These inefficiencies lead to four core problems or, as I like to refer to them, the Four Horsemen of the Retention Apocalypse. True, they might not sound quite as cataclysmic as the original four horsemen, but these issues present serious (even fatal) concerns for customer-facing organizations, particularly as they scale.

Today, I’m going to clearly define these four common pitfalls and then share ideas about how to address them. Common to all of these threats is the inability to make use of the vast amount of customer data that every company has at its fingertips. By leveraging a combination of artificial intelligence and machine learning, companies could stave off these threats and prevent an apocalyptic collapse caused by failed customer management, at scale.

But more on that later. First, let’s meet our horsemen.

Introducing the Four Horsemen

Horseman #1: Failure to be able to identify opportunities for growth

As companies grow, they often miss out on easy opportunities to build revenue from the existing customer base and instead invest excessive resources into looking for new customers. Every company has low hanging fruit in the form of predictable renewals and cross sell/up sell opportunities; too few companies have a systematic approach to knowing which accounts to target or when to engage. To capture this fruit, a company needs a way to identify which customers are ripe for an upsell and when they’re most likely to opt in.

How can you address this?

Your customers are constantly interacting with you, but because of the sheer volume of messages, too often it all sounds like noise. In their communications on Help tickets, Slack channels and other platforms, customers are providing clues that indicate how satisfied they are and show their interest in upsell/cross-sell. In order to identify these clients, you need an AI-driven tool that can filter out the noise. As an example, a customer might be complaining about a specific pain point that your Premium plan solves. Let them know about it, and they’re likely to upgrade.

You also want a system in place to generate meaningful health scores for each client. These scores can also help you know which accounts need nurturing and which ones are satisfied. Technology helps you find the needle in the haystack.

Horseman #2: Losing strategically important customers

Businesses often invest an enormous amount of energy into finding new customers and growing their base; in fact it’s established orthodoxy that acquiring new customers can cost 4 to 5 times more than retaining current ones. By focusing on new growth, however, companies may neglect existing accounts and end up churning great customers. Consider that, according to the Harvard Business Review, a 5% increase in customer retention rates increases profits by 25% to 95%.

As CS reps juggle a growing customer base, it can be hard to know how to best nurture each account, and even harder to pick up on signs that a customer is at risk. Instead, CS teams are often forced to be responsive, constantly putting out fires rather than proactively managing accounts. Companies need a reliable and systematic way to prioritize existing accounts and give the attention needed to prevent churn before the fires start—particularly when it comes to important (usually larger) customers.

The flipside of this is worth a mention, too. Sometimes CS teams invest too much effort in the wrong accounts and there are definitely cases where strategic churn has a net-positive effect. Eliminating unprofitable (and often demanding) accounts is not only good for the bottom line; it can also boost the morale of your CS teams.

How can you address this?

Take the time to clearly map out risk factors that can precede churn. Then, make a list of your top strategic and VIP accounts. Using this information, you can build an AI-driven prioritization mechanism to help ensure that CS reps catch risk indicators on time, particularly among your most important accounts.

Horseman #3: Missing out on valuable product insight and development opportunities

We all have heard Henry Ford’s quote about customer input and faster horses, however, there’s no doubt that customers have an integral role to play in influencing product development and strategic roadmaps. At scale, faced with an ocean of unstructured customer correspondence, it is hard to understand what your customers actually think about your product. This is particularly true for companies with a self-service product and limited direct interaction with their customers.

Do you know what your customers most want to see in your product? Do you know what drives them crazy? As you scale, it can be remarkably easy to lose touch with customers and their needs. But knowing how to focus your product development depends upon understanding them. If you’re out of touch with your base, you can miss out on countless opportunities.

How can you address this?

Technology is essential to preventing this failure. With so much contextual data created by a growing customer base, you need to implement an engine that can track features names, negative sentiment and product issues across your different textual channels. Then, you want an automated way to send the relevant feedback to the relevant PM at the relevant time. As we speak to prospective customers, we see time and again that this is one of the key problems that LoudNClear can solve for.

Horseman #4: Poor customer experience

With high ticket volume and endless textual input, it can be hard to stay on top of customer support and even harder to know when a customer really needs to hear from you - fast. This can quickly cause customers to feel like they are not being heard at all. Or, that despite what they’re paying each month, they’re just one of thousands of customers, not special to nor valued by your company.

A growing customer base can quickly lead to long response times and support that isn’t differentiated or tailored in any way. If your CS team is managing tickets manually, there is no way to provide each and every client with the answers they need, when they need them. And worse, there’s no way to know where to focus efforts and how to prioritize accounts. The cost of poor CS is high; with a click, frustrated customers can and will migrate.

How can this be addressed?

AI-driven auto-labeling and prioritization can help you quickly classify and label customer requests and concerns, even at scale. This solves countless issues and provides a seamless, efficient and far more accurate customer experience. Your CS reps no longer have to review each and every ticket, saving time and reducing human error. You can customize the labeling to fit your company’s business needs and KPIs, choosing which types of requests to prioritize and flag, and directing them to the relevant team member.

Staving off the Horsemen: Finding Solutions

While there is no one-size-fits-all answer to keeping these four horsemen at bay, there are ways to successfully navigate these issues and find solutions - particularly with the help of technology.

Tools like ZenDesk, Salesforce and even Facebook are goldmines of client data. Buried within them is valuable contextual information that can help companies understand their clients, prevent churn, increase upsells and know where to focus development efforts.

A tool like LoudNClear offers a powerful way to combat all four of the horsemen. It helps companies synthesize data to make it both accessible and usable. LNC analyzes the mass of data and flags the information that matters - to the relevant stakeholder. It offers a way to superpower your product and CS team, arming them with information to help them prioritize customers and manage their time wisely.

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