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Big Data Analytics in Commerce and Supply Chain management - Part 2

Big Data Analytics in Commerce and Supply Chain

In Part 1 of our September article, we saw that big data analytics and AI tools can be applied to a wide variety of sectors and industries. This month we will focus on the retail sector, specifically Customer & Marketing Insights and Supply Chain & Logistics Management, and examine how data analytics can provide insights into customer behaviour, trading patterns, supply chain management or even social media and marketing campaigns so as to increase a business's productivity.

Understanding customer behaviour and marketing actions

Big data analytics tools help to understand customer needs and predict customer behaviour contributιng to the success of a marketing campaign by turning performance data into business-applicable scenarios

The analysis of historical data and data collected from unstructured sources can help to understand customer needs and predict customer behaviour, including any seasonal patterns, and can become a competitive advantage for marketing services and products. It leads to knowledge of what customers are likely to buy at any given time and the price they will be willing to pay, so that a business can increase its profitability.

On the other hand, measuring and analysing marketing strategies can also enhance an organisation's performance and profits. The success of a marketing campaign can be further enhanced when predictive algorithms are in place to help determine future outcomes, such as patterns of customer behaviour and the creation of new opportunities.

Data analytics tools turn marketing performance data into business-applicable scenarios. Collecting and analyzing marketing data from structured and unstructured sources (e.g., social media) and applying advanced statistical modeling and machine learning provides meaningful insights into past and future marketing campaigns.

Management of supply chain and logistics

The application of big data analytics and artificial intelligence in supply chain and logistics can help to better coordinate business operations, leading to more efficient use of resources, boosting productivity and increasing revenues.

Combining large volumes of data from various sources, analysing it using advanced statistics and artificial intelligence, and using interactive real-time monitoring tools can help make data-driven decisions and streamline operations throughout the supply chain.

For example, data analysis can help supply chain managers:

  • Determine sales volumes, anticipate inventory needs and changes in customer behaviour before they occur, so that they can take appropriate action on shipments of goods and inventories

  • Reduce obsolete stock in warehouses, inventory errors and miscalculations, based on their sales forecast

  • Anticipate future process disturbances and/or equipment failures, maximizing uptime and ensuring timely replacement

  • Optimize procurement processes by monitoring in real time the prices and availability of goods from various suppliers

  • Streamline the distribution of goods by choosing the most efficient possible routes for the timely-exact import/export of goods to their premises and delivery to their customers.

At E-ON INTEGRATION we put all this into practice and carry out Big Data Analytics projects for commercial enterprises using artificial intelligence tools that we have developed. From suppliers to warehouses to point of sale and customers, Data Science and Predictive Analytics help make data-driven decisions to improve service quality, performance and results. All a business needs is to gather and organize the information found in big data from internal and/or external sources and turn it into knowledge.

Application of Big Data Analytics in understanding customer behaviour

Big Data Analytics in Customer Insights

Our offering:

  • Customer intelligence analytics (e.g. demographics, preferences)

  • Data gathering from unstructured sources (e.g. social media) using NLP

  • Prediction of customer churn

  • Modelling of customer preferences and satisfaction

  • APIs and dashboards for data visualization and monitoring

Your benefits:

  • Selection of appropriate audiences for product promotion

  • Personalization of product offering according to customer profile

  • Improvement of marketing strategies

  • Efficient inventory management based on supply and demand

  • Increase in revenues by improvement of product strategies

  • Reduction of customer churn

Application of Big Data Analytics in Marketing

Big Data Analytics in Marketing Insights

Our offering:

  • Data aggregation and processing from structured and unstructured sources

  • Historical data, social media, lead, website and ROI analytics

  • Predictive algorithms and scenario testing (e.g. customer clustering, recommendation systems, sales predictions, lead generation)

  • APIs and Dashboards development for data visualization and real-time performance tracking

Your benefits:

  • Real-time monitoring of marketing campaigns

  • Optimization of return of investment (ROI)

  • Customer segmentation and lead prioritization

  • Profit increase by selection of appropriate audiences and advertising channels for product promotion

  • Customer retention

Application of Big Data Analytics in Supply Chain and Logistics management

Big Data Analytics in the Supply Chain

Our offering:

  • Data aggregation (e.g. ERP, SCM, external databases, unstructured sources)

  • Data analysis and business intelligence

  • Development of prediction models (e.g. sales volumes, inventory needs, procurement processes, transportation of goods)

  • Development of APIs and dashboards for real-time monitoring of KPIs and supply chain processes (e.g. suppliers, inventory, warehouse, imports/exports monitoring)

  • System Integration and prediction models deployment

Your benefits:

  • Data-driven and accurate planning of operations and inventories through prediction of future demands

  • Productivity increase through real-time monitoring of KPIs and supply chain processes

  • Increase in efficiency and reduction of unnecessary costs through improvement of procurement, inventory and warehouse management, and logistics optimization

  • Minimization of risks


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