First: daily data feedback (based)
The Key Points of PPC AD Data Analysis in China
Correlative data with flow : IP, PV, online time, jump out rate, the proportion of new users
Correlative data of order: Total orders, valid orders, orders with efficiency, total sales, customer unit price, gross profit, gross profit margin, gross profit margin, gross profit margin, gross profit margin, gross profit margin.
Correlative data of conversion rate: conversion rate of placing order, conversion rate of payment (conversion rate of the phone, conversion rate of online customer service.
Marketing cost data: the number of clicks, reaching IP, consumption, single click charges, the cost of single IP (Baidu, Google).
Data Description: get the above data is generally depends on the direction of the artificial + system, the latter can develop docking system, according to the daily demand is derived automatically (you can save a lot of manpower, raise the efficiency, the data must accomplish know fairly well, best can advance on the second day of the data to do a simple prediction (can effectively detect you the data prediction ability, large-scale operation has very good).
Developing a reporting system that is in line with its own product is very necessary and not superstitious about what is called a professional SEM tool.
Second: weekly data analysis and next week's strategy formulation (core)
Users’ orders and payments may be not completed on the same day, but a week's data is relatively accurate, so we put the weekly data as a reference object. The main purpose is to compare the difference between last week and the week before last data, the operation of a certain aspect of the work, the product has made some adjustments, and corresponding data will also have a certain change. If it is not improved, it means the method has problems or their own problems in this.
Website usage: IP, PV, average browse pages, online time, jump out rate, returning ratio, access to depth ratio, access time ratio;
It is the most basic, each data increase is not easy, which means to continuously improve finding the details of the problem, continue to improve the shopping experience. To illustrate the important data index:
Jump out rate: high jump out rate is not a good thing, but the problem is the key to find where it is. In some promotional activities or casts enlarge media advertising, jump out rate will be very high, which may implies that there are great differences in population is not accurate, or in advertising appeal and access to content, or the visiting page has a problem.
Return ratio of a week 2 visit / total visitors, which means website attractiveness and member loyalty, if under the steady flow, the data is relatively high, too high is illustrated in developing new users is too little, and if too low means that customer loyalty is too poor, re-purchase rate is not high.
Access to depth ratio equals the users of access to more than 11 pages / total number of visits, access time ratio equals the users of access time in 10 minutes more than the number / total user number, the two index represents the appeal of the website content, the higher the ratio the better data.
Operational data: Total orders, valid orders, orders with efficiency, total sales, customer unit price, gross profit, gross profit margin, gross profit margin, gross profit margin, gross profit margin, gross profit margin, conversion rate of placing an order, conversion rate of payment, return rate.
The daily data summary, weekly data must be stable, compare to the data of the last week, focus on guiding the operation inside of work, such as product guide, pricing strategy, promotion strategy, shipping strategy etc..
Contrast to data, why the number of orders decreased? But the sales increase? Is this a good thing?
Contrast to data, why the customer unit price increased? But profit margins are down? Is this a good thing?
Compare to data, if you can do it, sales increased, profit margins increased, the number of orders increased? This is not impossible.
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