09 Oct

Using PrediCX to reduce Helpdesk Costs while Improving Service Levels

In a large company an internal helpdesk can be as large and complex as any external CRM and keeping down costs whilst keeping up service levels are high priorities.

 

How PrediCX was applied to ServiceNow data

 

The customer was an enterprise providing software and consultancy with around 5,000 employees and around 30,000 tickets per year. Warwick Analytics applied its PrediCX software to the helpdesk tickets from ServiceNow (it can also work with BMC Remedy, Zendesk, Salesforce and others). After a short training period of a couple of days, PrediCX was already classifying unstructured data i.e. the text notes within the tickets as well as the notes of the solutions and corrective actions. There were several use cases:
Early warning of issues and hints for root causes to minimise risk and lost productivity
• Root causes of common issues to obviate tickets and cost, and improve service levels
• Identify opportunities for automation and self-serve both direct and to support agents
• Automated, accurate classification

The view was both retrospective and forward-looking, i.e. to identify the opportunities it could have saved in the past had it been implemented at the time, as well as identifying opportunities going forward.

What PrediCX found

 

  • The support teams spend much of their time dealing with these situations rather than enhancing the service. Whilst there will always be emergency situations, the opportunities are to spot the common issues as quickly as possible with alerts, as well as identifying the root causes from the notes of investigated tickets. This can isolate the relevant failure mode quickly and hint at the corrective action required, as well as identifying whether the issue is new or a repeat of something in the past. This is compared to the alternative of manually classification which does not pick out the rich detail of the ticket symptoms (or solutions) and is often inconsistent and inaccurate.
  • Early warning of payroll issues – The insight allows managers to quickly see when certain failure modes are reappearing e.g. the Expense error FM2 which reoccurred from the first incident on 16 March 2018 and reappeared on 4 May 2018. If PrediCX had been used at the time, it would have helped to implement a permanent fix during the first incident. It also would have shrunk the time of impact of issues by providing the earliest warning of an issue and hints at root causes e.g. the Submission error FM2 from 4 March 2018 to 25 March 2018. PrediCX can help obviate future failure modes and facilitate projects to implement preventative and corrective actions that can be executed ‘offline’ without disrupting service levels.
  • Hidden laptop issues – the analysis revealed a driver incompatibility issue that took 9 months to resolve. With PrediCX, it’s easy to see that it correlates with a particular Windows error and memory error too. Alert triggers within PrediCX would have picked this up.
  • Opportunities for automation and deflection – PrediCX looked at tickets over a period of 14.5 months and provided an analysis of whether issues are to do with staff growth and activity, wear & tear (for hardware), a repeating issue which can be deflected (i.e. estimates based on common root causes), a repeated issue which can’t be deflected (i.e. where no common root causes) and issues which appear to be non-repeating. These hint at the potential opportunities for deflection and automation. It shows that 25% of the total tickets analysed
    could have been deflected and 29% could be automated to some degree. Given there are about 40,000 tickets per annum and based on a typical cost of solving a ticket, it is estimated that PrediCX identified savings of around a third of the cost of the helpdesk. There may be further opportunities to save on wear and tear too, e.g. by further insight into the supply chain and whether alternative suppliers or processes can prolong the life of assets. There are also opportunities to classify the rest of the tickets automatically and more consistently which leads to more accurate triage and resolution.

Conclusion

 

Warwick Analytics is able to generate actionable insight and automation at both a strategic and tactical level for helpdesks. It enables helpdesks to optimise their costs whilst maintaining service levels to meet the expectations of their internal customers.

 

Download the full case study here

Using PrediCX to reduce Helpdesk Costs while Improving Service Levels

 

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02 Oct

Using AI text analytics to uncover drivers of loyalty and churn in restaurants

When it comes to the restaurant market, along with the rest of the hospitality market, customer tastes can change and their expectations only grow. Every brand needs to stand for a memorable experience.

Warwick Analytics applied its PrediCX software to publicly available reviews, in particular TripAdvisor reviews for London restaurants. The analysis was centred around use cases that would improve the profitability of restaurants including:
• Understanding the issues which drive churn, loyalty, yield and advocacy
• Operational early warning with granular analysis of issues
• Marketing effectiveness in terms of looking at voucher and campaign feedback
• Compare against the competition, by chain and by location

PrediCX is an automated machine learning platform that quickly and accurately generates models for text, using ‘human-in-the-loop’ technology i.e. it only needs minimum input from a non-data scientist. It took only a few hours to generate meaningful output, no matter how large the dataset, based on concepts instead of keywords and sentiment scoring.

What PrediCX found

  • By looking at all of the second level concepts being talked about by diners in London, aggregated for all reviewed restaurants and normalised as a proportion of total reviews, we can see the concepts that diners talk about most frequently. The two most common issues are both negative – small portions and bland food – followed by a positive one – good drinks selection etc. This view can be aggregated in any way required: By geography, by branch, over time, segment, sector etc.
  • Overall, bad service was the main driver of churn at Level1 and at Level2 – small portions, bland food, poor cooking and rudeness were the main causes. This could be used at the brand or branch level to set KPIs and ensure that levels are maintained appropriately.
  • At Level 1, excellent food and ambience were cited although excellent service was less essential. At Level 2, the view, drinks selection and entertainment were drivers.
  • Loyalty and churn indicators were also analysed for one specific London restaurant, TGI Friday’s in Covent Garden.

Reducing churn

PrediCX can be used to pick up the reviews which contain concepts for churn, negative advocacy or the root causes of churn. They can be quickly intercepted by the restaurant to try to recover the customers with an appropriate message or offer, as well as decreasing the negative advocacy on the web. It can also be used for marketing effectiveness, e.g. picking up concepts of where people have used vouchers and the associated experience and loyalty.

Identify fake reviews

One of the banes of social media is the growing issue of fake, solicited and gamified reviews, the latter being where review sites work with companies to encourage or invite positive reviews and discourage negative reviews in a non-transparent way. There is no way to stop this entirely, but PrediCX can help to train on known fake reviews, remove suspicious or simply glib reviews such as: “everything” [5 stars], or “excellent” [5 stars]. Clearly more reliable data would come from a properly weighted survey, or from the CRM system.

Conclusion

Warwick Analytics is able to generate actionable insight at both a strategic and tactical level for of opportunity for any chain of restaurants, bars or other hospitality. It enables chains to maintain their brand promise whilst at the same time having the ability to react quickly to issues at an aggregate and even a specific customer level to optimise customer experience and maintain loyalty.

Download the full case study here

Using AI text analytics to uncover hidden drivers of loyalty and churn in restaurants
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