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Articles - Data Marketing - November 2, 2019

7 ways predictive analytics can improve customer experience

AI-powered analytics can drive sales to higher levels by helping organizations anticipate customers' needs and exceed their expectations.

It’s a win-win situation. Predictive analytics is revolutionizing the customer-marketer relationship, boosting sales while simultaneously increasing shopper satisfaction.

And it’s all because of data, business’ new superpower, states Paul Gaynor, a partner at professional services firm PwC. “Advanced business analytics gives you the ability to see and predict everything, everywhere,” he explains. “Every interaction with customers, every moving part in your supply chain, every financial transaction, anywhere in the world.”[ Find out how to get started with predictive analytics and the secrets of predictive analytics success. | Beware the 12 myths of data analytics and the sure-fire ways organizations fail at data analytics. | Get the latest on data analytics by signing up for CIO newsletters. ]

In the increasingly cutthroat retail world, predictive analytics gives sellers a powerful new edge, one that more than compensates for the internet’s ever-expanding array of choices and anytime, anywhere comparative shopping.

“Predictive analytics help you know what might happen, prepare a response ahead of time, get ahead of the risks, and influence the outcomes,” Gaynor says. “It’s like looking ahead with a telescope, not glancing through the rear-view mirror.”

Still skeptical that predictive analytics is the most powerful marketing tool since the arrival of online shopping? Then consider these seven ways your organization can use the technology to take customer service — and sales — to the next level.

1. Hyperpersonalized marketing

Hyperpersonalized marketing is all about serving customers the right message at the right time on the right channel. Mastering it requires a merging of art and science. The science part, observes Bindu Thota, vice president of technology for online clothing and accessories retailer Zulily, is giving shoppers the right mix of categories, selections, price points, shipping times and other key services. “The art comes into play when we determine how we curate these elements, how we weave them together to create the most engaging customer experience,” she notes.

Imagine a world in which the retailer knows exactly what a shopper wants even before arriving on the company’s website or app. That’s a capability predictive analytics can provide. “Through data-driven technology, we can create a personalized collection, incorporating thousands of products, every single day — a customer experience that is truly relevant and engaging,” Thota says. “That is the human touch.”

For the first time in history, social media and other online avenues allow marketers to interact with people anytime, anywhere. “This provides an unparalleled opportunity to discover the emerging patterns that help companies marshal their resources and direct their energies more effectively,” explains Adam Lichtl, founder of Pacific Data Science, a data analytics consulting firm. “By collecting all these little points of data around the customer experience and integrating them together we can get a better picture of the customer journey — before, during and after they engage with the company.”

2. Virtual concierges

Consumers have come to expect instant, frictionless gratification in all aspects of their lives and now expect similar experiences with the brands they interact with. “Predictive analytics is a critical tool in delivering a comprehensive view of the consumer to provide these types of experiences,” says Ravi Narayanan, global head of insights and analytics at Nisum, a business and technology consulting firm.

Using AI-driven analytics, it’s now possible to create both an immersive experience and instant gratification. “Spotify and Netflix, for instance, change their suggestions based on what you are watching or listening to in the moment,” Narayanan says.

3. Customer needs forecasting

Organizations can now use predictive analytics to precisely forecast customers’ needs, in some cases even before the individual has made up his or her mind. “Predictive analytics can provide early detection of precursors to change in customer behavior,” Lichtl observed. This allows brands to be more proactive, enabling them to tailor their messages in anticipation, effectively serving the customer before they even know they have a new need. It’s an approach that allows organizations to provide superior customer service, Lichtl noted.

Lance Gruner, Mastercard’s executive vice president of customer experience and care, notes that predictive analytics helps his company ensure it has the right amount of support in place to address customers’ needs in a timely fashion and then exceed their expectations. “In addition to forecasting the volume of inquiries so that we can resource accordingly, we’re also using advanced models to predict the complexity of the inquiry,” Gruner says.

Looking to get inside its customers’ heads, AT&T Business has implemented a customer experience machine learning system. The technology ingests hundreds of unique data elements — literally petabytes worth of data — throughout customer project lifecycles. “It predicts, based upon customer effort, cycle time, retry rates and so on, if a customer will stay a promoter or if they start sliding towards neutral or detractor territory,” explains Sorabh Saxena, AT&T Business’ president of global operations and services. The system generates predictive alarms that drive the next-best-action recommendation, either automatically or via system assist, to ensure that the customer remains a promoter. “The best part is that it is constantly learning and fine-tuning the algorithms as it gets more experience,” he says.

4. Customer churn reduction…

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