The advertising application of user shopping habit data can be divided into two aspects, one is the optimization of advertising exposure time, and the other is the optimization of single-click bidding for advertising.
The operator can use a bar chart to show the daily trend of the total orders of the store.
The operator can mark the overall shopping peak of the store.
The peak order period is from 6:00 to 19:00 US time, which is also the peak traffic period of Amazon US Station. Therefore, the operator can use this period to maximize the exposure efficiency of the advertisement. It should be noted that the best exposure period here does not refer to the period with the lowest ACoS, but the period with the highest advertising efficiency in the same time, which is suitable for products in the growth stage rather than the stable period.
Regarding the optimization of single-click bidding for advertising, most operators will choose the ladder-shaped optimization method of “high unit price and low total amount” and “low unit price and high total amount”. For example, when a product is just launched and needs advertising exposure, many operators will choose a higher single-click bid (such as $1 per click), but set a lower budget for the single-day advertising budget (such as $10 per day). If the advertising budget is quickly consumed after the advertisement is released, the operator will lower the single bid and increase the advertising budget until the advertising ACoS reaches a stable value.
The advertising setting method of “high unit price and low total amount” and “low unit price and high total amount” belongs to the traditional technique, which is convenient for operators to approach the optimal value of the single advertising bid step by step under limited costs, but its optimization efficiency varies from person to person. This section will explain how to accurately optimize the single-click bidding from the perspective of data.
First of all, operators must recognize the fact that the orders of each store in each time period of 24 hours a day fluctuate. Operators can divide a single trading day of a certain category of products in the store into 24 hours, and then analyze the changes in total orders (including orders brought by advertising and orders brought by natural exposure) and advertising expenditures in different time periods of 24 hours.
The first step is to calculate the changes in order volume and advertising expenditure in different time periods.
Regarding the changes in order volume of a store, the operator can know it through the store background or third-party operation plug-in.