Advanced Analytics
Gain competitive advantage by unearthing insights with Artificial Intelligence and Machine Learning
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Discovering deep insights
Advanced analytics introduces the use of machines and statistics to discover complex data patterns which can be used for a wide range of different applications. As an interdisciplinary field, data science and advanced analytics can feel overwhelming.
Our data scientists and data analysts can guide you to use the right platforms to meet the unique needs and skill sets of your team. We can assist you to unearth deep insights from your data, while keeping the solution explainable and understandable at every step.
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Predictive analytics
Predictive analytics is the use of statistical modelling applied over historical data to predict future data trends and performance. Aginic uses a variety of tools, such as cluster analysis, text mining and machine learning to identify these patterns in data that would otherwise be lost in a sea of reporting solutions and databases.
Fraud detection
Through pattern detection, high performance analytics can measure actions to prevent threats.
Improve operational efficiency
Forecasting demand, inventory and improving resource management.
Healthcare diagnosis
Use predictive modelling to understand diseases based on past data and get to the root causes.
Asset maintenance
Predict equipment lifecycle and required maintenance patterns to prevent accidental breakdowns and risk associated.
Risk reduction
Using predictive models to understand risk by building trustworthiness scores.
Complex Modelling
Aginic has extensive experience in assuring, re-modelling and correcting complex pricing, payroll and regulatory models. We have worked with organisations operating in complex regulatory environments where rule-driven pricing or payroll is calculated from a number of data-sources with complex overlays.
Our process involves working alongside your existing team and subject matter experts to ensure that we can quickly absorb context, whilst also identifying areas of risk where further analysis will be required. We build our models incrementally to reduce risk and allow for testing and verification to be embedded in the approach
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Advanced analytics case studies
Sentiment Analysis & natural language processing
Customers interact with organisations and provide feedback in many different ways. These ‘data points’ can be rich sources of information, allowing you to better understand customer experiences and help identify opportunities to improve the way you do business. At Aginic, we use a number of data analytical techniques to undertake sentiment analysis. We can process voice or text with natural language processing to understand the topic, sentiment and outcomes of a customer interaction. Combining thousands or millions of interactions allows us to understand trends in customer behaviour and loyalty and to provide recommendations for improving the customer experience.
Our clients harness the power of voice and text analysis for a variety of different use cases. This spans across reputation management, product and service development, training and support, call routing and regulatory compliance.
Here are just a few tangible ways sentiment analysis can add value to your business:
- Improve customer service, with a focus on empathy
- Overhaul or optimise service lines or key operational workflows
- Use customer voice and text data to enable staff training and development
- Utilise aggregated voice insights to improve product or service offering roadmaps
- Adapt your sales and marketing strategies to be led by the voice of customer
- Surface customer sentiment to front line staff to improve service
- Align staff performance with customer feedback
- Better manage brand reputation across all customer channels
