Leaders want our account to go further, but in the face of a mature account, what else can we do besides adding words, updating ideas and checking rankings? Taking the decoration industry as an example, this paper introduces some simple data segmentation methods, which can help us adjust the accounts.
First, we need to get the account data. It is not that the more detailed the better, but it must be as accurate as possible so that there will be no mistakes when subdividing the data. Of course, if you can use the link tag function of Google Analytics, and it happens that this monitoring is installed on your website, then your data will be much more accurate than others.
If you use Omniture, an expensive tool, it is certainly better. At least in terms of data, you are ahead of your peers. However, even if you use basic data tools such as Baidu statistics and 53 customer service, it doesn't matter. Although the accuracy of the data will be different, the analysis idea is the same. So suppose we only get the data of clicks, consultations and consumption, what should we do next?
Step 1: subdivide the platform
There will be different platforms for network promotion, and the traffic quality of the platform will directly affect our final income, but enterprises often evaluate the network as a single channel, which leads us to choose and focus on the platform without purpose.
Even on the same platform, taking Baidu as an example, the traffic quality brought by his PC\WAP\ Internet Alliance \ mobile DSP is very different, so the more you get used to the strategy of unified delivery, the more you need to make a detailed evaluation at this time.
By consulting the simple background data of the software, we can sort out the following table:
This is the data of a simple decoration website. We made a pivot table by consulting keywords and consulting sources. It can be clearly seen that Baidu's consultation accounts for a very large proportion. So, at this point, should we start adjusting keywords according to this table?
The answer is no! Because the meaning of the platform is not revealed. There are naturally many platform consultations with large traffic, but what is his cost performance? I can't see it from here, so I need more data support to compare different platforms.
By adding input data, we get the following table:
Click here to refer to effective consultation. First, we can clearly see that 360 and sogou have low traffic but low prices, while Baidu has high traffic but high prices. Especially Baidu's mobile terminal, the price has reached a certain level.
Do you want to control the data on the mobile side immediately? Lower the price? Check the ranking? Correct keywords? If you don't want the boat to capsize, you need to dig patiently.
Let's look at another set of data. 53 * * *, there are 95 1 valid consultations, of which 668 can be counted on the platform, accounting for 72%, and the rest 136 consultations that cannot be counted are from the mobile terminal. Fortunately, the mobile traffic of this decoration website is only invested in Baidu Mobile (natural traffic such as SEO is ignored here). Then let's correct the data. Get the following table:
It can be seen that in fact, Baidu's mobile terminal has high cost performance, large traffic and low price. In fact, we should increase the investment in this project, which is why the accuracy of the data is very important. He may bring you a completely different judgment.
Of course, if every platform of the website has a mobile terminal, you don't have to worry about inaccurate data. Unrecognized traffic can be allocated completely by the proportion of PC-side traffic. Of course, there will be some errors.
The next question is, except for Baidu's PC, the price/performance ratio of other platforms is better than this. Which one should we pay attention to? It's just that these data may not allow us to make a decision immediately. In this case, we still use the old method and add new parameters for comparison.
This time, the selected data is traffic. What should I do if all platforms join the consultation?
Is this dependence obvious? The market share of 360 is greater than that of sogou, but the drainage is low and the consultation rate is high. The consultation rate between sogou and mobile phone Baidu has been very tense. So what we need to do next is obvious: increase the investment of 360 parts and optimize the traffic of Baidu and sogou.
(PS: You should have noticed that we seem to be deliberately avoiding the data on Baidu PC. This piece has the largest flow. Why is this piece not adjusted? In fact, the reason is very simple. The place with the highest consumption must be the place with the highest density when doing SEM. It is necessary to compare and analyze multiple data to find out the optimization direction. Therefore, the following separate data analysis, determine the direction. )
We have made adjustment strategies for different platforms, so what is the most important part of Baidu? As usual, do a data breakdown again.
Step 2: Traffic segmentation
First of all, we need to get a series of original data, such as traffic reports, consultation visit reports, etc., and then sort them out. The purpose is simple. If possible, I hope every keyword is accurate, how much it consumes, how many clicks it brings, how many consultations it has, and how cost-effective it is. Of course, for most promotion, this is impossible. Huge data will only make our management cost too high.
What we can get is dead data, but the customers we face are alive. It is difficult for us to track down every customer and know their thoughts, so what we can do is to analyze the customer's behavior through these inaccurate and imperfect data. What we need to do is to put forward suggestions for adjusting the direction, rather than showing the value of each keyword.
