Commodity analysis was once the earliest form of data analysis. Most of the ideas of modern data analysis and data models evolved from here. It may be because it is too traditional, or because Internet companies do not need to earn money to support themselves. In short, there are very few articles introducing commodity analysis. Today we will start with a brief introduction to the basic concepts of commodity analysis.
Commodity analysis refers to the analysis of the purchase, sales and inventory of commodities. Commodities have a broad meaning. Broadly speaking, all things that condense human labor and meet people’s needs are commodities. In a narrow sense, people will distinguish between commodities, services, and rights.
- The goods that are prepared in advance, packaged, and delivered to customers are called commodities
- Those who need on-site preparation and deliver to customers in a process are called service
- The qualifications of some users for preferential and preferential enjoyment of goods and services are called rights
For example, you ordered a meal on Are you hungry. Because you are a super member, you can enjoy a coupon, and the rider brother will deliver the meal to you. Here rice is a commodity, coupons are rights and interests (you do not have others), and the courier brother provides services. Generally speaking, buying, selling and storing are all products in a narrow sense, and this article is also about this. Because of the physical form, the issue of purchase, sale and inventory is involved. Please pay attention to this point. Many Internet businesses provide virtual goods, often only for sale, without purchase and storage. We will share this category of goods separately later.
Commodity analysis is very important. Because for physical enterprises, selling goods is the only way to make money, and the cost of production, logistics, and inventory is the biggest cost, so it is necessary to analyze the goods clearly. Internet companies do not rely so much on commodity sales. They can make money by producing gorgeous data plus the sound of loud noise, so the status of commodity analysis has dropped a lot. However, in e-commerce, especially vertical e-commerce, fresh food e-commerce, and self-operated e-commerce, product analysis is still very important. After all, the goods are in their own hands and they will lose money if they can't be sold.
Commodity analysis is the ancestor of all data analysis methods. In the era without the Internet and membership cards, companies could not record data on user behavior, user attributes, product usage, store trajectories, etc. Whether it is a large multinational chain giant or a small supermarket, they can only obtain data through the supermarket POS machine to scan the code. The only thing that can be obtained is the product barcode information and the POS machine information that scans the barcode. Therefore, I can only stubbornly analyze commodities. Don't look at these two pieces of information, which are very simple, but they combine to form the entire basis of commodity analysis, and derive numerous analysis contents.
Commodities can be divided into durable goods and fast-moving consumer goods. Fast-moving consumer goods are easily consumed and need to be repurchased daily, such as instant noodles, paper towels, shampoo, toothpaste... According to the degree of preservation, they can be further divided into fresh products (live fish, fresh meat, vegetables and fruits...very Intolerable things) and packaged products (with packaging, preservatives, resistant for a period of time). Durable goods are often large, not changed for a long time, and can still be used after use. Such as furniture, cars, computers, mobile phones. According to the characteristics of use, it can be divided into single multi-function (such as cars, many accessories, change it at will) and multi-piece combinations (such as furniture and household appliances often need to buy a set).
Due to the difference in price, function, frequency of purchase, and usage, there is a big difference between durable goods and FMCG. Note that it is not ruled out that there are strange things in life that use durable goods as fast-moving goods and fast-moving consumer goods as durable goods. For example, a technology GEEK does not wash his hair once a month, but he has to buy a new mobile phone for fun. As a result, I bought a bottle of shampoo for three years, and changed a mobile phone a month—this kind of person certainly has it, but it is not a high probability event. Therefore, durable goods and fast-moving consumer goods need to be discussed separately.
Let’s discuss FMCG today. They are used the most in daily life and are easy to understand. Take popsicles as an example below. Some students will say: What is the analysis of popsicles? Popsicles won’t rot when placed in the refrigerator, so I just put them in a batch and sell them slowly. What are you afraid of? Yes, if you sit in the office and type on the keyboard, you definitely feel nothing. But if you really don't have a meal, you can't calm down when you count on this box of popsicles. This is the first concept to be understood in commodity analysis: only about 10% of the cost that consumers pay for the commodity is the production cost of the commodity. The rest of the money will be paid for the seller’s salary, shop rent, refrigerator’s electricity bill, and the boss’s and boss’s son’s internet expenses for the night’s Internet services...
Therefore, the goods must be sold within an appropriate time, and the goods must be turned into money in time. It is necessary to understand the off-season and peak seasons of goods, sell more goods when sales are good, and make more money; when sales are poor, buy less and save costs. Especially popsicles, which are highly seasonal products-very few people brave the biting wind. So its sales are likely to follow the curve below.
