In the past, we were in a retail era where data fragments were fragmented. With the advent of the digital trend, companies have been moving toward the use of data to improve operational efficiency. Ultimately, we will be surrounded by data and make good use of each in a seamless environment. A variety of data, completely depicting customers, clearly combining market trends and resource utilization, and accurately planning corporate strategies.
Big data is good, small data is worth mentioning, and many experts have discussed the value of using data. Only after so much analysis of data analysis, the author observed that most people in the market are still confused about data collection and analysis. Many readers are giving their feedback, the most worrying is that the role of data analysis in the system architecture of the IoT application design is not prioritized, so that the design of related solutions is just a collection of hardware, automation Or there is, and wisdom is often gone.
First of all, the author wants to clarify that there are many sources of data. Networked devices are only a means of collecting data. It is not only the Internet of Things application that can collect data.
For example, traditional cash registers are also accumulating data, but there is no electronic storage device, everything is kept on the paper tape, it is very inconvenient to access, and it is basically difficult to do fine business analysis. Until the cash register function was combined with industrial computers, data began to have the convenience of further analysis. However, in order to speed up the checkout speed, there are still restrictions on the fields that can be collected by the current electronic cash register, so if the reader pays special attention to the checkout, almost every electronic cash register has a lot of other devices attached. Several credit card swipe machines, plug-in or inductive, have recently emerged with the spread of third-party payments, and additional bar code scanning devices have begun to emerge. However, the data collected by these devices are concentrated in the business transaction checkout, the retailers still have no way of knowing how many people pass through the door at each time, how many people walk into the store, let alone individual customers around the path, or in each The length of stay or gaze in front of the counter or shelf.
Recently, with the development of hardware such as GPUs and the advancement of related visual recognition software, these data collections have gradually become impossible.
In addition, advances in data analytics technology have increased the performance and price of computing and storage devices, enabling data scientists to focus their attention on structured data to unstructured data. Simply put, the recording of speech and text data can be analyzed. First of all, the traditional customer service phone conversation content can be directly voiced to text, and then through natural language processing (NLP) word exploration for customer appeal analysis. The content and processing of the conversations that the customer service staff and the customer complain about become part of the customer profile. Even in order to save customer service manpower, chat bots developed with relevant data technology have emerged. The input device of the customer on the website or in the store can directly ask the chat robot (usually a text dialogue) for information related to the store or the product. In this way, not only the data collection process directly enters the computer, but the chat robot can even actively recommend the customer's suitable products according to the dialogue process. In contrast, traditional customer service personnel can do more detailed services or deal with more random problems, but the human brain memory capacity is limited, we can not blame the customer service staff to remember the product name or location of thousands of goods in the store. Location, it is even more difficult to expect customer service personnel to read the customer profile of the customer at any time, to instantly grasp the customer's taste, and to make the most effective product recommendation in the prime time before the end of the dialogue.
The source of unstructured data can also be a community discussion on the web or a report of any content media circulation and a comment on the message. I have never seen the viral spread of online messages, which is already a compulsory course for every marketing person. If you don't understand the marketing team of fans or online search optimization, the desire to speak in the company will gradually decline. In response to the new marketing trend in the Internet era, social listening/social media monitoring (SL) has been widely adopted by corporate marketing personnel. Even manufacturers who are market leaders (or have a rush to catch up) have gradually learned that market intelligence (MI) is no longer a copy-paste plus a lot of researchers interpret, using the MI system developed by NLP to let the market The mastery of business trends is no longer a unique tool that big companies can have. Compared to similar products, SL and MI developed by excellent data scientists can enable companies to quickly grasp industry trends, explore potential products or technologies, and monitor opinions or heavyweight media reports on the Internet for companies themselves or competing products. The impact of taking a quicker response. In addition, combined with the spirit of SL and MI, data scientists who are good at NLP can also develop competitive monitoring programs.
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