With acquisition costs soaring, losing a customer due to poor support has never been more costly. Operators need to find new ways to place customer support at the heart of their retention strategy, says Rdentify CEO Daniel Brookes. The following text is written by Brookes.
It’s no secret that in most regulated markets, the cost of acquiring a new player continues to increase.
These rising CPAs, coupled with tighter restrictions on how operators can bonus and conduct their marketing, mean retaining a player has never been more important.
Yet when I speak to operators, their customer support function is rarely seen as a particularly central part of this equation. I’d argue the opposite. To truly engage – and in turn, retain – your customer base, you need to be thinking carefully about how you provide support.
Simply put, losing a customer due to bad support now represents not only a loss in potential revenue, but also a wasted acquisition cost that is increasingly difficult to recover.
Sub-par support
The challenges of delivering great support are not new, but in many ways they are becoming even greater. Customers now expect real-time responses and personalised solutions to their issues. It doesn’t take much to send them off to a competitor, whether it’s a slow response or a drawn out process.
The problem is magnified in the iGaming space, where players are often dealing with financial matters or relatively complex topics around the terms and conditions of various bonuses or promotions.
Over the last few years, artificial intelligence has been portrayed as something of a silver bullet. However, I feel that at this point in time, AI is a vital tool in your customer support arsenal, not a foolproof solution.
We aren’t yet in a position where AI can deliver the level of service of a fantastic agent. But a smart use of AI certainly can support that agent and make them faster and more productive.
Equally as importantly, it can also help you better understand what your customer support team is doing well, and where it can improve. A poorly trained agent can be the difference between a great support experience and one that’s enough to send the player packing.
It’s never been easy to QA CS agents, and traditional resolution metrics often only tell a small part of the story.
One really interesting use case here is using AI, via a platform like Rdentify, to analyse conversations between customers and agents, and then process these into actionable insights that give you a clearer idea of how your agents – and the team as a whole – are performing. From here, it becomes easier than ever to upskill your team in the areas it most needs.
Another area is making your support more proactive. The traditional approach is to wait for customers to come to your agents with a problem.
But we’re finding that using AI to send out automated messages at proven pain points or when a player is showing signs of frustration allows your team to intervene early with assistance before things escalate.
This type of anticipatory support, when properly executed, almost feels like magic, and lets the player know you are paying attention.
Capturing the voice of the customer
While delivering better customer support has obvious benefits around player retention, there’s an even bigger prize at stake.
It may sound obvious, but your customer support function is the part of your business in most direct contact with your customers.
But rarely do I see operators using the feedback they receive via support agents to inform the broader strategy of their business.
Too many treat the resolution of a ticket as an end point; I’d argue it really should be a start for proactively solving the issues customers are regularly facing.
By using an LLM to analyse all your support requests, it becomes far easier to quickly understand the most pressing concerns of your user base.
There’s also a huge opportunity in using CS interactions to determine the voice of your customers, what they love about your brand and product, and the area where they feel you could improve.
AI-driven LLMs can pick up on customer sentiment and keywords to paint an accurate picture of what your customers think about your brand. Remember, these conversations are often the best window into how your customers perceive you.
Not taking the time to properly analyse them with the tools now available leaves a lot of value on the table when it comes to letting your customers tell you how you can improve.
Ultimately what customer support should be about, because it’s not just every bet that counts, it’s also every conversation.