Since the sales of popsicles are related to temperature, there must be differences in different regions. For example, Guangdong has hot weather and sells for longer; after heating in Northeast China, there is a habit of eating popsicles in winter, but there may be a wave of sales in winter. This is the second concept of commodity analysis: regional differences. To do commodity analysis, you must understand and respect this difference in order to sell well.
Popsicles are not high-tech products. You can make them, I can make them, and he can make them. If you want to sell more, you have to think about ideas. For example, make some raisins, some weird shapes, and some ice cream. This brings us to the third concept of commodity analysis: grade difference. Often the lower the price, the higher the peak sales, but the lower the profit of the monomer; the higher the higher the sales, the more stable the sales during the low period (because it has to meet the needs of some consumers to save face and pursue the quality of life, such as Valentine’s Day, or is it cold? I want to bring my little girlfriend to nibble Haagen-Dazs).
Since there is a difference between high-end and low-end products, there are new product launches and old products delisting. This leads to the fourth concept of commodity analysis: commodity life cycle. Especially for FMCG, the core functions are basically the same. Consumers always love the new and dislike the old. So often in addition to a few classic models, businesses will continue to introduce new products. But there are two possibilities for success and failure when launching new products. Therefore, the new product will have two curved shapes.
In reality, even if you are a small stall that only sells cold drinks, you will not only buy one type of popsicle. Therefore, in fact, the trend of the total sales of goods is that nnn multiple curves are intertwined with each other. This makes statistics and forecast sales very complicated. If he is really a stall owner, it is likely that he doesn't even know how many pieces he has sold and how much money he made in a day. Therefore, traditional commodity analysis consumes a lot of energy to make sales records of various categories. Especially in an industry where the categories, styles, and designs of clothing are very complex.
The complexity of sales directly leads to unpredictable inventory. Moreover, there is a long cycle for ordering, contracting, production, and delivery of goods. Therefore, people cannot wait until the stock is sold out before placing an order-new goods will arrive in one or two months, so you can't be hungry for a month or two! Therefore, the forecast of sales has become the eternal pursuit of commodity management. It is also a problem that commodity analysis has not been able to solve since it was introduced to the country in 1990 until 2020. Many people boast that their predictions can reach 80%-90% accuracy. Note: Many commodities have less than 10% net profit, and 10% inventory loss is enough to cross most industries. So the accuracy of 80%-90% is not enough. In particular, the prediction accuracy that these people boasted is often the total accuracy. When it comes to a category and a style, it’s a break.
Therefore, in commodity management, three business models have been derived.
**Mode 1: **Explosion mode. For example, iPhone has one or two popular models every year, which can greatly reduce the difficulty of product management and cut off the small categories that are difficult to sell and easy to fail. This looks very beautiful, but you can ask for the product to be really special and outstanding, otherwise the hot style will not burst, and you will have to drink northwest wind this year.
**Mode 2: **Group purchase mode. It is equivalent to locking the sales quantity first, then producing and shipping. This seems to solve the inventory problem from the source, but why do consumers have to wait so long? Why do I have to join the group? Why can't I refund if I am not satisfied Therefore, it is often impossible to control the price and high return rate when joining a group.
**Mode 3: **Hunger marketing. Similar to Xiaomi's early F code, the essence is to lock sales first, and then arrange inventory. It seems perfect, but you can ask your fans to be really loyal. Otherwise, what if you return the product?
Therefore, after the launch of these three models, many people hope that data analysis can analyze the characteristics of the hot money, how many people can participate in the group, how much discounts are required to participate in the group, and the number of loyal fans. But if you understand the nature of the product and the nature of these three modes, you know that this is a very difficult task-even if you type more codes, it is impossible for a Jobs to break out of the screen on your computer. Can't become an iphone.
Of course, some seemingly simple problems are actually very complicated to solve. For example, a simple question: Why can't the product sell? It may be contaminated by many factors such as region, grade, product mix of the store, promotion, product life cycle and so on. Of course, there are more messy factors such as users, product experience, brand influence, macroeconomics, and competing product actions. However, most of the students did not grasp the basic form of the product, so they directly inserted it into the analysis. Therefore, facing the similar curves, they could not read the meaning at all. I was anxious to add dimensions such as users and products to analyze, and I became more confused.
If you want to get started, it’s best to start by understanding the basic shape of the product—the basic curve shape above in this article. The basic curve of each commodity is a digital reflection of a specific business. Only by accumulating more understanding of the basic form can we dismantle complex problems. If you want to see more in-depth analysis methods, we will share the basic ideas of durable goods in the next article, so stay tuned.
Author: Chen grounded gas, micro-channel public number: down to earth school. A data analyst with ten years of experience and CRM experience in multiple industries.