P.V. Kannan, Co-founder and CEO
You know what I’m talking about. If you have a problem with your cable service, the only way to resolve it is to call the company. You state your account information, tell the agent your problem and more often than not, you’re transferred to a supervisor who makes you repeat everything you just said.
Imagine if you could resolve the same issue online and the company could actually predict why you were contacting them. “Hello PV, I see there’s a problem with your cable set top box. Please choose one of these available times for a visit from a technician.” That’s a completely different experience. While that’s hard to imagine, it’s perfectly doable with today’s technology.
Businesses already have a tremendous amount of data about their customers—what they’ve bought, previous customer service issues and how they like to communicate—yet many businesses leave that information untapped. Customers languish in phone trees, repeat information, and experience unconnected channels and impersonal interactions. That may have been acceptable a few years ago, but Not in the Age of Intent. Customer expectations are rising and so are the businesses that harness the data to meet those expectations.
Data analytics has proven to be an important way for businesses to glean insight into the patterns, preferences and behaviors of customers. But while many businesses use this data to inform product roadmaps, promotional campaigns, website design and more, very few are putting this data to use in a way that actually improves the customer experience.
Consumers are willing to share data—but they need something in return
Netflix is a prime example of customer data being put to good use—every time a consumer watches a show or leaves a rating, they are essentially sharing their data. But the benefit is easy to see: better, more relevant recommendations. Amazon is another example of a company that uses data to create a better experience—when you go to make a purchase, you can see products that might appeal to you based on your history, or what others with similar interests have also purchased. But the benefits of data-sharing aren’t always quite so clear for consumers. In fact, in a recent survey of more than 1,000 U.S. consumers we found that a significant number of consumers expect promotions and discounts (43 percent) and expedited customer service (39 percent) in return for sharing personal data.
The challenge for businesses is to ensure they are making the best use of data by using it to not simply understand consumer behaviors, but to enhance the overall customer experience. The best way to approach creating a great customer experience is to start with the data you already have, and use it to understand consumer intent. For example, if a customer calls with an issue after unsuccessfully trying to self-serve for a resolution online, they should not be greeted by another self-service menu on the IVR, but rather, a live agent who can see and understand their previous actions. That agent should have easy access to that customer’s purchase history or the customer’s key identifying information—to ensure speedy and accurate resolution. The agent should never have to ask the customer to repeat information that the company already has.
Personalization forges trust and loyalty
Today's technology allows companies to access vast amounts of data about customers, but not all companies are putting this to use in a way that drives customer loyalty. With big data, machine learning and artificial intelligence becoming more commonplace, retailers can now drill down on consumer personas, engagement behaviors and preferences, all of which help ensure messages resonate with consumers.
Companies have the opportunity to be more proactive about the customer experience as well. Rather than analyzing data to simply understand how a consumer has behaved in the past, companies can now focus on prediction to better determine customer intent—in other words, what is this customer trying to accomplish? To be truly successful, this needs to happen in real-time, and across any channel. Some leading retail brands are already using data to accurately predict consumer behavior and to improve customer experience. The most successful companies will be those that tap into the power of AI and machine learning to enhance their customer acquisition and customer engagement efforts across all touchpoints.
Delivering a personalized, predictive and effortless customer experience
If your customers buy into your value proposition, then you can predict what a customer is trying to do. Forward-thinking businesses are bringing together vast amounts of data, using data science, artificial intelligence and machine learning to develop predictive models. These businesses can understand, anticipate and act on consumer intent across all channels to create a personalized customer experience that drives improved satisfaction and increased revenues.
Here are a few simple tips for businesses that want to provide a better experience:
Use their phone number—If you ask a customer whether you can remember his/her phone number to enable a better experience, the vast majority of the time, they say yes. Companies can easily associate phone numbers with previous customer journeys to personalize an experience and better predict consumer intent in real time. For example, when I call Avis about a car rental, they can say “Hi PV, it’s nice to have you back. Would you like to change your reservation or make a new one?” If I’ve returned the car and call them back within a couple of hours, odds are I left something in the car. They know that. When I tell the story, it’s fairly obvious, but that’s not how most companies think about service.
Stay one step ahead—If you look at free services like Facebook, LinkedIn or Google, you’ll see a lot of what I’m talking about is already built into the experience. Those companies are always one step ahead of the consumer. Over a period of time, the systems become smarter. These companies often say “Hey—do you know this guy who went to school with you?”
Think about how to solve the customer’s issue in the fewest steps possible—If a customer starts a journey on the web, chances are they want to stay on the web, not pick up the phone. Keep customers in the channel they prefer. This not only reduces costs, but it reduces frustration as well. Furthermore, it allows businesses to mine all interactions to continuously improve the customer experience. This can be done across both structured and unstructured data. Transcripts can be analyzed, and predictions verified to create a feedback loop for training your machine learning models.
I hope that someday, in the not-to-distant future, I’ll be able to write about the fantastic experience that I’ve just had with my cable company.
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