The world has more data than ever before. In fact, it’s estimated that by 2020, we’ll produce 44 zettabytes every day. That’s equal to 44 trillion gigabytes. One gigabyte can hold the contents of enough books to cover a 30-foot-long shelf. Multiply that by 44 trillion. That’s a lot of data — too much for most companies to process. And yet front-line employees are still often left operating with data that’s “too little, too late.”
Most organizations are challenged to extract meaningful insights from their customer data when they’re drowning in so many data feeds. Data is not always shared efficiently. Many of the world’s biggest companies operate in silos — for example, their customer service and sales departments do not share a customer relationship management (CRM) database, and employees don’t collaborate around the customer to ensure a powerful customer experience. More often than not, employees in one department don’t even know the employees in other departments, let alone use data that spans the organization. This often results in wildly inconsistent customer experiences that make companies look disconnected and when the business becomes a little more complicated — such as with a merger or acquisition — the situation gets even worse. For example, I was recently shopping at a major mattress retailer that was acquired by a bigger company. I had previously bought a mattress from them a year before. The mattress store sales rep told me he did not have any information about that mattress because after the acquisition, they threw out the old customer relationship management (CRM) data. That’s not a great customer experience.
Organizations need to create easy and elegant customer experiences; how can they overcome their data challenges to satisfy increasingly fickle customers?
Machine learning offers one solution, if organizations can overcome their silos enough to implement it correctly. Each new customer action feeds back into the analytics engine, which helps inform the next best steps for a positive customer experience. For example, if a customer indicates through her online browsing habits that she’d prefer an Android phone instead of an iPhone, she’ll immediately start seeing an Android upgrade offer the next time she goes on Facebook. Granted, some customers find it a bit creepy that brands can make their way into personal social media feeds in this way, so it’s important to make it easy for customers to opt out of social media targeting. However, companies can earn customers’ trust simply by being relevant and providing value. Just like in any relationship, a business can earn trust and loyalty by being a good listener and being there for the customer at a point of need. By leveraging automated analytics, customer interactions can fuel a continuous feedback loop that adapts in real time to add value at every touch point.
Consider how Sprint is using data to create better customer experiences. In 2014, Sprint had a customer churn rate of 2.3% — twice as much as its biggest competitors. The company was relying on customer experience agents — who were relying on their own judgement — to comb through data on how to best serve the customer. Previously, the agent would look through more than 20 offers, trying to pick the best one while on the phone with the customer. Sprint knew it needed to get away from relying on its employees to make these split-second decisions. After implementing a data solution from Pegasystems (Disclosure: Pegasystems is a former client of mine), Sprint deployed predictive and self-learning analytics to identify customers at risk of churn and proactively provided personalized retention offers. As a result, Sprint reduced customer churn by 10% to historic lows, while also increasing its net promoter score by 40%, boosting customer upgrades by 8 times, convincing 40% more customers to add a new line, and improving overall customer service agent satisfaction.
Another example comes from Royal Bank of Scotland (RBS). Speaking at Pegaworld this month, Jessica-Lynn Cuthbertson, Head of Data Science and Customer Decisioning, and Christian Nellisen, Group Managing Director of Data and Analytics, presented their story about how they used data to move from a sales-driven culture to being more of a trusted partner for the customer. The company used to focus on aggressive sales goals — specifically, generating 200,000 new credit card customers per month. However, through a new culture and technology strategy, the company pivoted and raised its Net Promoter Score by 18 points. “We want to do the right thing for the customer at every moment,” said Cutherbertson. RBS has 17 million customers, seven brands and eight different customer channels. The company went through a transformation focusing on becoming a more trusted advisor to the customer than a typical bank would be. For example, analytics helped the bank identify customers that were in need of financial advice. Now, when RBS sees a customer that’s continuously overdrawing their bank account, the bank will flag that customer and give them a call to provide financial advice. Cuthbertson said, “We are truly looking out for our customers. It’s about a continuous conversation.”
Data can provide huge insights for companies, but making the most of the big data being generated is no longer possible without the help of machine learning. Artificial intelligence tools can help companies make better data decisions that improve the customer experience in real-time. And using data to drive more personalized customer experiences benefits customers and businesses alike.
A Harvard Business Review Article by Blake Morgan