How we analysed the pain points of one of the UK's largest clothing retailers
With consumers heading online in swathes, and 62% of them using multiple channels before making a transaction - retailers across the UK are realising that with the growing expectations of good customer experience (up 59% compared to just one year ago), it is no longer good enough to focus efforts on one channel alone.
The consequences of getting things wrong can be high, too. With UK online retail sales worth around £120 billion in 2021, organisations can't afford to get this wrong.
Listening to customers to identify pain points, improve data-led decision-making and understand how you perform around different demand types and channels used to be tough, but TMAC's leading AI-powered Conversation Analytics platform makes it easy to analyse and understand customers wants, needs and frustrations.
As part of our set up, we helped analyse the omnichannel landscape for a UK leading clothing retailer and identified £100,000 in immediate cost saving and revenue increase opportunities.
Our client was ready to embrace digital transformation and drive a seamless CX, one that their customers were used to through partner retail stores. In order to garner more loyalty, differentiate themselves from the competition, and better align their distribution to their target age group - it was strategically paramount that the business start selling direct-to-customer.
Quickly though, they spotted that their webchat and email produced high levels of escalation, eating into handling time (AHT) efficiencies and eroding revenues. All whilst believing that the simple demand deflection that these channels provide would in fact drive down costs and improve CX.
Data teams were dealing with BAU work, and they needed expert help to understand the role that these channels play within a wider omnichannel experience.
So, they came to TMAC (where else?) and asked us to help.
In just a few weeks, we conducted ad-hoc analysis on the clients first party data across all text channels, powered by our Conversation Analytics platform.
Using complex AI-driven language models, we were able to cater understanding according the context - clothing - and get an accurate read of sentiment and intended meaning. From there, we could analyse relationships between clusters and understand drivers of poor sentiment which ultimately encouraged refunds, returns and lost revenue.
By using our proprietary platform, we were able to circumnavigate the need of sampling and analyse every piece of the data provided.
We identified 8 areas of improvement:
These problems aren't unusual, either. As consumers we interact with companies everyday and often encounter poor CX first-hand. If you'd like to develop an omnichannel approach to your CX, get in touch with us today and see what we can do for you.
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