In the Hastings Direct VoC Feedback Analyses example, we analysed public review feedback for a period of 12 months to understand what improvements could be made and identify the impact such improvements could have on customer experience, as well as how these impacts would transfer to revenue savings and operational improvements.
Analysing VoC feedback over a prolonged period allows you to detect changes in customer experience. Using AI to do this enables a user to analyse unstructured text data in volumes that are much higher than traditional methods such as manually reading comments, and removes the risk of sampling bias.
Categorising this feedback using NLP & Sentiment analysis gives users the ability to group common themes together and helps detect key areas of interest and commonality (in engagement, processes, products and more) that are undetectable through manual tagging processes. It also highlighs key areas of concern within the businesses operations as well as defining subtopics from these high-level groups giving specific stakeholders granular insights in to areas that impact their aspect of the business. Most importantly, reporting consistent, detail-relevent and actionable information, with proven impact measurements, to key stakeholders in a timely manner is the critical link between identifying and acting on customer pain points.
- Feedback concerning ‘paperwork’ caused a 50% drop in ratings within a 5 month period. The frequency of comments increased in October for 3 months. By expanding the issue, the decrease was due to the mailing of physical copies not being received. Solution; review internal processes, set up automotive alerts between relevant stakeholders to action requests (e.g. print and send hard copies). The result, increase ratings by 50%
- Identify stages of the customer life cycle that results in churn. Premiums increased when renewing policies resulting in a .85% drop, plus multiple renewal quotes with different pricing are causing questioning on Hastings Direct competitiveness and transparency. Solution; operational inefficiencies through renewal process between manual and automated quotes. Set up a single path for renewal, if issues are detected offer a manual option, allow the system to only send a single quote. The result, 1 increase point in satisfaction, customer retention and reduction of system errors.
- Lack of efficiency in staff interactions are increasing call volumes (through chasing), decreasing customer satisfaction, resulting in poor reviews and churn. Solution; investigate specific aspects of call centre protocols around approval for call transfers. The result, repair customers’ impressions, improve in-market brand trust, retain customers and improving public reviews by 2 points.
Explore in-depth how these findings were achieved using Touchpoint Group's AI Customer Analytics Tool by visiting the case study here. If you have data to analyse, get in touch with our team of experts to get started analysing your customer feedback data today.