Today we will talk about nine application scenarios that big data can't avoid. If the following application scenarios sound so much like your company, you should seriously start thinking about big data analysis tools, which will be a reasonable investment!
Customer analysis: This includes analyzing customers' information, behaviors and characteristics to develop models, subdivide customers, predict losses and provide the next best offer to help retain customers.
Sales and marketing analysis: There are two marketing use cases. The first is to use marketing model to improve customer-oriented applications and better provide recommendations to customers. For example, better identify cross-selling and upselling opportunities, reduce abandoned shopping carts, and improve the accuracy of integrated recommendation engines as a whole. The second is more reflective, because it is to show the performance of the marketing department's processes and activities, and suggest adjustments to optimize performance. For example, analyze which activity solves the needs of identified groups, or stimulates the success rate of putting the activity into action.
Social media analysis: the content generated through different social media channels provides rich materials for analyzing customer emotions and public opinion supervision.
Network security: The occurrence of large-scale network security incidents (such as cyber attacks on American retailers Target and Sony) makes enterprises more and more aware of the importance of rapid identification when cyber attacks occur. Identifying potential attacks includes establishing an analysis model, monitoring a large number of network activity data and corresponding access behaviors, so as to identify suspicious patterns that may be invaded.
Factory and facility management: With more and more devices and machines connected to the Internet, enterprises can collect and analyze sensor data streams, including countless potential variables such as continuous power consumption, temperature, humidity and pollutant particles. The model can also predict equipment failures and arrange preventive maintenance to ensure the normal operation of the project without interruption.
Pipeline management: More and more energy pipelines have sensor and communication functions. Continuous sensor data can be used to analyze local and global problems and indicate whether attention or maintenance is needed.
Supply chain and channel analysis: By analyzing warehouse inventory, POS transactions and various transportation channels (such as land transportation, railway transportation and sea transportation), a prediction analysis model can be established, which can effectively help to replenish goods in advance, formulate inventory management strategies, manage logistics, and optimize routes and send notices when delays endanger timely delivery.
Price optimization: retailers want to maximize the overall profit of product sales. The established analysis model can combine different kinds of data streams, including competitors' prices, cross-regional sales transaction data (to check demand), and production, inventory and supply chain information (to monitor supply). The model can dynamically adjust the product price: when the supply is in short supply or the competitors are out of stock, the price will rise; When the inventory needs to be cleared due to seasonal changes, the price is lowered.
Fraud detection: Identity theft is increasing, followed by fraud and transactions. Financial institutions analyze hundreds of millions of transaction data to identify fraud patterns. This analysis model can also send warnings to users when potential fraudulent transactions may occur.
All these application scenarios have similar characteristics, that is, the analysis involves structured and unstructured data, the accessed data or data streams come from different sources, and the data volume may be huge. On the contrary, analyzing data can establish an analysis model to identify patterns from the same data source and data stream in real time.
The above are the related contents of nine application scenarios that big data can't avoid. For more information, you can pay attention to Global Ivy and share more dry goods.