Evolution of Customized Marketing
The first technology is content customization, the ability to adapt content and advertising to the individual customer. This is easiest to appreciate in the context of digital delivery of videos and other media, where content and ads are tailored to the individual viewer. But the development of content customization has penetrated less conspicuous areas also, such as the coupons delivered to customers at supermarket check-out, political fundraising emails, music playlists, financial service offers, cell phone plans, and so on.
While it has long been possible to implement a rudimentary level of customization in product marketing, for example through direct mail, the evolution of the digital economy has made customization down to the family or individual consumer level both practical and cost-effective on an entirely new level.
“Flexible product with great training and support. The product has been very useful for quickly creating dashboards and data views. Support and training has always been available to us and quick to respond.
- George R, Information Technology Specialist at Sonepar USA
Advent of Machine Learning
The second key technology that makes data such a critical asset is the proliferation of machine learning algorithms for product associations and recommendations. Such algorithms have evolved from early "market basket" association learning to the sophisticated algorithms that guide YouTube recommendations and Facebook feeds to their users.
Being able to predict what a user or customer will want to purchase or engage with based on their past activities is a tremendously effective way to keep existing customers locked into a service and consuming ever more content and advertising. It is this technology that generates the knowledge that makes content customization work. It would not be possible to deliver a custom experience to users or customers without the understanding of what users want to experience, or what content and advertising they can benefit from. Machine learning makes it possible to analyze millions of users to determine what particular content can be delivered to a particular customer at a particular moment for maximum utility or revenue.
Together, content customization and machine learning combine to make data among the most valuable assets in the possession of modern organizations. It is worth mentioning here also an ancillary technology that provides a catalytic effect to the combination of customization and preference learning: social sharing. While "word of mouth" or "heard about it from a friend" has always been an effective and often low-cost marketing technique, the technology of social sharing via email, Facebook, WhatsApp, and hundreds of other tools, has accelerated the ability of algorithms to feed data to the content customization engine. With social sharing thriving in the digital world as never before, machine learning algorithms can generate insight based not only on an individual family's preferences and habits, but on the preferences and habits of their neighbors and friends. This enriched data can then be used to feed an ever expanding program of content and ad customization both to the original consumer and to members of that user's social network.
This cycle of machine learning feeding content customization, energized by social sharing, is one that is primed to become greatly magnified in coming years, which will ensure that data remains among the most crucial and vital assets to any business enterprise for the foreseeable future.