Keywords: Journey Analytics, Churn Analysis, Customer Lifetime Value, Dynamic Segmentation, Clustering, Pattern Matching
Calculating how much to spend on acquisition and retention is something of a black art. If too many customers are lost, revenues will plummet. If too much is spent, margins will suffer unduly. But big money is at stake.
Recent research studies show that the average service provider in a mature market typically spends 15-20% of service revenues on acquisition and retention activities.
Discover how Machine Learning can help...
Keywords: Anomaly Detection, Behavioural Analysis, Logistic Regression, Kafka, Spark, spark Streaming, Unsupervised Machine Learning, Fuzzy Clustering, Time Series Analysis, Arima, Entropy Index, Index of Dispersion, Correlation Index (Pearson, Kendall, Spearman), MLib and R.
The complexity of mobile Radio Access Networks is quickly increasing and demanding additional efforts to monitor and maintain. Thingbook learns network data’s behaviour to automatically detect anomalies, correlate Key Performance Indicators (KPIs) behaviours, discover new patterns and perform root-cause analysis helping Telcos to optimize service levels, minimize overhead costs and maximize profitability.
Keywords: Apache Kafka, Spark Streaming, Internet of Things, Cyber Security, Streaming Analytics, Device Behaviour Classification, Abnormal Behaviour Detection
We are fast approaching the point where the existential problem of IoT based cyber-security attacks is a serious threat to industrial operations, business activity and our social interactions that leverage IoT technologies...
Our Technology applied a Spatio-temporal methodology to characterise network behaviours. Once characterised, anomalous behaviours can be identified by calculating a similarity metric to previously identified behaviours (e.g. attacks, intrusions or malfunctioning machinery)
Keywords: Tensor Flow, Insurance, Fraud detection, Deep Learning, Claim processing
"The extent of insurance fraud varies between countries. Detected and undetected fraud is estimated to represent up to 10% of all claims expenditure in Europe. This figure varies between countries and classes of insurance due to a number of factors, such as how the market functions or the local prevalence of one type of insurance. " Insurance Europe
Our Technology stores, analyses and predicts thousands of Claims and Customers behaviours simultaneously, finding anomalies and similarities and reducing the human intervention in problems like fraud detection, the discovery of new ways of fraud, identify churn rate propensity and accurate policy personalization.
Keywords: Anomaly Detection, Behavioural Analysis, Kafka, Spark, spark Streaming, Unsupervised Machine Learning, Fuzzy Clustering, Time Series Analysis, Arima, Entropy Index, Index of Dispersion, Pearson correlation, MLib and R
The increasing adoption of connected devices produces a massive volume of streaming, time-series data presenting significant technical challenges for telecom operators in areas like Quality of Experience, determination, Resource Scalability, Predictive Maintenance, Fraud Detection and Cyber Security.
One fundamental capability for network streaming analytics is to model each stream in an unsupervised fashion and detect unusual, anomalous behaviours in real-time as training batch are not always available, particularly when new network behaviours emerge.