Models they’ve used:
Topic Classification Model
Classifies text by topic.
Archer is a technology-enabled financial services company that simplifies operations for investment managers, who are freed to focus on managing investments and growing their business.
Archer connects with investment managers’ trade counterparties, custodians, administrators, agents, and authorized participants, to provide exacting reconciliation, smooth client onboarding, and effective trading and trade settlement.
Archer utilizes Zendesk as its primary tool for communicating with investment managers and their business partners. The firm receives a high volume of support tickets every day, many of which are time sensitive, including live trading requests, cash requests, and account maintenance requests.
Alyssa Wilkowski, Senior Programmer Analyst at Archer said:
"The breadth and complexity of topics we address is vast, and our team members are deeply specialized. We are most effective when requests are routed to the team most skilled to meet the requestor’s need, so quarterbacking is a key aspect of our service delivery and contributes directly to the timeliness of our responses and – as a result – to our customer satisfaction.”
Archer is among the Inc. 5000 fastest growing companies in America. The number of investment managers they serve, the dollar amount of assets they administer, and the breadth of investment types they service are quickly expanding.
Alyssa said: “To efficiently absorb our fast-increasing volumes, we required automations to stand in as quarterback of our tickets, to file but take no action on others, and to recognize and escalate requests for which we are contractually obligated to meet specified service levels.”
To help their ever-increasing workload, Archer needed:
An automated solution to process large ticket volumes.
Speed. Time-sensitive requests required responsiveness within forty-five minutes or less, so automated and immediate routing of requests to the appropriate team members was needed.
Accuracy. It was Archer’s goal to virtually eliminate the need for human intervention in the tagging and routing process in order to maintain high levels of customer satisfaction. Because of the breadth and complexity of Archer’s tickets, a customizable solution was needed.
Archer chose to work with MonkeyLearn to help with efficiency as they grow.
The team at Archer leveraged the MonkeyLearn topic classification model. After an extended feedback period, during which Archer’s agents evaluated and “taught” Monkeylearn more about the company’s practices, the classifier’s functionality was fine-tuned to Archer’s needs.
Now, tickets are automatically routed to the right team, tagged with the correct group and action required, and quickly addressed by Archer’s busy agents.
“Most importantly, our service managers are freed from most quarterbacking work, so they are available to help agents to respond to complex matters,” said Alyssa.
MonkeyLearn helped Archer efficiently handle a surge in ticket volume by:
Routing tickets to the right agents immediately, improving initial response time by 65%
Pre-tagging tickets, reducing the administrative burden on agents so each agent could take on 20% higher ticket volume
Ensuring service managers were more available to train and support agents, improving customer – and employee – satisfaction
“MonkeyLearn helped us to grow efficiently as our business expanded, ensuring that our clients’ needs were quickly addressed while our agents’ expertise was fully leveraged.”
Robert G. Lage