Improving data strategy to create the best customer experiences
In association withAdobe
Businesses today have a wealth of information to draw upon. It comes from customer touchpoints, mobile interactions, internet-of-things (IoT) devices, e-commerce transactions, and many more sources. And corporate governance has made it easier to comply with data privacy rules, so organizations can be confident about the data quality.
But usefulness? That’s a major challenge that many brands are grappling with.
“The key is not how you collect data but how you tie it together,” says Anjul Bhambhri, Adobe vice president of platform engineering. Organizations have plenty of data, “but nobody knows what data they have. Data is siloed, fragmented, and scattered across the enterprise.”
Put someone in charge of cataloging the company’s data, says Bhambri, as well as managing the technology to connect the data. Without a data strategy, an enterprise cannot organize the data or gain any insights from it to respond to changing market conditions. It also means the organization misses opportunities to provide data-driven personalized customer experiences that enhance user loyalty, increase sales, or encourage repeat engagement.
Businesses can use data to achieve a true, unified view of their customers. This can be done by creating real-time customer profiles through Adobe Experience Platform, which stitches data across the enterprise and helps companies create and deliver memorable digital experiences. It can handle more than 13 million cloud API calls a day, which means it can handle data from any part of the organization. To prove this capability, Adobe decided to be its own “customer zero.” With the platform, Adobe could fully personalize the customer experience across every channel for its millions of customers. And these experiences update in 10 to 14 seconds—not 24 to 72 hours.
A sure way to earn customers’ trust and loyalty is to understand them better than anyone else, and to deliver what they need, exactly when they need it. “A richer view of the customer allows enterprises to understand the customer’s journey,” says Bhambri. And that’s where Adobe sees real opportunity: “How do we make it personalized, so that the customer becomes a loyal fan?”
One slice of a customer journey
Every consumer is frustrated by uncertainty. When will my order arrive? Will it be what I wanted? A business that offers predictability can get a bigger piece of the pie.
For example, a large pizza chain built an online ordering system that improved its customers’ journey—or at least the pizza’s journey to the customer. Naturally, the pizza chain tracks every order for the benefit of its own workflow. But by sharing that data with customers, the company continually offers reassurance that food is on the way. The pizza chain integrates the real-time data it collects at every step in the process, from the pizza order to delivery, and sends personalized status change updates. The customer gets text alerts that the pizza was put in the oven, that it’ll be ready for pickup in 10 minutes, or an update, based on location data, showing how close the pizza is to his house.
Now, that’s delivering a customer experience.
Organizing data for improved customer experience management
Organized data makes it easier to get a holistic customer view, which enables businesses to offer personalized user experiences at every touchpoint.
The pizza chain isn’t the only example. Manufacturers can share information with customers to notify them of the progress of their orders, with e-mail messages saying, “Your custom server is being assembled!” and text messages to track shipping progress and offer predictability.
But data analysis doesn’t always have to be consumer-related. For example, John Deere is analyzing IoT data to help farmers improve their operations. With the tractor company’s shared real-time data, farmers can analyze performance, make equipment choices, and collaborate; the end result is that farmers can better decide what to plant, as well as where and when. The agricultural company also extended its data platform to third parties, which sets up farmers to share the data with input suppliers. That can trigger automatic ordering (such as seeds and fertilizer) for just-in-time delivery.
Using data to help customers affects every participant in the food chain. IoT-enabled AI and machine learning are being used by a Fortune 200 manufacturing company to optimize inventory levels and predict equipment failure, relying on analysis of sensor data, site monitoring data, and asset management systems.
Pulling this off successfully obviously goes beyond real-time data collection and data governance models that stuff databases into silos. It needs an experience architecture to organize the data, a conceptual model of what can be delivered, and technology that connects the pieces.
A data strategy requires someone to conceptualize the process: collect, connect, organize, then actualize. “That’s when experiences come into the picture,” says Bhambri. Not every enterprise has all the pieces in place to offer this depth of customer experience—at least not immediately. Businesses can the data they collect to start the process and, more so, by the connections they can put into place. “It depends on where the enterprise is on the lifecycle of data from collection from actualization,” says Bhambri. “When the goals are clear, then it’s easier to see if we connected the right data.”
This is a life cycle, says Bhambhri. “You cannot embark on this journey just once. You learn and improve.” And that’s a good deal for data management (and pizza), any way you slice it.
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