If you want to accurately improve your registration conversion rate, you need to know these three factors!

If you want to accurately improve your registration conversion rate, you need to know these three factors!
Internet practitioners are certainly familiar with the conversion rate indicator. We often pay attention to conversion rates, such as registration conversion rate, purchase conversion rate, etc. These conversion rate indicators are closely related to our website operations : registration conversion rate can measure our website's ability to acquire users; purchase conversion rate can measure our website's ability to generate revenue, which directly affects our revenue. Suppose there are two similar e-commerce websites with similar traffic sizes. If one of the websites has a higher purchase conversion rate, then its revenue will generally be higher. 1. Three major factors affecting conversion rate Today's course is mainly aimed at online transaction websites, including but not limited to O2O , P2P, content communities and other websites. As we all know, there are many factors that affect conversion, and it is impossible to list them all. Based on previous work experience, we can analyze from three major perspectives: traffic channels, user marketing and website/APP experience. 

 These three factors are actually the relationship between external (channel traffic), internal (user marketing) and internal (website/APP experience). Channel traffic is what we get from outside the website. User marketing is a series of operations and marketing work we carry out for the users we have acquired or already have on our existing website. The website/APP experience can be optimized through internal product, design, engineering and other departments, and is also an internal factor.  2. Traffic Channels 

 The first factor is channel traffic, which is mainly divided into two steps: channel optimization and quantitative allocation to analyze how to improve conversion rate. What is "Preferred Channel"? When we are marketing or operating, we will choose multiple channels, among which there are good channels and bad channels. The quality of the channel is directly reflected in the conversion rate, which ultimately affects the website's revenue. "Optimizing channels" means that we should try our best to choose channels with good quality, abandon poor quality channels, and maximize the effect of a certain advertising budget. What is "quantitative allocation"? After completing the first step of channel optimization, suppose you get 10 channels with relatively good quality. Now the question arises: given a fixed budget, how should resources be allocated among the 10 channels? In the past, we allocated channel resources in a very subjective manner, relying more on experience or business understanding. Nowadays, we are more inclined to consider how to accurately allocate resources to different channels from a quantitative or mathematical perspective, combined with business understanding . 

 Case: Purchase conversion rate of an e-commerce website This is the purchase conversion rate of the entire e-commerce website. I used GrowingIO's [Funnel Analysis] function to obtain the purchase conversion situation of each step of the website. Users go from the home page to the list page, then to the detail page, and then to the shopping cart, guiding the final payment to success. At the same time, in the red box, there is an overall purchase conversion rate of 1.5%, which is actually not very high. Select [Dimension Comparison] in [Funnel Analysis] to compare the conversion functions of users coming from different channels. As shown in the figure below, the red box shows the comparison of conversion rates at each step between visit source 1 and visit source 2. 

 We can see that the overall conversion rates of these two channels, visit source 1 and visit source 2, are very low. At the same time, it was found that the traffic from access source 1 was very large. At this time, depending on our business judgment, there may be two situations: The first situation: the quality of this channel itself is relatively poor. The second case: maybe it is an auxiliary channel. The investment in this channel may not be much, but the amount is particularly large. For these two situations, we should solve the problems according to their categories. If the quality of this channel is relatively poor, but the volume is large, can we consider optimizing the delivery of this channel, such as advertising content, keywords, landing page design , etc., and observe for a period of time? If the quality of this channel is not bad, but it is just an auxiliary channel, then we can adopt a wait-and-see strategy at this time and observe its development trend for later optimization. After we have screened the channels, there are 10 relatively high-quality channels left. So how should we allocate resources among these 10 channels? The following is the traffic from different channels of the website monitored by GrowingIO: 

 Combined with business experience, we know that the customer matching accuracy of search engine channels (such as Baidu SEO and SEM ) is relatively good, and we should invest more. But how much should be invested specifically? We need to use mathematical methods to quantify the relationship between different channels and the overall conversion rate to obtain the optimal solution. 

