A recent report by email marketing provider GetResponse found that although targeting has long been a mainstay of modern digital marketing, 42% of marketers send everyone on their mailing list the very same email.

This is the polar opposite of personalization, and a surefire way to drive respondents to search for the “unsubscribe” button.

Until recently, the options for personalization have not fulfilled customers’ demands for relevant content. One example: segmentation tools require merchandisers to identify a segment and then choose a message; tasks are time consuming and often inaccurate. For example, it’s snowing in Colorado, so a merchandiser promotes parkas to all 20-30 year old men in Denver. But what about the men who just purchased a parka yesterday? Irrelevant messaging causes them to tune out, often times doing more harm than good.

Any retailer knows that shopping has changed drastically over the last few years. Shoppers stay on sites for shorter periods of time when they are not presented with relevant content and offers. Mobile devices have brought a whole new challenge to connecting shopper behavior and data across channels. Overall, customers are less forgiving of generic engagements.

But, all is not lost for retailers. New technologies in machine learning and big data are becoming accessible to brands of all sizes, not just the retail giants. These new technologies have opened retailers up to the possibility of true one-to-one personalized customer engagements, every single time. In the video below, Demandware Chief Data Scientist Rama Ramakrishnan describes how “little data” achieves this.

The predictive intelligence engine crunches customers’ data and context in real time to fully understand their behavior and preferences. Instead of considering a customer as part of a much larger, broader segment, data science allows for the creation of predictive models for each unique visitor. These models are constantly learning based on customer behavior, enabling brands to deliver the right message for each individual customer.

The best part – this technology is applicable for loyal customers and new or inactive visitors to your site. This allows retailers to capitalize and cater to the largest segment of their population – the infrequent shopper.

The beauty of predictive intelligence is that the engine does all of the work behind the scenes. Merchandisers and marketers simply have to set strategies, for example “show compliments” or “show alternates,” and the predictive intelligence engine will take care of the heavy lifting.

Whether you have been trying to personalize your site for years or you’re just starting to look at personalization techniques, now is the time to consider predictive intelligence. Why? Because personalized engagements will be a major differentiator as competition for consumers’ eyeballs and dollars surges among online retailers.

By treating customers as the individuals they are, retailers can turn “one and done” buyers to loyal customers, all while boosting revenue per visit and overall business growth.