Every business should want to create stronger connections with their customers, to better understand their needs and motivations in order to personalize communications and contextualize their touchpoints. After all, customers today want it and expect it.
Recent studies have shown that more than 90% of consumers are more likely to shop with brands that provide relevant offers and recommendations. And many organizations have made progress with personalization based on known customer attributes, particularly when using these attributes as part of scheduled campaigns.
What has proven more challenging, however, is integrating historical customer data with live data and responding intelligently while the customer is still actively engaged. To do this requires something that is often elusive—the ability to use real-time data.
The term real-time has been around forever, but it's frequently misused and often misunderstood. Let's be clear: Data that's two hours old is not real-time, and neither is two-minute-old data for that matter. To be real-time means to be able to use it now—right now.
Some of those challenged with truly harnessing this data will claim that the "right time" is more important than real-time. And they're right, of course. It isn't always critical to respond in real time. But when a customer is actively engaged with your business, that's absolutely the right time to optimize your engagement with them—and that requires the effective use of real-time data.
In Oracle Marketing, we've taken a streaming-centric approach to data, building capabilities for evaluating sessions, events, and users in real time, identifying live sessions that meet our criteria, and sending that data to an orchestration engine for immediate action.
With each click or swipe, customers provide details about their interests. By evaluating and using that data live, we can modify site design, offers, or copy, and generate content or product recommendations to surface on the site or deliver through triggered real-time follow-ups, all while ensuring consistency across channels.
Today, we're also working to leverage real-time data in new ways—using machine learning to make predictions on the likelihood of conversions or abandonment. By evaluating session data in real time, we can use probability scores as part of the triggering criteria, acting upon prospective abandonment before it occurs.
One of our clients aims to use this functionality to identify customers who are about to "click for a live chat" and plans to build out helpful online content to surface before the customer actually clicks. The business can keep those customers in a more cost-effective channel while still providing the information and details they need to be satisfied. It's a win-win!
Using real-time customer data to optimize experiences on the fly will continue to become more and more important as customer expectations rise. As shared in The Forrester Wave™: Experience Optimization Platforms, Q4 2020 report, Oracle "shines with real-time," and we're excited to continue developing innovative ways to engage customers with relevant experiences. What real-time use cases do you believe are the most critical for customer engagement and sales success?