This information visualization technique can also be used for data on regional distribution of prices. For example, mark the top five regions with the highest average customer price with green symbols on the schematic diagram, and mark the regions with the lowest average customer price with yellow symbols on the schematic diagram.
After marking the locations, the operator can combine the average customer price distribution with the distribution of the main markets for information visualization analysis.
There is not much overlap between the regions with high average customer price and the “head market” + “long tail market” of the store, which means that the operator can select products for these regions with high average customer price separately (develop products with higher average customer price and greater profit in combination with the needs of local users), and at the same time, distribute physical marketing brochures to the “long tail market” with higher average customer price. In the domestic e-commerce industry, physical marketing brochures have become a common means of offline traffic diversion, which can not only help operators bring in additional good reviews and orders, but also understand the shortcomings of products and customer needs through information feedback in the marketing brochures.
Similar to the design of domestic marketing brochures, Amazon’s physical marketing brochure contains the following information:
Brand Logo + slogan;
Coupon information;
New product shopping guide pictures and texts;
Good review request text;
Fan community QR code;
Product return form;
Other promotional information.
During the formal design, Amazon operators can select the most needed information from the above information and add it to the physical marketing brochure. A standard Amazon physical marketing brochure design.
In addition to the basic cover, back cover, brand logo, and new product pictures and texts, the marketing brochure has a separate area for pasting coupon codes.
Through the separate offline coupon distribution, the operator can learn from the backend order report how many users have placed orders through the offline physical marketing brochure, and then calculate the return on investment of the physical marketing brochure, that is, the ROI value. Generally speaking, the printing cost of a 4-page, 8-sided color physical marketing brochure is about 0.5 yuan. Because the marketing brochure is mixed in FBA and FBM orders and mailed to users, its logistics cost is almost negligible, and 0.5 yuan is even less than 0.1 US dollars when converted into US dollars. Such low-cost marketing has gradually made physical marketing brochures the preferred channel for small and medium-sized sellers to attract traffic.
The return on investment calculation of physical marketing brochures is very simple. You only need to use the backend order report to count how many orders come from new products promoted in the marketing brochure. Assuming that every 100 marketing brochures issued will bring in orders of 50 US dollars, and the cost of each marketing brochure is set at 0.1 US dollars, then the return on investment of the physical marketing brochure is: 50- (100x 0.1) 500%, that is, every 1 US dollar invested can get a return of 5 US dollars.
By comparing the return on investment of physical marketing brochures with the return on investment of in-site advertising, operators can choose their own marketing methods more efficiently and further reduce operating costs.
Price-time distribution sensitivity data is mainly used in two aspects: listing optimization and advertising optimization. Regarding listing optimization, price-time distribution sensitivity data can be used to achieve “price discrimination”, thereby helping operators obtain excess profits. For its operational ideas and operation process, please refer to the relevant content of “Listing Optimization Application of User Shopping Habits Data”.
Regarding advertising optimization, the order volume change data in the price-time distribution sensitivity data can be used to determine the best exposure period for advertising, thereby helping operators determine the best opening time for advertising. For its operational ideas and operation process, please refer to the relevant content of “Advertising Application of User Shopping Habits Data”.