Data Analytics
Making Your Data Meaningful / Services
We offer fully customised data analytics services, tailored to the needs and visions of the customer.
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Data Analytics – a process of examining and interpreting data to find meaningful patterns, trends, and insights. It involves collecting data from various sources, organizing it, and using different techniques and tools to understand what the data is telling us.
Data Lake – a centralized repository designed to store, manage, and analyze large volumes of diverse data types in their native formats. It allows for the ingestion of structured, semi-structured, and unstructured data, facilitating scalable storage and providing a foundation for advanced analytics and machine learning.
What do we offer?
Fully customised data analytics services, tailored to the needs and visions of the customer. To either:
improve effiency and productivity, and/or
achieve more effective decision-making, and/or
drive better financial performance.
The final product is an optimised and continuously updating data lake with a user-friendly dashboard for data visualisation tracking and displaying key metrics, trends and forecasts.
Business problems addressed
Many businesses miss the benefits of analytics tracking to drive their decision-making. Adopting a data-based approach ensures better costs optimisation and significant rise in revenues.
Opting for an affordable investment with swift results (longer projects do not exceed 4 months of work), the company will benefit from immediate and clear benefits with the minimal disruption to their existing IT systems.
We ask the client
To formulate precise goals and overarching strategy in order to extract key metrics to track
To provide access to data sources
To undertaking thorough reviews and provide feedback when necessary in order to get the best end result possible
To provide authorisations, to sign agreements, such as the ones related to GDPR, in order to avoid legal hurdles
Deliverables
Technical and user documentation + key metrics documentation
Configured data lake
KPIs / trends / forecasting dashboard
Possible use cases
Entertainment sector
Content personalisation: recommending shows and movies based on viewer preferences and behaviour - in the long term, leads to increasing engagement and retention.
Churn prediction: identifying users likely to cancel subscriptions and implementing retention strategies.
Content acquisition: analysing viewing trends to guide content acquisition and production decisions.
Example metrics could be:
Monthly Active Users (MAU) - the number of users engaging with the service monthly.
Average Watch Time - the average amount of time users spend watching content.
Churn Rate - the percentage of subscribers who cancel their subscriptions.
Content Engagement Rate - the level of interaction with specific content (e.g., likes, shares, comments).
Finance
Fraud detection: identifying fraudulent activities by analysing transaction patterns and behaviours.
Risk management: assessing credit risk by analysing customer financial data and market trends.
Customer insights: enhancing customer service by analysing transaction data to understand customer needs and preferences.
And example metrics could be:
Net Promoter Score (NPS) - measures customer loyalty and likelihood to recommend the bank.
Loan Default Rate - the percentage of loans that are not repaid on time.
Fraud Detection Rate - the percentage of fraudulent transactions detected.
Customer Lifetime Value (CLV) - the total revenue expected from a customer over their relationship with the bank.
Manufacturing
Predictive maintenance: analysing machine data to predict failures before they occur, reducing downtime and maintenance costs.
Quality control: real-time monitoring of production processes to identify and rectify defects, improving product quality.
Supply chain optimisation: enhancing supply chain efficiency by analysing supplier performance and logistics data.
With key metrics including:
Overall Equipment Effectiveness (OEE) - measures the efficiency and effectiveness of manufacturing operations.
First Pass Yield (FPY) - the percentage of products manufactured correctly without any rework.
Mean Time Between Failures (MTBF) - the average time between system breakdowns.
Supplier Lead Time - the time taken for suppliers to deliver goods from the order date.
Retail
Personalised marketing: by analysing customer purchase history and behaviour, the retail chain can create targeted marketing campaigns, increasing customer engagement and sales.
Inventory management: predictive analytics can forecast demand for products, reducing overstock and stock-outs.
Customer experience: analysing customer feedback and interaction data to improve in-store and online experiences.
Tracking:
Customer Lifetime Value (CLV) - measures the total revenue expected from a customer over their relationship with the company.
Stock Turnover Ratio - indicates how many times inventory is sold and replaced over a period.
Customer Satisfaction Score (CSAT) - measures customer satisfaction with products and services.
Sales Conversion Rate - the percentage of store visitors who make a purchase.
Project phases, timeline
Free initial meet-up & consultation - 1 hour
Integration scope & planning - 1 month
Data pipelines implementation & visualisation - 2 to 3 months
Related cases