People love great stories — storytelling is how we relate to each other, compare experiences, and make connections. The more unlikely the outcome, the more we want to believe it. Comeback wins, terrible odds, unlikely redemption—we embrace these concepts because the characters feel so flawed, so human, so very relatable.
Given that, imagine this scenario.
Nan, a single mom, is a customer service rep (CSR) that works for a major wireless carrier. She gets up early every day, washes the sand out of her eyes, and slurps down two cups of coffee (cream, no sugar). Then she piles her kids on the bus and slogs through a 45-minute commute, halfway on auto-pilot the whole time, just hoping that today will be a GOOD day. That’s not too much to ask, right?
She gets to work, puts her headphones on, and takes a deep breath before taking the first call. She needs that moment to get ready because the person on the other end of the line will be confused, frustrated, angry, or maybe even confrontational — they won’t be easy to please. But she is Nan! She’s organized, thoughtful, creative, and a born problem-solver. She’s got this.
Nan has two goals. First, she has to provide great service to keep customers happy and retain their business. That’s priority #1. The organization depends on people like her doing exactly that, but she’s also tasked with cross-selling customers into additional products and services — for example, generating new revenue — which is a totally different animal. Secretly, she has doubts about how well she (or any other rep) can really do that. After all, providing great service is tough enough, but she’s dealing with very frustrated customers, and how do you sell angry people anything?
The problem is that Nan has to struggle mightily for every success, regardless of the customer context. She has to leverage all her knowledge training, experience, and intuition just to make it through each service call. She’s very good at her job, but she’s battling internal process and technology problems (15 different systems, and 40 screens!) piled on top of caller frustration every time she picks up the phone — so most days are hard.
Nan’s situation isn’t unusual in telecommunications, or any other industry. The technologies being used in call centers and other agent-facing channels are often homegrown, with huge capabilities gaps that fail to account for dozens of common journeys and use cases, all of which shifts pressure back onto the agent, asking her to work “outside the box.” In these cases, the technology and support systems just haven’t kept pace with the needs of the modern consumer.
But the world is changing, like in the well-documented case of Sprint where the company implemented a Next Best Action approach to customer engagement, reducing customer churn by more than 50 percent over a short period of time, and resulting in a seismic shift across the US wireless market. It’s important to remember that stories like Sprintnever happen by accident. They’re made up of thousands (sometimes millions) of individual interactions occurring on the front lines every day, each of them orchestrated by someone like Nan, and supported by AI.
Sprint is the third largest wireless carrier in the U.S., with a revenue of $35 billion yearly. It supports 50 million customers overall, 30 million "post-paid" wireless subscribers, 35,000 call center agents (folks like Nan), and 4,500 retail stores. Yes, Sprint is very big, but a few years back, it was also very troubled, losing customers at twice the rate of competitors like Verizon and AT&T. In 2014 their “customer churn” rate was 2.3 percent, then the highest in the industry.
Like many organizations, the quality of the customer care agent's work had a significant impact on Sprint's ability to retain its customers. Juggling multiple systems and screens, and without access to reliable intelligence (like sales and retention recommendations), agents had to use their own judgement and experience to pick the best approach for each situation. This meant parsing through over 20 offers for each frustrated customer, searching for the "right" one while still talking on the phone. Performance levels and customer satisfaction were massively inconsistent from rep to rep.
Customer Dissatisfaction is Everyone's Problem
For the last 30 years, Marketing has generally considered customer retention to be a “Customer Service Problem,” which they could safely ignore, but that’s an outdated concept. Customers are connected and well informed about both your company's and your competitors' offerings. They're empowered and feel entitled to great customer service when they need it — not when brands are ready to provide it. And they hate using the phone.
By the time a customer calls to tell someone like Nan that he has a problem, it’s already too late — that person is halfway out the door and ready to churn, where she’ll be immediately be scooped up by a competitor. Customers only call because: 1) they're completely out of options (and too frustrated to actually listen) or 2) because their promotional rates have expired, and they’re going to demand a discount. This kind of situation is poison: Customers that are unhappy and in pain (ready to churn and trained to seek discounts). Pair that with an organization that has set up an unsustainable service model, and the result is continuously diminishing profit margins.
This situation also puts terrible pressure on agents, making them the last line of defense. Even when Nan does a tremendous job or saves the day, it’s an exception, rather than the rule — we’re setting her up to fail. Employee satisfaction is a major issue — you're going to see too many really great people ready to walk out the door.
Rethinking Your Retention Initiative
Clearly, customer retention initiatives need to focus much further upstream, well before you drop customers and agents into a poisonous situation. An organization has to see every interaction as a tipping point, with only two possible outcomes: either bring that customer closer, or push them away.
First, you need to predict your customers' needs using the data you’ve collected from them — their product interests and channel preferences, pain points, communication cadence, etc. You need to know if people are ready to churn, versus when they’re ready to buy, so you can react in the moment and guide marketers and CSRs towards the right approach for each unique situation. That’s half the battle.
Second, from your very first interaction with an individual, you need to re-shape your approach around them — you need to test what you think you know, and discovering what a great experience really is. You need to examine a customer’s lifetime value, test sales and retention offers, bundle products that match his needs, and work within a personalized budget that will help CSRs like Nan contain discounts. You need to truly know your customer, inside and out, so you both can have a conversation like real people, and you're not treating them like merely a sales opportunity.
Third, customers are constantly changing, so you need to adapt with them, and never let yourself lag behind. When that customer starts a new journey, finishes one, or simply gets stuck somewhere along the way, you need to identify the context of that customer’s situation, pivot your approach, and stay in tune with where they’re going — not just where they’ve been. This is known as Next Best Action. That means shifting between selling, retaining, and servicing at a moment’s notice, making it seamless for the customer.
But in a case like Sprint, when you’re talking about tens of millions of customers and billions of interactions every year, human beings can’t do it alone. That kind of scope requires AI.
Great Experiences Are Balanced
Sustainable, long-term relationships are all about balance — the balance between customer needs (relevant and timely info, delivered in-context) and business objectives (like growth, profitability, and risk management).
Pega’s AI leverages uses predictive analytics and machine learning to understand what will resonate with a customer, and how to use that insight to make your interactions relevant. The AI balances customer and business needs in real time, then recommends exactly how you to interact with that customer, in that unique context. In real-time, the system determines whether to sell, service, or begin retention efforts – and recommends the most appropriate Next Best Action (such as an offer, message, or service action) for that moment. That information is distributed across your channels as it’s needed, whenever and wherever the customer chooses to engage with you. This ensures you'll always “know” them, and their situation – and be relevant, timely, and consistent.
Intelligent Unified Marketing Keeps on Giving
In Sprint’s situation, they not only saw a huge decrease in churn, but also saw an amazing 800 percent (that’s not a typo) increase in upsell / service upgrades after implementing an AI-driven, Next Best Action approach. The corporate stock jumped 27 percent in one day after they announced their results in a July 2016 earnings call, and the results just keep getting better.
BUT perhaps the greatest impact is on Sprint's customer service agents, like Nan, who once faced a nearly impossible situation. Thanks to smart investments by the company, its CSRs are now empowered and they have a lot more good days than they used to:
Customer: “I have a problem.” Nan: “Yes sir, I know exactly how to help you.”