Phase 4: “Data Science Models”
Once you have a CX Insights Framework in place, you will have a better understanding of your particular situation and where you need to focuss to get maximum CX results. A logical next step is often improving loyalty, using data science models for direction.The most common data science models used are; Text Mining (on customer feedback sources, e.g. surveys, web reviews & social, but also customer contact sources, such as chat, email and CRM records), Driver & Impact modeling not only used for prioritisation and forecasting but also for clustering CX issues around touch points and mapping quantitative customer journeys. Using these techniques, you will know how to influence your NPS and be able to identify exactly those bottlenecks leading to dissastisfaction or even increased churn.
What We offer:
Underlined deploys data science to identify impactful opportunities in optimising customer journeys (read more about our Data Science magic here). The Underlined Hub for CX Analytics contains several data science models that can be offered using automised APIs for direct deployment. The enriched data is visually presented as actionable insights in clear dashboards. See here an example of our work with Aegon, an insurer also for pensions, where we apllied our data science models to uncover the actual ‘retirement’ customer journey’.