Decades ago, like many reading this post, I had a youth newspaper route. Sunday morning was when I collected subscription payments. My primary objective during this process was not to collect money, but rather to maintain and build relationships with my customers.

Through this process, I knew, for many of my customers, who in the household read Newsday and what their content interests were. I knew when to visit each customer to give myself the best chance for the largest gratuity.

I knew which customers to see less frequently because, based on when they picked up their newspaper and what they said about it to me, I thought they may stop their subscriptions. And, I knew with which customers to spend more time with the goal of growing a more prosperous relationship.

Data makes it possible to predict reader actions.
Data makes it possible to predict reader actions.

In fact, within five years, I grew the more prosperous relationships into an income that was in the range of 75 times more than the income from the youth newspaper route. I did this through establishing and growing relationships, and even purchasing a business from one of my customers.

Today, through audience data and analytics, inclusive of predictive modeling and coupled with consumer research and customer experience management, we have an unprecedented level of intelligence into the quality of Newsday’s relationship with each and every one of our customers.

Among the unprecedented intelligence, we understand the habits and dynamics each individual customer has with the various touch points of our brand.

We know, with a high level of accuracy, the cancellation probability for each customer. We have insight into the steps and stages of why and when they cancel. We know the price elasticity of each customer.

We know the content consumption patterns that predict greater retention and price elasticity. We know which customers will and will not be influenced by retention treatments. And we even know an optimal personalised retention treatment for each customer.

However, all of this customer-level relationship knowledge is just the prerequisite for actionability. At Newsday, we have developed many practical data-enabled strategies, some smart and quick to market, and some innovative and under development.

An example of one of the smart and quick-to-market, but very impactful, strategies is an expression of gratitude via a personal thank you note sent to customers when an acceleration in churn probability (most often a decrease in routine with one or more touch points) is detected. This has proven to reduce churn by 30%, sustained at six months post-treatment.

This is an example of one that is more innovative and under development: Data related to the understanding of content consumption patterns that predict greater retention and price elasticity coupled with satisfaction by content type is top fuel for content development, targeted marketing, and revenue diversification considerations.

The impact of making data analytics actionable is tremendous to our business, and it is at the core to aiding in strategic decision-making across the organisation. No, I do not see 75x growth in total revenue, despite how opportunist I am. However, I do see our data-related investments in time, talent, and technology yielding such otherwise inconceivable returns.

If you’re not already in the actionable data game, after data fusion and ideation, you will likely get the feeling of customer intimacy. And, after data actionability, you may get the feeling that it’s a new frontier.