focus is to bring in customers and turn them into loyal brand advocates.

However, media touchpoint has evolved beyond the TV and phone to new channels such as chat, email, social, and wearable technology to name a few. This channel proliferation is now ever-present. Offering a compelling omnichannel experience used to be the bleeding edge of media service providers. Now it’s a requirement for survival.

To keep ahead of the curve in an evolving marketplace, one of the key elements of the omnichannel experience is hyper-personalization. Like personalization, hyper-personalization highlights the interaction of a brand with its consumers on a customized, individual level. Nonetheless, hyper-personalization goes beyond basic customer information, pulling from real-time and behavioural data to deliver highly relevant, individualized messages. As such, this becomes an effective strategy to drive deeper, lasting customer engagement/loyalty with the industry partners.

ML (machine learning) can in particular help brands create hyper-personalized experiences for their customers. To this end we aim to employ the state-of-art machine learning solutions to enable researchers and industry partners to automatically transform data from MarTech tools (i.e., descriptive, diagnostic, predictive analytics) into marketing intelligence (i.e., how can we make it happen, so called prescriptive analytics) ensuring their competitiveness through audience value optimization in the audience lifecycle.