1. It's not enough to look at sales. All these aspects should be considered.

2. How to use big data tools to select products efficiently?

Wonderful guide:

Cross-border e-commerce is popular with a saying that "seven points depend on selection and three points depend on operation". If the product selection is incorrect, the difficulty of later operation will also soar.

Novice sellers often follow the simple logic of "what sells best" when they first enter the market. However, products with high sales volume on e-commerce platforms may not be suitable for you. All sellers are paying attention to the best-selling list, unless you can find differentiated demand points, you will only be submerged in the vast sea of people. Doing cross-border and blindly following the trend is the first taboo.

In addition to the product sales itself, the market, groups, e-commerce platforms, logistics, profits, and even the entire product line strategy will have a direct impact on profitability, and sellers must consider it comprehensively.

It's not enough to look at sales. All these aspects should be considered.

Market Group: Sellers need to fully understand the cultural background, lifestyle, consumer demand and habits of consumers in their market. By browsing the sales rankings of major e-commerce platforms and the hot search keywords or topics of search engines/social media such as Google, Facebook and Instagram, we can find products that meet the needs of local people.

E-commerce platform: different platforms have different market positioning and rules, and the hot-selling categories of each site are different. Sellers need to do their homework in advance. Be careful not to choose products with complicated review process, such as medical devices, health products and precious jewelry. , restricted/banned in Amazon, requiring sellers to provide additional certificates and sales qualifications. Novices of this kind of products had better not touch it.

Logistics: There are many uncertain factors in cross-border e-commerce logistics, such as sudden weather change, route adjustment and customs detention. , will affect the transportation aging, short shelf life, fragile and afraid of extrusion, need to be carefully considered.

Profit: The first priority is to control the cost, which mainly includes: commodity cost, packaging cost, inventory cost, logistics cost, promotion cost and labor cost. In addition, we should also pay attention to the hidden costs of e-commerce platforms, such as service fees and transaction commissions. Generally speaking, make sure there is enough difference between the cost and the selling price.

Product line planning: While selecting products, we should arrange our troops scientifically according to the characteristics of different SKUs, and set the product structure of "drainage money+explosion money+profit money" so that different types of products can play different roles. Drain money to bring traffic to the store and attract customers at low prices; Explosions to meet customer needs, cost-effective; Profit, used to support the seller's main profit.

After locking in your favorite category or product, you need to focus on:

The market capacity, sales volume, repurchase frequency, competing product scale, ranking, evaluation, shelf time, keyword popularity in search engines/social media, advertising trends, interaction volume, etc. So as to judge whether the product is likely to explode.

How to use big data tools to select products efficiently?

Faced with these massive and multidimensional data, big data selection tools can provide more scientific and efficient support for enterprise decision-making. Some sellers rely too much on their intuition, and the decisions they make are often thousands of miles away from the real market, which is also the second biggest taboo for doing cross-border.

The big data selection tool is simply a massive commodity library supporting multi-dimensional data screening methods, which can be used to study the data characteristics behind the explosion. By filtering data of different dimensions, such as category, sales volume, keywords, etc. , you can quickly narrow down the scope until you find the product you want.

A thousand readers have a thousand hamlets. The freedom of big data selection tools is very high, and different permutations and combinations can derive different results, all based on the seller's selection strategy.

To give two simple examples, if the seller's strategy is to promote potential products in the rising period, then the screening conditions can be set as: short shelf time, relatively low/rising BSR ranking, high/low score, and continuous sales growth.

If the seller wants to make use of seasonal goods to start quickly, he can search the keywords of related goods, understand the specific data characteristics, and filter according to this condition. It should be reminded that to make products with strong periodicity, the premise is that sellers must fully predict market changes and the supply chain must be strong enough.

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