The move towards personalization

Personalized or 1:1 marketing is the epitome of targeted marketing – creating tailored messages based on customer propensities, affinities, or other key metrics. Whereas, more general classes of marketing attempt to make wide-cast offers that touch as many customers as possible, personalization tries to optimize campaigns and make the right offer to the right person at exactly the right time. Personalization is undoubtedly a more efficient use of resources and greatly increases conversions.

However, the idea of personalized marketing isn’t a new one – think of the “casino whale”, nightclub VIP, or the Amex Centurion. It’s just been difficult to do personalization at scale and beyond high-spend customers, who justify the personal touch. So, while the value in nurturing high-value and loyal customers has been known, there hasn’t been an effective way to reach the masses.

Today, with the increases in globalization, growth of e-commerce/online brands, and various regulatory changes etc many business are becoming increasingly competitive and commoditized. With decreasing barriers to entry and higher stakes, a more customer centric and interactive organization is now necessary to differentiate yourself from the increasing “noise”. Even more interesting, is a recent Gartner report, which cites that 20% of customers produce 80% of future revenue. In order to be successful, you need to be proactive with these customers through direct and targeted messaging, which increases their advocacy, loyalty, and responsiveness. Fortunately, with a good data and infrastructure strategy these insights are generally available in the form of business analytics.

Today, as a result of major technology breakthroughs, such as open-source technologies, decreased costs in computing power and memory, etc, data science has become increasingly more accessible. The playing field has been leveled and we are at the juncture of being able to deliver truly personalized and in-the-moment customer experiences.

Now, the question remains how do we achieve this, since traditional tools aren’t meeting expectations? We’ll be covering the tools you can use, how to approach and interact with data, as well as other general data science questions in upcoming posts. Stay tuned…