Data sensing system
What is Data sensing system?
Data Sensing is the high intelligence technology for analyzing customer behavior in order to improve the market planning process according to the current customer needs and market value. The data sensing analyzes the customer behavior with the current market trend using artificial intelligence technology, therefore the data sensing can provide information about the future trend of the market, trending of seasonal products/services and so on. Therefore, the data sensing technology can be used to assist the business team to achieve a marketing goal according to efficient market analyzing and planning
What is the motivation of data sensing system?
To achieve the marketing goals, business analysts and marketing teams have to analyze the market trend according to customers' behavior and the surveyed information through customer satisfaction surveys. After the information is gathered, the market strategy has to be formulated and then develop the market. Once the highly complex information is analyzed by humans, it takes a long time and may not suit to fast-changing behavior of customers.
Artificial intelligence technology becomes an important tool that is used to assist humans in information analysis and decision making. Nowadays, artificial intelligence is used in several business applications such as customer behavior forecasting, market trend forecasting and so on. Therefore, business analysts and marketing teams benefits from real-time information analysis and can formulate the market plan efficiently.
The system will collect all information that occurs such as sales orders, stock replenishment, and customer behavior, then the AI (Artificial Intelligence) system analyzes, learns and mimics customer behavior. which will be able to forecast various demands as follows
A field of predictive analytics which tries to understand and predict customer demand to optimize supply decisions by corporate supply chain and business management.
The practice of using past data, trends and known upcoming events to predict needed inventory levels for a future period.
A system seeks to predict the rating or the preference a user might give to an item. levels for a future period
Manage stock to meet demands from estimating customer needs.
More efficiency plan more and reduce the risks that may occur in the organization
Reduce costs in storage and transportation costs
Reduce the dead stock or insufficient demand, it can predict customers demand accurately.
Reduce working time and increase employee efficiency.
User can filter all data such as activities, time and other factors
User can export the report as PDF, CSV and XLSX
User can edit, add and manage role easily
Create satisfaction and confidence for customers
4. Online marketing
Example use cases
Cruise Company (Traveling)
One of the cruise ship companies in the United States. Experiencing inconsistent customer call volume handling problem with service provider staff. and there is a large number of missed calls which uses the Data sensing system to predict the volume of incoming calls according to the needs of customers. This ensures that we have the right number of staff on a daily. This reduces the number of missed calls by up to 20%.
Grocery Store (Retails)
Food wholesalers and grocery retailers in India There are 22 hypermarkets and 624 supermarkets across India with a network of 13 distribution centers. Seven fruit and vegetable collection centers and 6 major food processing centers need to simultaneously manage the availability of fresh products in stock and minimize waste by using the Data sensing system in Inventory Forecasting to forecast. the distribution of goods to the warehouse in order to be enough to remember and reduce leftovers and spoilage.
Entertainment service (Others)
Movie platform uses data sensing system to recommend movies to customers. That person would like it from the analysis done. Understanding customer preference behavior building a recommendation system customized This detailed individual income also helped the company reduce its movie procurement budget. Because you don't have to chase after spending money on a single famous movie. and used that budget to buy a lot of lesser-known movies.
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