16 Apr

Warwick Analytics partner with S4RB to enable grocery retailers to utilise machine learning and AI

A new partnership will enable grocery retailers to utilise machine learning and AI to generate detailed analysis about the root causes of customer sentiment, emotion and intent as well as the reasons for customer churn.

Grocery retail technology specialist, Solutions for Retail Brands (S4RB) has partnered with Warwick Analytics to integrate PrediCX natural language processing into its Affinitysolution, offering a higher level of insight than existing sentiment analysis solutions.

Warwick Analytics’ technology is based on highly accurate machine learning models that can be applied to textual data such as customer feedback, complaints, calls, CRM notes and chat, helping to optimise the customer experience, identify root causes of churn and loyalty, as well as emotional causes of purchasing decisions.

Sentiment analysis is used widely in the grocery sector, but studies have shown that its accuracy can be as low as 58 per cent. Furthermore it typically misses detailed signals such as specific nuances within customer concerns, plus emotional intents such as signifying churn, loyalty, repeated issues and customer effort – which become ‘unknown unknowns’, or unidentified risks for retailers.

By contrast, PrediCX can accurately and automatically pick up these subtle, actionable signals. The partnership allows S4RB to combine the unique PrediCX technology (patent pending) with S4RB domain expertise to create a machine learning model specific to grocery retail. The model is continuously optimised by bringing in a ‘human in the loop’, which can be a non-data scientist, to validate anything it might not be sure about and then trains and updates itself with the input.

In creating an industry specific mode for the grocery retail market, it has been able to accurately determine the root causes for dropped baskets; churn from particular stores, products or grocery brands; recommendations for future products, packaging, recipes and store layout; and early warning of previously unforeseen quality issues.

This technology will be available for retailers on S4RB’s Affinity platform, which enables retailers to access a complete view of product performance alongside other product and supplier KPI, also sharing this information with own brand suppliers to enable retail teams and suppliers to collaborate more effectively on winning products and brands.

James Butcher, CEO of S4RB, said:

“We see so much data coming into retailers from customers and other sources. Without proper categorisation we are only able to realise a fraction of the value that it brings for our retail customers. PrediCX enables us to unlock that value with consistent and specific labeling and brings exponentially more value to clients and their customers.”

Dan Somers CEO of Warwick Analytics adds:

“The success of machine learning models is enhanced by making it industry, and indeed customer and situation specific. This is why we’re so excited to be working with S4RB who bring deep retail domain expertise.

“S4RB can use our PrediCX technology integrated with Affinity to identify customer intent and insight to both the brand experience and also for the first time to product insight and the root causes for dropped baskets. No other analytical product was able to do this in a blind test.”

For more information about Solution for Retail Brands’ Affinity platform, please visit: https://info.s4rb.com/affinity

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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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