Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. Watch now.


The new normal, the age of disruption, the millisecond economy — however you choose to label it, the last two years have been a massive catalyst to change the way businesses engage with their end customers. While most of the focus on this transformation to date has been on the evolution of digital touchpoints, the real revolution has been happening behind the scenes.  Increasingly, businesses are finding that the difference between success and failure in this digital-first marketplace is rooted in their ability to harness data to inform their digital strategies

A sleek interface and highly functional mobile app are not enough to impress customers in a crowded marketplace where every business has nice-looking, utilitarian technology. Because 72% of all customer interactions are now digital, having technology that works has become table stakes. Brands that want to separate themselves from the pack and create memorable, engaging digital experiences need to push further. That means delivering highly personalized, intelligent digital solutions that can predict what customers want and serve it up before they even ask for it.

Tapping data to unlock mass personalization

Until recently, the prospect of executing this type of mass personalization strategy at scale would have been impossible. Companies would try to create a handful of personas and tweak their strategy for each group. Relying on customer satisfaction surveys and net promoter scores, they could create generalized buckets of customer types. But the idea of delivering a completely tailored digital experience to each individual customer across a customer base of thousands, hundreds of thousands — or even millions — of customers was always thought to be impossible.

That changed with advances in AI-powered content extraction and cloud-based data integration. These technologies make it possible to analyze large volumes of unstructured customer data and infuse it into multiple points in the customer engagement journey. This enables companies to not only deliver a more personalized customer experience, but also anticipate future customer behavior.

Event

Intelligent Security Summit

Learn the critical role of AI & ML in cybersecurity and industry specific case studies on December 8. Register for your free pass today.

Register Now

For example, in the ecommerce space, which now accounts for roughly 20% of all retail sales worldwide, retailers are finding new ways to use hyper-personalization to zero in on individual customers by tapping into highly granular data on everything from how a customer navigates the website to previous payment histories.

The key to identifying what resonates and delivers this unique user experience is data that can deliver a truly 360-degree view of the customer. Fortunately, most retailers have more customer data than they know what to do with. The challenge is harnessing it and being able to distill it down to specific patterns of behavior like where shoppers are drawn to on a page, what they click, the site flows that lead to transactions and the flows that lead to customers clicking away.  

This detailed, customer-specific approach is equally important on the payment side of the equation. With more retailers rolling out ‘buy now, pay later’ programs and other new forms of digital payments and lending offers — the ability to make on-the-spot, real-time credit decisions has become a crucial component of an online retailer’s overall customer experience. Relying on static data from credit bureaus is not enough. It requires detailed knowledge of customer behavior and the ability to tailor credit offers accordingly. 

Likewise, when the conversation turns to collections, retailers and lenders are finding that highly personalized, customer-specific interactions are helping them recover funds faster, while preserving customer relationships.

The promise of data-driven customer intelligence

Examples like this are playing out in virtually every industry. Auto manufacturers are collecting “voice-of-the-vehicle” data that includes billions of discrete touchpoints between drivers and their connected vehicles, consumer shopping behaviors and customer experience data. FinTechs are capturing merchant-specific data, such as customer purchase history, offer acceptance behavior, loyalty membership tier, etc. which can feed into the optimization of underwriting and identity verification processes. Even live sports and entertainment franchises are tapping into things like regional mobility data, weather analytics and media preferences to fine-tune their fan experiences.

While the results of these data-driven analytics exercises often manifest themselves in a digital solution, the real magic making it all possible is in the data. By breaking down the silos that used to restrict access to that data and opening up the bandwidth required to process it at scale, data leaders are finding it is possible to truly know their customers individually, deliver tailored solutions through hyper-personalization and serve up products and services based on what they need today and will want tomorrow. 

Rohit Kapoor is the vice chairman and CEO at EXL.

DataDecisionMakers

Welcome to the VentureBeat community!

DataDecisionMakers is where experts, including the technical people doing data work, can share data-related insights and innovation.

If you want to read about cutting-edge ideas and up-to-date information, best practices, and the future of data and data tech, join us at DataDecisionMakers.

You might even consider contributing an article of your own!

Read More From DataDecisionMakers