Many of my friends want to ask what are the better tools that can be used to analyze the competitive strength of products? In my opinion, the essence of this question is not which tool is suitable. The tool is secondary and auxiliary. The final decision is made by people's subjective judgment based on data. Many of the tools have similar functions, with only differences in accuracy and data latency. Whether a product can be finally made and what it can achieve still depends on the analysis of data.
Before looking for the corresponding tools, you must first determine your or your company's operational capabilities and how many operational resources are needed to combine with the operational capabilities. This is a very important condition for measuring whether you can succeed in making a product. In one sentence - know yourself. Secondly, you can use software or tools to analyze the competitive strength of a product. In other words, predict and judge what you can do.
Let’s first talk about how to position your own operational capabilities and your own operational resources. You can look at some of the products you have made in the past and see how much daily, weekly, and monthly sales you can generally achieve within one to three months. How many products are selling steadily, and how many products are out of stock? Think about the resources you used to make these products, such as flash sales on the site, coupons, PPC, off-site deals sites, reviews, FB and other social sites. . . . . . You know these best.
After you have a complete positioning, you can now use the data you have obtained as a reference standard to analyze the data, so as to prepare for the promotion of future products in advance.
Let’s start with some basic data and briefly discuss the role these basic data play in analyzing the competitive heat of a product and guiding the sales of the product.
When launching a product, we need to analyze these basic data:
Categories. Different products exist in different categories. A product may have only one category or multiple categories. This will result in differences in traffic size, click-through rate, ranking increase speed, sales volume, advertising click-through rate, and recommended bidding fees. If you can place products in multiple categories, it is necessary to analyze the performance of the same product in different categories.
Price range, which price range has the highest sales volume, which price range has the least people doing it, which price range is most suitable for your own pricing and can maintain profits?
Estimated monthly sales: Which products have the most unique sales, with sales exceeding 1,000 or 10,000? How much traffic do they occupy in this category?
Amazon's own and third-party sellers, in the small category node, what is the proportion of the top 100 Amazons, and how is Amazon's performance? What is the proportion of third-party sellers, and how are they performing?
Time on the market: How long has a product been around? Which product that was launched in the same year performed the best? Which subcategory is in the top 10? If you make this product, how long will it take you to reach their volume?
Number of reviews and ratings: What is the average score of the top 100 or 500 reviews, and what is the average number? Which ones have the highest scores and the most reviews, and which ones have the lowest scores and the fewest reviews?
Keywords, what are the core keywords? One or more? What are the click-through rates and conversion rates of different categories? What are the long-tail keywords? What is the proportion of traffic, click-through rates, and conversion rates of the core keywords and long-tail keywords?
In terms of advertising, which category has high traffic and click-through rate? Based on the analyzed keyword data, how to choose the type of advertising, and how to adjust the advertising promotion cycle strategy according to the product data? What are the advertising data of other companies? Broad, phrase, precision, brand, several types of recommended bidding ranges, how to allocate the budget?
The above information can be obtained through many software, and the type of data obtained is not limited to the one I mentioned. Needless to say, I have used it. I won’t talk about other types of data here.
If I were to make this product, I would set myself a short-term three-month reference goal, such as
Take the unit price of 15.99 to 26.99, monthly sales of 800 to 2200, number of reviews between 0-320, score of 3.3-4.3, category of electronic, and listing time period between June 2017 and June 2018 as reference conditions.
Filter out the ASINs that meet these conditions from the crawled Excel spreadsheet, and then use your own positioning and resources to judge whether you can do it, how to do it, how long it will take, and how many resources to invest to reach their data volume. If you meet expectations within the plan, have you exceeded by what percentage of sellers in this range? If you meet this expectation, can the next reference standard be raised or adjusted?
Of course, the data above is to let you estimate the amount of data you can achieve in the short term, and use it as a reference to learn from your peers and form competition. In addition, you also need to have an overall data understanding of the category in which the product is located, which is actually the distribution of various data. This part actually overlaps with what I said above, for example
Price distribution range, percentage, and which range has the largest proportion.
The proportion of self-fulfillment and FBA (although most of them are FBA now, there are still products suitable for FBM).
The ratio of third-party sellers to Amazon's own operations. If the ratio of Amazon's own operations in a category is high, this product may not be easy to promote (not always).
In terms of sales volume, which listings account for 70% to 80% of all orders in the top 100 or 500?
Distribution of review rating data
Shelf time
...
You may be thinking after reading this, isn’t the question I asked about how to judge the competitive strength of a product and what suitable tools are there? How did it become a question about how to analyze product data through tools?
My answer is that most products can actually always be made, but these products have thresholds and requirements for people. They are not suitable for everyone. They have requirements for one’s own abilities and resources. If you can achieve them, then it is not a problem for you. If you can do it, then it is not a problem for you. If you can’t achieve them, then this is because the competition is very intense and this is not suitable for you. It’s not as simple as just looking at the sales and thinking you can do it.
