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Peak Case Study

Helping a fashion brand introduce AI, and scale up in 5 days to manage unprecedented demand.

Issue

Our client had already ramped up their customer service for the golden quarter, however they were getting an increasing number of customer enquiries due to third-party delivery service issues.

During the golden quarter of 2022 almost all delivery carriers were facing unprecedented volumes of parcels, due to pent up demand from Covid.

This was further fuelled by Royal Mail strikes, which after failed negotiations, went ahead for 4 days over Black Friday and Cyber Monday. Then for a further 6 days in December, in the lead-up to Christmas.

Approach & Analysis

The contact volumes doubled already-high peak levels. The Taskaler team identified they needed to:

  • Help the client scale up their customer service team to handle the enquiries, as an immediate priority.
  • Further help the client to improve their customer experience.
  • Take proactive steps to ensure the client was better prepared for any future unexpected demand.

Solution

Taskaler handled the additional volume by: 

Scaling up and careful resource management.

Newly deployed staff were directed into queues which were easy to handle responses, whilst existing more experienced staff were moved into queues that were more complex. 

Recommendation for Conversational AI.

Rather than simply increase the number of Taskaler FTE required for Peak, we recommended the client adopt conversational AI to support webchat, to deal with the simplest / routine enquiries.

Implementing AI for Webchat.

We scoped the market for a suitable supplier, reviewed root causes and established contacts such as WISMOs (Where Is My Order), that could be deflected away from agents into conversational AI. 

Impact

Within just 5 days we had scaled up the team to deal with the immediate increased enquiry volumes.

CSAT levels maintained before there was a discernible drop.

AI was in place to support future peaks. The Taskaler team had helped to:

  • Map out user journeys and worked with the team to come up with responses to the journeys.
  • Thoroughly test the AI and phase its release.
  • Drive continuous improvement through QA, robust feedback loops and working with the client, technology partner to help improve/train the machine learning.