AI And Machine Learning: The Secret Behind Santa’s Success

Ursa Mihajlovic

There’s a lot we can learn from Santa. Every year he delivers presents to an estimated 22 million children an hour, over the course of just one night – that’s an estimated stay time of about three ten-thousandths of a second per home.

Since Santa would never rush the important job of distributing presents to children and spreading holiday cheer to everyone, he’s had to find a way of traveling from house to house at great speed. According to NORAD, the only logical conclusion is that Santa functions within a different time-space continuum from the rest of us.

Santa’s magical sleigh journey aside, what’s truly impressive is how Santa always knows exactly what gifts will satisfy his millions of young customers.

With little time or resources at his disposal – apart from the eight reindeer and elves that help him – he’s got to shift through millions of handwritten letters, as well as all the messages sent via social media, messaging apps, and chats to map and analyze every child’s request.

He’s also got to detect and monitor the behavior patterns of children throughout the year so children who have been good get rewarded with the gift of their choice on Christmas Day.

It’s a complex challenge that requires exceptional omnichannel capabilities, a 360-degree view of his pint-sized customers’ interactions, and a large sprinkling of automation to achieve the data-driven insights needed to evaluate behaviors, adapt gift profiles, and match the appropriate present to the right child.

Clearly, Santa’s North Pole operation needs to react in the moment to changes as they happen. What’s more, to deliver on his unique brand values of efficiency, personalization, and customer satisfaction, Santa has mastered the art of predicting the desired outcomes of every child that interacts with him. It’s a skill that ensures he maintains an exceptionally loyal customer base.

Like I said before, we can all learn a lot from Santa. Especially when it comes to personalizing customer conversations to individual customers’ preferences. Or using behavioral data to take customer service to a new level of engagement.

It won’t have escaped your notice that today’s consumers expect businesses to be digitally savvy, knowledgeable, and joined up in their interactions with them.

Little wonder that a more customer-centric, omnichannel approach now depends on being able to integrate online, call center, and bricks-and-mortar stores to achieve a single view of the customer.

That said, a recent survey of 47 global telco operators found that, despite their omnichannel capabilities, many are failing to spot the warning signs that indicate customer dissatisfaction.

In other words, they’re not using the wealth of data at their disposal to proactively identify customers likely to churn – and targeting these high-risk customers before it’s too late with appropriate incentives that make them want to stay.

Worse still, the survey findings reveal many telcos still rely on old customer segmentation approaches that are hampering their ability to drive a more effective and personalized digital marketing approach.

Part of the problem is that telcos are sitting on a mountain of data about their customers – but it’s what you do with this Big Data that counts. And that’s where technologies like artificial intelligence (AI) and machine learning can help.

We’re already seeing forward-thinking companies embracing AI, machine learning, and analytics to transform data into insights that make it easy to intelligently address customers at the right time, with unique and personalized messages based on that person’s known tendencies and prior behaviors.

It’s a revolutionary approach that transforms enterprise data into business value. For example, you can now use AI technologies to spot the sequential patterns that typically lead to customer churn – smartphone upgrade, text plan change, change from prepaid to post-paid, and finally contacting the call center – and create prediction models that make it possible to effectively target at-risk customers early on in their journey.

Similarly, machine learning can provide insights into customer behavior over time, identifying cross- and up-sell opportunities that boost retention rates.

Let’s say a customer wants to upgrade their mobile plan so they can have more data to stream videos. Powered by AI and data analysis, a chatbot can personalize the conversation, based on that customer’s preferences, to up-sell services in the right context and in real-time in order to offer a plan with unlimited video streaming. Clearly, the ability to nudge customers toward good outcomes with the automatic offer of a personalized plan that’s better suited to their needs is a win-win scenario for both operator and customer.

Sophisticated AI tools for analyzing customer data are finally allowing companies to optimize every marketing point to drive more efficient and relevant experiences and react fast to opportunities. It all adds up to a smarter sales and marketing approach that’s truly transformational.

One thing is clear. AI technologies will be powering a new approach to customer relationships in every channel. Indeed, by 2020, our sales and marketing campaigns will look very different, so say goodbye to old-school blanket approaches that do little to personalize the conversation.

Instead, get ready to demonstrate how, just like Santa, you’re using appropriate listening to predict exactly what every customer really wants for Christmas.

Learn more about new ways of Influencing Customers Through Infinite Personalization.


Ursa Mihajlovic

About Ursa Mihajlovic

Urša Mihajlovič is Global Marketing Director for Telecommunications, Media, and Public Sector Industry Units at SAP Hybris. She has more than 15 years of marketing experience working in IT and telecommunications industry.