Gaining a clear, near-real time overview of incoming customer feedback. Monitoring trends over time.
Laser-focus your resources where they are most needed.
Identify strong promoters and detractors.
Tailor the output for relevant decision makers.
Deploying supervised machine learning for categorization and sentiment analysis.
feedback pieces per quarter
40,000 of open-text feedback pieces / quarter
120 high-skill man-days spent on tagging the feedback
3 weeks needed to compile results after receiving a feedback batch
Each time after receiving a batch of NPS (net promoter score) feedback, Albert’s analysts sorted a fraction of it – around 30 % – into categories. The painful process was taking 120 man-days per year while not producing consistent results. Albert needed to speed it up and relieve its employees.
SentiSquare AI created a semantic model by learning to understand the meaning and context of Albert’s past feedback, and reached near-human accuracy in automatically tagging the category and sentiment for 100 % of incoming feedback. A more detailed, multi-label categorization was introduced.
120 high-skill man-days saved yearly through automated classification. Time needed to deliver feedback report reduced from 3 weeks to 3 days. Based on the feedback coverage, each department, branch and region can laser-focus resources to make the greatest impact on customer satisfaction.
“SentiSquare AI automatically handles hundreds of thousands customer responses so that the right person can address customer needs effectively”
feedback pieces per month
12,000+ open feedback pieces / month
Multiple touchpoints and 3rd party services to monitor
Subtle differences between topics – difficult to classify
Open feedback consisting of unstructured text is crucial to understanding the customer’s view but difficult to classify automatically. For E.ON’s purposes, differences between key operational categories are subtle; a keyword-based text classification model would take years to build.
E.ON receives NPS and feedback from different sources at multiple touchpoints. The task was to bring all of them together to provide a 360° view. By learning to understand the meaning and context of E.ON’s past feedback, SentiSquare AI created a topic and sentiment recognition model with near-human accuracy.
With SentiSquare AI recognising the topic & sentiment of incoming feedback, E.ON can now measure the drivers of NPS at every touchpoint, down to each region and 3rd party contractor. Importantly, E.ON’s management makes better- informed decisions enabled by focused insight into all important indicators!
“With SentiSquare AI, we better understand customer pain points and focus our initiatives where they are most needed.”
Detecting hidden topics and emerging trends. Finding specific feedback in the structured output.
Increase retention through identifying clusters of churn topics and customers
Grow customer satisfaction through recognizing and tackling new challenges swiftly
Gain insight into incoming feedback with our analytics tool - quick and clear
Leveraging unsupervised machine learning for clustering and keyword extraction. Enabling semantic search (aggregations etc.)
feedback SMS per month
Read & Forgotten
15,000+ feedback SMS / month
Struggling to measure what drives customer satisfaction
Biased & siloed customer intelligence
Despite the ample feedback, managers struggled to measure causes of customer satisfaction — they lacked a quantified view; knowledge was biased and siloed within teams. T-Mobile tried out multiple state-of-the-art rules-based text analytics solutions. However, none could deliver a reliable output.
To provide a clear overview, SentiSquare deployed machine learning on T-Mobile’s data, resulting in high precision in topic & sentiment recognition. An analytics tool makes the output easy to visualise, interpret, and export. It also enables search for specific examples of customer feedback.
With SentiSquare AI now recognising the topic and sentiment of incoming feedback, T-Mobile can measure the drivers of customer satisfaction in near-real time. Importantly, T-Mobile’s management makes better-informed decisions based on focused insight on any important issue available any time.
“SentiSquare AI ‘reads them all’ in our stead and gives us invaluable insight through advanced analytics. In this regard it is the best tool we have seen.”