- 30 employees
- 3 Shopify Plus stores
- Consumer electronics
Dealeaz is one of the largest e-commerce sites in Canada. They operate three Shopify Plus stores, from consumer electronics to fitness.
A few months ago, they decided to profoundly improve the quality of their customer service and decided to bet on Facebook Messenger as their primary support channel.
Transitioning from email to Facebook Messenger support
The goal was to transition from email support to more instantaneous interactions. They added a Messenger chat widget on Bodeaz, one of their stores, and implemented the Shopify Facebook bot, which sends shipping notifications to customers.
Though, this quickly became a challenge. The bot was continually opening tickets in Zendesk for each bot conversations. As a result, every morning, their eight people support team was spending hours closing out those tickets. On top of this, the number of conversations increased by 35% and the team had a hard time keeping up with the pace. Response time was growing, and customer satisfaction suffered.
Phil, the CFO of the company, decided to take action. He started looking for ways to increase the productivity of the team. He tested a few solutions and ended up giving Gorgias a try, by recommendation of his Merchant Success Manager at Shopify Plus.
The right combo of bot & human support
Phil’s team moved to Gorgias last July, during the sales peak period. The immediate benefit was that the team no longer had to close Facebook conversations. When the customer only interacted with the bot without leaving a message for the support team, it didn't open a ticket.
On top of that, the Shopify orders are now automatically pulled up next to Messenger conversations. This way, the support team can see a holistic view of the customer, with their order history, loyalty points, or past discussions.
Though, most of the questions were still pretty similar. The team started to create rules to respond to the most basic questions automatically. For example, delivery questions were instantly answered with the delivery status, or return requests with the return procedure.
As a result of this, 30% of the tickets were responded automatically with personalized responses. But Phil’s team didn’t stop there. They created some personalized macros, which used as much customer info as possible. For instance, the refund macro mentions the credit card last four numbers on which the money is going to be refunded. This way, the experience ends up feeling personalized for the customer.
Overall, the average response time dropped from 24 hours before the switch, to 15 minutes today. As a result of the efficiency gains, the team reduced the amount of time spent answering support tickets by 30%.