Daisy White of Rdentify on What iGaming Operators Get Wrong About Outsourcing

Daisy White, Director of Operations at Rdentify & Rdentify Connect, leads operational delivery across outsourced customer experience, team management and compliance-sensitive support for iGaming businesses. Rdentify Connect works with operators across areas including VIP management, KYC, payments and player interactions, while helping build scalable support teams around increasingly complex operational demands.

In this CasinoRank Interview, White discusses why traditional outsourcing approaches are becoming less effective as player expectations rise. She explores the operational pressures behind scaling support, the limitations of metrics such as average handling time, and how AI can strengthen rather than replace human agents. White also explains why the future of outsourcing is less about supplying more people and more about helping operators understand and reduce the problems driving customer contact.

How have player expectations across areas like sportsbook and sweepstakes changed what operators now expect from customer support teams? 

Daisy White: A few years ago players probably compared the support experience with other gambling operators. Now they’re comparing it with Amazon, Revolut, Uber and every other digital service they use. That’s changed the benchmark quite a lot.

If somebody has made a deposit, is trying to place a bet or is waiting for a withdrawal, they expect an answer quickly. But speed isn’t much use if the person answering doesn’t understand what they’re talking about.

Sportsbook is a good example. If someone contacts you during a live event, the context can completely change within a few minutes. Sweepstakes has its own terminology, rules and player journeys too.

That’s why I think the old outsourcing model of giving somebody a script and a knowledge base is becoming less effective. You need people who actually understand the product and the environment they’re working in.

And one thing we’ve learned from running teams is that the best agents aren’t necessarily the fastest. Often they’re the ones who know when they need to slow a conversation down and actually understand what’s going on. Getting somebody off chat quickly isn’t much of a win if they come straight back again.

Where do operators most often underestimate the operational strain involved in handing VIP support, player verification and compliance-sensitive interactions to a third-party provider? 

Daisy White: Probably the amount of judgement involved.

On paper a process can look quite simple: check the account, follow the procedure and respond to the player. In reality, there are lots of situations that aren’t completely black and white.

VIP is probably the clearest example. You’re dealing with commercially important customers while still having exactly the same responsible gaming obligations. KYC can be difficult because the player may already be frustrated before they even speak to you. Then you have payments, source of funds, account restrictions and potentially vulnerable customers.

You can’t outsource the responsibility just because you’ve outsourced the interaction.

Another thing operators sometimes underestimate is the management overhead. Outsourcing shouldn’t mean your Head of Support suddenly spends half their week answering questions from the outsourced team. If we’re constantly going back to the operator for basic decisions, we haven’t really taken the problem away.

The external team needs the right access, clear escalation routes and enough understanding of the operation to make good decisions themselves. Then when something genuinely does need escalating, everybody knows why.

Bonus abuse, account verification pressure and high-value player activity can all create sudden spikes in support demand. Which operational challenges are becoming most difficult for support teams to manage consistently across fast-scaling platforms? 

Daisy White: For me, the biggest challenge isn’t always volume. It’s unpredictability.

Handling 10,000 contacts when you’ve planned for 10,000 is one thing. Expecting 3,000 and suddenly getting 10,000 because a payment provider has an issue during a major sporting event is completely different.

And you can’t permanently staff for your worst day. It would be incredibly expensive and most of the time you’d have people sitting there. But if you’re understaffed when something does go wrong, that’s often exactly when some of your most valuable customers are trying to get hold of you.

That’s when quality can disappear quickly. Everyone focuses on getting the queue down, agents start moving too fast, escalations increase and customers contact you again because the first answer didn’t actually solve anything.

Bonus abuse adds another layer. Genuine customers and abusive behaviour can sometimes look quite similar initially. If your controls are too loose, you lose money. If they’re too aggressive, you start putting friction in front of good customers. Neither is a particularly good outcome.

So the challenge isn’t simply adding more people. It’s spotting what’s happening quickly enough to react and having the flexibility to move resource around when you need it. By the time you’ve seen the problem in yesterday’s reporting, you’ve potentially already annoyed a lot of customers.

Which customer support KPI do operators still interpret too narrowly, and which metric gives you the earliest indication that outsourced support is either strengthening or damaging long-term retention? 

Daisy White: Average handling time.

If you told me an outsourced team had reduced handling time by 30%, my first reaction wouldn’t necessarily be that they’d improved. I’d want to know how many of those customers had to come back.

