How to make data statistical tables for SEM? SEM data reports and problem analysis ideas!

Account data statistical analysis is the most important and core stage of work in bidding. Many bidders have been lost and confused in this stage. How should I collect statistics? How to analyze data? How to adjust the account based on the data? This is undoubtedly the most troublesome and difficult thing for many bidders.

Usually the data in the account background gives people a feeling of being numerous and complicated. “Lots” means the amount of data is too large; “complex” means the data is too complex.

In the daily management of an account, you need to do the following data:

1. Summary data of each promotion platform

(Figure 1)

"Impressions" refers to the exposure on the promotion platform. "Clicks" refers to the traffic brought to the website. "Consumption" refers to the promotion cost invested. "Click-through rate" = click/impression. "Average click price" refers to the average cost paid for one click. "Conversion" refers to the return brought by the invested consumption. "Conversion cost" = total consumption/conversion

Comment: This data is not only for yourself to see, but also for your leaders to see. It is a summary data to evaluate the promotion effect of the account. It records the total daily consumption and return on investment in the account. It is a must-have data every day.

2. Disease classification conversion data for each promotion account

(Figure 2)

Comment: If you label the plans and units in your account with disease types, this data can be easily obtained. How much does a certain disease cost per day? How many clicks? How much conversion? When your leader asks you this, don’t just think about compiling the data. This data is not for your leader to see, but it is a data that you really need to look at seriously when analyzing your accounts.

3. Conversion data of each promotion account by disease type and region

(Figure 3)

Comment: If your account is for national promotion, it is very necessary to collect data statistics on the delivery status of each promotion area every week. As a refined bidding promotion specialist, your superior gives you a total promotion fee every month. Will you allocate this promotion fee to various promotion areas in advance? How much fee will be allocated to area A? How much does it cost to divide the area into b? And how much funding will be allocated to each type of disease in each region? And later on, you need to track this data based on a set of regional data, and make feedback and adjustments.

Although data statistics are complicated, if you have the spirit of dedicated research on bidding, these data statistics are no problem. Of course, you will also dig out and collect more valuable data, which is also one of the essences of our bidding.

The first set of data is the aggregated data from the account (Figure 1); we know the total consumption of a promotion platform, but where does this total consumption come from? So we decomposed and counted the first data, which is the second set of data (Figure 2).

Because the first set of data can only show the overall data and cannot meet our in-depth data needs. Next, let’s talk about data analysis, but the prerequisite is that we must compile statistics on the data so that we can analyze it later. We will use the “consumption and transformation” of disease data (Figure 2) to conduct an in-depth analysis and think about the key points.

(Figure 2)

Key points of data analysis

1. Analysis direction of high-consumption and high-conversion diseases

1. Why do diseases with high consumption and high conversion rate consume so much? Which words are being consumed? Are costs under control?

2. Which words generate conversions for diseases with high consumption and high conversion? Can we make these words that generate conversions continue to retain conversions? How to keep high-consumption and high-conversion diseases maintaining high conversion rates?

3. Are high-consumption diseases the ones we mainly promote? (The leaders have always emphasized that we must increase the promotion of the diseases we are promoting because these diseases bring high profits).

4. Do the high-consumption and high-conversion disease types include some keywords with consumption but no conversion? Can we find out the words with high consumption and no conversion and make adjustments to make the cost of diseases with high consumption and high conversion lower?

2. Analysis direction of high-consumption and low-conversion diseases

1. Why do diseases with high consumption and low conversion rate consume so much? Are those words consuming? Why don’t these consumer words generate conversions? How can I adjust these high-consumption, non-conversion words?

2. Which words generate conversions for diseases with high consumption and low conversion? Can we make these words that generate conversions continue to retain conversions? How to make the cost of high-consumption and low-conversion diseases fall within a normal range?

3. Are diseases with high consumption and low conversion the diseases we mainly promote? Have we considered lowering the conversion costs of diseases with high consumption and low conversion rates first? Can it make the overall data cost lower?

3. Analysis direction of low-consumption and high-conversion diseases

1. Which words generate conversions for diseases with low consumption and high conversion? Why can such a small amount of consumption for this type of disease generate so many conversions?

2. For diseases with low consumption and high conversion, should we further increase our investment in these diseases so that these diseases with low consumption and high conversion can bring more conversions?

4. Analysis direction of low-consumption and low-conversion diseases

1. Are the conversion costs of low-consumption and low-conversion diseases within the normal range? And which words are being consumed? What are the words that generate conversions?

2. How should we optimize and adjust low-consumption and low-conversion diseases in the future so that these diseases can maximize their value? We need to think deeply and plan and summarize these.

Above, we conducted in-depth analysis and thinking from four perspectives. The overall direction of the analysis is to tell us why there are so many types of diseases that consume so much? How can we keep the transformed diseases continuing to transform? And how should we optimize and manage these diseases? The simplest and clearest points.

After going through the above series of data statistical analysis, do you still think that bidding data statistical analysis is a difficult task?