Many of you reading this will have gained experience working many positions before finding your current role. You may recall those lists of names and numbers that can be the dread of sales and retention shifts. Of course, some people are better suited to the task than others, but everyone knows it’s a pretty hit and miss affair.
You call the first number on your list, no response. You call the next one, again no answer. When you do finally get through to an actual person, they’re normally upset by the unsolicited contact and the inconvenience of having to talk to a stranger. After all, who’s ever happy to deal with random callers?
According to Or Gold, Head of Commercial Operations at Panda Trading Systems, these are the kinds of frictions the company works on addressing with each new product launch.
“Okay, you’re loving the new Web Trader and CRM, that’s great. Your dealers are making the most out of the plugins, fantastic. But what else can we do to optimise the way you operate and really give you an edge. This is where we can add value in our opinion.”
Or is referring to a string of recent updates the company has brought to its core product line. In March they announced an AI-powered document verification module for their flagship CRM that eases the burden of low quality, repetitive tasks for back-office staff and increases verification throughput.
More recently, the company introduced Next Call AI, a new CRM module that provides sales and retention teams with prioritised call lists generated by client actions. The algorithm compiles and prioritises call lists based on certain event triggers that are customisable. These include when a client registers, logs in, finds themselves on the deposit page, has had their card declined, successfully deposits, makes a withdrawal request, has a withdrawal request approved, and much more.
“Our test groups of brokers using Next Call report that re-deposits have increased by around 27% since prioritising their call lists via the module. Brokers have also seen an increase in successful outgoing calls of 20%, with effective calls increasing by 17%. So far, the module has improved agent performance by, on average, 13%. These are just preliminary results from data gathered over a two-month period, but we believe these numbers are a baseline that can be improved upon. Especially as the algorithm receives further training and we see brokers getting more granular about the different combinations of events they want to use to pinpoint what clients need help with most.”
An example of a real use case would be to have the new system prioritise rejected deposits, or when account balances fall below a certain threshold. This allows the correct member of staff to get in touch and offer help and advice.
Alternatively, it can be set to offer proactive customer support, such as when a prospect is stuck on the registration page, or to inform a client that a withdrawal has been approved, or even to help a new registration find their bearings and understand how the platform works.
Brokers can prioritise which events the system focuses on, allowing the machine learning algorithm to continue serving sales and retention teams with the optimal next call suggestions.
By allowing this actionable information to flow between departments, issues that lead to clients stuck in call or live chat queues can be avoided. According to Gold, this data-driven approach has become a top priority for Panda in recent years.
“For us it’s not just about getting these systems into brokerages, it’s about what we can continue to do with them once they’re in place. The message we’re always trying to share with our brokers is that they have to think about how to harness the enormous amounts of data they generate daily. In our view, it can be used to optimise everything they do.”
While offering a suite of off-the-shelf brokerage systems, Panda has made its name over the years as a go-to for custom developments and complex implementations. Gold says that the approach has earned the company loyal clients who appreciate the personal touch and are often willing to test new products under live conditions.
“It’s all about collaborating in ways that benefit everyone involved. The fact that we have this ongoing relationship with the brands we service allows us to learn all about the frictions and bottlenecks they experience at different stages of their growth. It has been hugely instructive for us and inspires where our various teams focus their attention.”
But it’s about more than just addressing recurring client issues. He explains that once a broker implements systems that are data-driven, their own inter-departmental systems become smarter.
“Quite often we think of these departments as set in stone, each with its own pre-defined role, working as it always has. But if the data shows you can reliably increase re-deposits by taking certain steps, then naturally the department gets smarter as a whole. But it can also inform the workflow of how different departments and members of staff collaborate. Maybe a member of staff from another department is required to record a quick platform tour that is automatically sent from marketing when a set of events get triggered. It all depends on how deep you want to go.”
The close relationship that Panda has developed with its brokers over the years has also led to a situation where they can receive feedback on just how well these products are performing.
Or told us that Panda TS looks forward to debuting the module more broadly and has promised to update us on its ongoing performance as the data continue to come in.
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