So, let's start with moving bricks:
The data is very direct, from the keyword consumption table compiled by Baidu backstage, the interview scale compiled by consulting backstage, and the consultation scale, and then do a little splicing. Aside from consumption and consultation, the consultation rate of the words in the renderings is 0.9%, and the consultation rate of the regional+decoration company is 10.9%, which is the direct reason why we will put the company's words in the renderings at the price of 10 times or more. Isn't it intuitive? If you are busy sorting out these highly consulted words one by one at this time, then we are really moving bricks.
Because the keywords of bidding have matching patterns, we can see if there is any way to directly merge the consumption words in the left table with the consulting words in the right table. So how is this fault connected?
As I said before, we only need one direction, not every word. Therefore, we classify and merge keyword data. The principle and simplicity of merger are based on business lines, customers' buying stages and search intentions. You can go to the website to see the categories of goods. If it is an e-commerce website, then the home page has already helped you classify it. Looking back at this decoration website, we can classify it according to customers' search intentions, such as price, design, renderings, institutions and so on.
If these methods can't satisfy you, then divide them according to the customer's purchase stage: pay attention to the goods, start to be interested, search for relevant information, compare products and prices, and consult to buy. Different stages must correspond to different keywords, classify keywords, and then sort out different types of data:
(Note: Due to space reasons, the last general class can actually be subdivided. )
Let's talk about the part with the most traffic-Puban. General category refers to the most commonly used words once we think of this product, taking decoration as an example, that is, decoration, family decoration and decoration.
These words are difficult to adjust, because they will involve many problems such as purchase stage, product exposure and market coverage. If the unit price is not particularly high and the budget is not tight, it is recommended to maintain the status quo. The same is true for brands and companies, so I won't go into details.
Aside from these, we can see that the biggest problem is style and price category, and the category of renderings is the most cost-effective word. Although his consultation rate is only 0.9%, it is better than the unit price.
Then we can draw a preliminary conclusion and expand the keyword flow of the effect graph class; Ceiling price, style words, extract the parts with high consumption and low consultation to optimize; Platforms (decoration network, decoration forum) have low traffic and large expansion space, which need to be upgraded.
Is the idea of account adjustment clear? After several months of adjustment, do the same data table for comparison, and then further optimize the adjustment account.
abstract
The idea of data subdivision is actually very simple: integrate existing data and make comparative adjustments with experience. If the current data can't draw a conclusion, add a new data dimension to adjust. By constantly adding new data and subdividing dimensions, the original "whole" is divided into multiple blocks for analysis and adjustment.
Extended reading:
Logical thinking is simple, but the key is how to get your own data through various simple tools! This data is enough to spend hours sorting out and analyzing. If you are doing this analysis for the first time, you will spend a day sorting out all kinds of data and eliminating all kinds of noise.
I sorted out the presentation data, click-through rate, ranking and so on. Before, it was abandoned in the end. Therefore, we need to do a lot of exercises and efforts to master the key data for analysis.
So, have the above efforts been completed? To paraphrase Uncle Ma Yun's famous saying: Business in the world is hard to do!
This is only the first step of optimization, and it only provides you with an idea of data segmentation, paving the way for us to adjust our accounts. Before the real adjustment, we need to do many things:
Data combination for effective consultation:
In fact, there is still a long process from consultation to billing. Anything can happen as long as the customer doesn't pay. Therefore, collecting effective customer data has become an arduous task. What happens if keyword effectiveness is added to the above table? How much will it change?
For example, after communicating with the sales manager, I found that there was a lot of consultation about the renderings, but the effect was very poor, that is, the consultation cost might be very low, but the effective consultation cost was very high, so I finally gave up the expansion of this participle.
Ignored guide words:
Every time you make a single order, there are many searches and consultations. Except for some impulsive customers, most customers have a long selection and purchase cycle before trading, especially for more expensive things.
So how many words have been searched but not consulted in this cycle, and how many words have assisted our transaction? This is one of the main reasons why we gave up the adjustment of general classes and institutional classes before, and it is also the reason why we mentioned the Link Tag function of Google Analytics (a cross-domain tracking method) at the beginning. So, how many words can we measure directly with data before adjustment?
Success or failure of return on investment:
ROI is very important, but it is not the only important indicator. Experience tells us that if our investment increases, the return on investment will inevitably decrease, and at the same time, we can occupy more market share. Market share and ROI seem to be the opposite. Just like Dangdang, a crazy strategy of pursuing ROI made him fade out of people's sight, and a strategy of pursuing the market at a loss in JD.COM Mall made him firmly occupy a corner of the e-commerce field.
It depends on how high you stand to look at this problem, but for an enterprise, ROI is like a bird. If you hold it too tightly, it will die; if you hold it too loosely, it will fly away. Maybe only time will tell me the answer, but I am fat, not fat.
The road to success lies in correct data analysis and judgment, please pay attention! Is the correct data analysis.
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