03 Apr

Major high street bank uses PrediCX to improve customer loyalty

A major UK bank was looking to improve its customer loyalty. It was already using the latest analytical tools including social listening, sentiment analysis and a large data science team but they were experiencing limitations and not making enough progress.

The bank was keen to find more opportunities to improve customer loyalty and reduce their operational costs by gaining more customer insight. They were also interested to see what online feedback their main competitors were receiving.

The bank invited Warwick Analytics to carry out analysis using the automated text analytics platform PrediCX.

Some key outcomes from the analysis included:
• £200m per annum of churn mitigation was identified through operational opportunities
• £240m per annum of churn mitigation was identified through CX opportunities
• Customer Service savings opportunities of £20m per annum through automation
• Ongoing tactical opportunities identified for marketing effectiveness and propensity

PrediCX analysed Tweets associated with the bank, and 23 of their competitors, over a 3 month period. Sentiment Analysis can present issues with accuracy, context and multiple sentiments. PrediCX overcomes these by using the latest in machine learning and AI to classify by ‘concepts’ instead of ‘key words’. The result is a highly tuned model, built in a single day, with thousands of classes that pick up signals of issues, customer sentiment and more importantly customer intents.

A number of key recommendations for the bank were identified just by this analysis alone:

  • Estimated 20% of churn is caused by incidents and this represents maximum opportunity.
  • 10% improvement is c. £200m pa revenue compounded yearly
  • Comparable best-in-class churn e.g. Nationwide is 25% lower.
  • Potentially a further 10% i.e. c. £200m opportunity with improvements
  • Online and mobile banking is a key issue, and is causing direct churn
  • Sentiment is middling. It does not appear to correlate to churn for the market
  • Drivers of churn are mostly customer service, branch closures, marketing offers, interest rates and vulnerability issues
  • Early warning can help predict churn tactically and intercept likely churners
  • 28% of Tweets and potentially all non-voice queries can be automated. This could be £20m pa saving
  • Business banking, current accounts and ancillary services have the highest churn, and insurance the highest negative advocacy
  • Mortgages, current accounts, savings and overdrafts cause the most attritional set-up.
  • Opportunity to improve the journey
  • There are distinct patterns and opportunities to change customer services planning over the week and day to reduce churn and costs

It’s clear to see how the adoption of the latest machine learning for text analytics can present far more insight, a much deeper level, than traditional sentiment analysis and text analytics. More significantly it is able to correlate these signals to operational issues to reduce costs and improve customer loyalty.

With PrediCX, this level of insight can be set up in a matter of days, delivered in near real time and without the need for a data scientist to maintain the model.

You can download the full case study here.

Major high street bank uses the latest AI text analytics to improve customer loyalty
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