You can reduce handling time very quickly by rushing conversations. It looks fantastic on a dashboard until you realise the same customer has contacted you three times about the same problem.

And that’s not just a support KPI problem. You’ve now paid for three interactions instead of one, the customer is getting more frustrated each time and, if it’s something like a withdrawal, you may be putting a valuable customer at risk.

So I pay a lot of attention to repeat contact and, more importantly, why it’s happening.

We’ve seen situations where something initially looks like an agent performance problem, but when you dig into the conversations it’s actually a customer journey problem creating the same contact over and over again. Coaching the agent won’t fix a broken withdrawal journey or a verification process customers don’t understand.

Sentiment and conversation quality can also give you an early warning. Sometimes you can see frustration building before it starts appearing in the headline retention numbers.

That’s where I think QA becomes much more useful. It shouldn’t just tell you whether somebody followed a script. It should help you understand what is actually going wrong.

Your messaging places strong emphasis on regulated-market support, including UK Gambling Commission-certified teams and responsible gambling protocols. How do you structure escalation and QA processes so agents can move quickly without creating unnecessary regulatory exposure? 

Daisy White: The important thing is making it very clear what an agent can deal with themselves and what needs to be escalated.

You don’t want agents escalating absolutely everything because they’re frightened of making a mistake. That slows everything down, creates more work for the operator and isn’t a great experience for the customer.

But you also don’t want people improvising when something has a genuine regulatory or responsible gambling implication.

We put a lot of emphasis on training, clear escalation paths and then QA once agents are live. And QA shouldn’t just be about finding mistakes. It should tell you where people need more coaching, where processes aren’t clear enough and sometimes where the operator itself needs to change something.

Technology can help with that. Where clients want it, Rdentify’s QA technology can analyse interactions at much greater scale and flag areas that might deserve attention. But it’s not a requirement for using a Rdentify Connect team. We work with the systems and processes our clients already have as well.

There still needs to be human judgement. Particularly around responsible gambling, context matters enormously.

Rdentify has spoken about AI and NLP improving efficiency, personalisation and multilingual support. In day-to-day player support operations, where has automation delivered the clearest improvements so far, and where do human agents still consistently outperform AI-driven workflows? 

Daisy White: Automation is brilliant at repetitive work.

Finding information, answering straightforward questions, summarising conversations, categorising contacts and helping agents get to the right information more quickly are all areas where we’ve seen genuine benefits.

The same applies to QA. Technology means you can analyse far more interactions than a human QA team could realistically listen to or read manually.

But I think the industry sometimes jumps from “AI can do this really well” to “therefore we don’t need people anymore.” I don’t think that’s where we are.

If somebody is angry about a withdrawal, confused about verification or potentially showing signs of vulnerability, a conversation can change very quickly. Good agents understand tone, history and nuance. They also know when something just doesn’t feel quite right.

So I don’t really see it as AI versus people. The best use of AI is removing repetitive work and giving agents better information, so humans can spend more time on the conversations where being human actually matters.

As AI, regulation and player expectations continue evolving simultaneously, where do you want Rdentify Connect to position itself within the iGaming support ecosystem over the next few years?

Daisy White: I don’t think the future of outsourcing is simply supplying more people.

Traditional outsourcing has a slightly strange incentive when you think about it. More customer contacts generally means more agents, which means more revenue for the outsourcer. We’d rather help an operator understand why those contacts are happening and, where possible, remove the reason for them.

If technology or a process change can prevent thousands of unnecessary contacts, I think that’s a good outcome even if it means fewer conversations for our team to handle.

Operators also don’t always know what they’ll need six months from now. They might be entering a new market, launching a new product or simply growing much faster than expected. Building an internal team around that uncertainty can be difficult. Part of our job is giving them the ability to add capability without having to recruit and build everything themselves.

That doesn’t mean every client needs to buy a technology stack from us. Rdentify Connect can provide specialist teams that work within an operator’s existing systems, and that’s an important part of what we do.

But being part of the wider Rdentify group means we can go further where clients want us to. We have the people, but we also understand QA, analytics, automation and AI. What we use really depends on what the operator actually needs.

I think over the next few years more of the simple interactions will inevitably be automated. That actually makes the remaining human interactions more important because they’ll tend to be the complicated, sensitive or high-value ones.

Ultimately I want operators to see us as part of their operation rather than an external call centre. If we’re doing our job properly, we’re not just answering customer contacts. We’re helping them understand why customers are contacting them in the first place, where they’re getting frustrated and what we can do together to make the whole operation better.