Second, we are unable to trace the data from the next day because Quantum clears the previous day's data once it hits midnight. This means that if a customer sees the product via search on the first day and directly enters the URL on the second day, we would be unable to track the user's real source. The third step: We can create horizontal and vertical analysis reports based on the recorded data, which is highly beneficial for our advertising placement. For Taobao online stores, this allows us to very intuitively see the effectiveness of each source and the trend chart of each source’s effectiveness. I won't go into detail here as Excel is quite powerful; as long as you can think of it, you can uncover meaningful data for Taobao online shops. This method is quite helpful but should only be used temporarily due to its significant limitations. It is more suitable for the initial stage of a store or for stores where daily traffic is not enormous and transactions are not extremely frequent. First, it still requires relatively high tracing skills from the data analyst when it comes to Taobao online store rentals. Thirdly, Quantum only retains 40 pages (1000 records) of data per day. If the store has a large amount of traffic, there may be issues with tracking unless the data is regularly saved in Excel or imported into a database. Step two: When a customer places an order, we can immediately obtain their order time and location. It's best to check the chat records between the user and customer service to see the start time of the conversation. Between the order time and the start of the chat time, we take the earlier one, which theoretically is closer to the time the customer visited our store. With the time and location, we can trace back to the real-time customer visit and locate the access record of that particular customer in the Taobao shop, find his first visit, and see the source of his entry, thus recording it.
Promotion of Taobao online stores should be based on data rather than intuition. Following intuition will only lead further off course, while following data will broaden the path. How do we analyze Taobao store data? Here, I recommend a tool called Quantum Hengdao Statistics. Let's talk about Quantum. Quantum, developed by a team aiming to provide microscopic quantum-level data, is currently the best statistical tool from the perspective of a store. I remember during a communication meeting at the Shanghai Crown Club, when merchant asked Quantum's staff member Ni Hao why some Quantum data sometimes does not align with other tools' data, he confidently responded: "This is normal because Quantum is the most accurate." Impressive indeed. Also, when consulting Quantum's Bingyi and Ni Hao on Wangwang, they were always willing to answer my questions, which moved me deeply. Recently, Quantum's sub-account login function shows the team's continuous efforts towards quantization. Many friends have already explained Quantum's functions clearly with screenshots, so I won't elaborate further.
We specialize in collagen products, currently having three products, which is quite a few in the collagen industry. However, compared to other industries, the number of products is still small. We were eliminated in the second round of applying for Taobao Tianxia's cloud brand due to insufficient product numbers, which was quite regretful. Few product numbers, high costs, and high customer unit prices make us particularly concerned with data mining. Because of the limited number of products, we cannot afford loose management expecting success from one area compensating another; due to high costs, every cent spent must consider the return on investment; and due to high customer unit prices, losing any customer means significant loss. We have always desired such data analysis that could directly tell us the source of each transaction, allowing us to quickly adjust our advertising direction. However, this has been hard to find. We even considered whether Taobao could open up a user visit record interface for us to develop ourselves, but we never found the entry point, and even if we did, development would require human and material resources. Quantum Statistics provides real-time visit data reports, so we use this report combined with Excel to create basic promotional effect analysis data reports. I'd like to share our reports with everyone, and if there are any areas that need improvement, please let me know.
Step one: We first familiarize ourselves with each column of the real-time customer visit report. Serial number, the record sequence number; visit time, self-explanatory; entry source, here displays the visit source, where the Quantum team lists common sources like Direct Express, Diamond Booths, etc., and other uncommon ones are grouped under other sources. What we actually want is not what we directly see but the specific source address we can get when hovering over with the mouse. For example, when we hover over the "other sources" position of the first record, we can see the specific URL address of the source in the browser's address bar or tooltip, which is very important because through these, we can know where the source is. Visited page, where users are currently visiting; visitor location, self-explanatory, visitor's address; customer tracking, this column is very important for tracing; repeat customer, used to judge if the user is a first-timer.
Fourth, we discovered that data might be lost, causing difficulties in tracing. For example, if a customer generates a few PVs around 16:00 without purchasing, then visits again around 20:00, generating a few PVs and placing an order, we try to trace back but find the PV records generated during the 16:00 timeframe are missing, even though the total number of visits PVs had not exceeded 1000.
The ideal situation would be for the Quantum team to provide performance analysis reports. I believe the Quantum team will eventually develop them. But as for us, we can't wait. I imagine all buyers are eagerly anticipating this. In the absence of adequate analytical tools, leading competitors in business details gives us an earlier opportunity for self-breakthrough and a greater chance of success.
To manage a store well, we need both macroscopic and microscopic reports. The process of gradually mining from the macro level to the micro level is the process of a store or enterprise progressively refining its management. Quantum Statistics is undoubtedly a powerful tool helping us refine our operations. Thank you, Quantum Statistics, and we hope the Quantum Statistics team becomes stronger, achieving greater breakthroughs in terms of report expressiveness, accuracy, and development speed.