 The top formula, conversion rate = F (channel 1_PV, channel 2_PV,..., channel n_PV), says that the final conversion rate is a function of the traffic of each channel. That is to say, we must first determine a functional relationship between the conversion rate and the traffic of each channel. So how do you determine this functional relationship? You can combine business practice or industry experience. Method 1: Linear Model The linear model is relatively simple and easy to understand. The fixed ratio between channel traffic and final conversion is represented by a straight line on the graph. Of course, this is a very extreme situation. Method 2: Time Series In another case, it will show a relationship with seasonal cycle fluctuations. Maybe in Q1 and Q2, they are on an upward trend, but may decline in Q3 and Q4. At the same time, his previous conversion rate will also have a relationship with our current conversion rate. The conversion rate t here refers to our current conversion rate. In the time series company, the conversion rate is t-1, and this t-1 conversion rate refers to the conversion rate of the previous cycle. After finding the relationship between channel traffic and total conversion rate, we next need to solve the value of this big F. This big F is our conversion rate. At the same time, it has a limiting condition which is our total cost M.  The final solution to the optimization problem is an optimized delivery combination, which is a set of coefficients. Mathematically speaking, it is a coefficient, but in actual business terms, it is a combination of different deliveries. 

 The above picture is a comparison of the effects before and after our channel launch. On the left is the resource allocation of different channels, and on the right is the final conversion rate. The final conversion rate (green on the right) of the delivery combination of all channels in 2014 (blue on the left) was relatively low; after a new round of channel optimization, the final conversion rate (orange on the right) of the delivery combination of all channels in 2015 (yellow on the left) increased significantly. This case illustrates that when resources are limited, the conversion rate can be effectively improved through channel optimization and quantitative allocation.  3. User Marketing  What is user marketing? In fact, it is a series of operational activities we carry out for users on our own website, including attracting new users, activation or awakening actions. Through these activities, our users repeatedly make purchases on our website. Moreover, such purchases are efficient, and such operations or marketing are precise. Only in this way can we achieve the goal of improving our overall purchase conversion rate. How to conduct user marketing? Step 1: Determine the business scenario and identify our target users by drawing user portraits. Step 2: Based on the first point, after we have found the target users, we need to carry out precision marketing, which is the so-called activation or awakening, so that these target users will make purchases on our website, thereby increasing the final conversion rate. How to create user portraits? One of the more experienced usages in the industry is to grade the user's value. When we talk about user value, this term is actually quite vague. How can we accurately grade this value? 

 We draw on the RFM model commonly used in the industry: R stands for Recency, which is the time from the last purchase to the present; F stands for Frequency, which refers to the user's purchase frequency; M stands for Monetary, which refers to the user's purchase amount. We can classify users based on these three dimensions and divide them into 8 categories. These 8 categories can cover the value of the user that we want to explain. For example, we can define users who have recently purchased or used the product with high frequency and high consumption as high-value users of our website, platform or APP. Through this division, we can divide our overall users into different levels. However, to understand this classification, we need to combine it with actual business. For example, suppose we are an e-commerce website and are holding a promotion to sell more expensive digital products. At this time, we need to find these high-value users and push some activities to them , rather than pushing them to all users of our website. The reason is that high-value customers are more likely to purchase our new digital products, and only by accurately pushing them can we save costs and improve efficiency. 

 In addition to RFM model classification, other classification methods are also available: 
  1. Classify members based on their attributes, such as the user's gender, city, device, number of logins, etc.
  2. Users are classified according to their activity level into unconverted members, new members, active members and dormant members. What are unconverted members? It refers to users who have registered but never made a purchase.
  3. According to the user's purchasing preferences and actual purchasing scenarios, classify him. At the same time, you can also give him points based on the user's sensitivity to the booking platform and promotion channel.
  4. There are also registered sources, such as PC/APP/H5. Suppose that most of the members on our website register through the APP. If we do some activation activities, we should focus on the APP instead of choosing ports such as PC or H5.
4. Website/APP Experience  Whether it is an O2O, P2P or content community website, some common and important experience issues will seriously affect the purchase conversion rate, such as: the smoothness of the payment process, whether the page is simple and easy to operate, whether the picture quality is clear, whether the search is accurate and matched... 
  1. The smoothness of the payment process. If the payment process is smooth, the possibility of user churn will be greatly reduced;
  2. The page is simple and easy to operate. For example, large websites such as Alibaba , JD.com or Ctrip have many buttons on the page. However, in fact, they sometimes affect the final conversion process because there are too many buttons and exits in the entire conversion process, which makes it easy for users to lose.
  3. Picture quality. For example, when we make a purchase on a website, it is easy for factors such as low image resolution, watermarks, and poor lighting to affect our judgment of the product, thus affecting the final purchase conversion;
  4. The exact match of the search, for example, if a user searches for mango, but the final result is dried mango or mango candy, etc., it does not actually accurately match the user's needs. As a result, the user experience is seriously affected, resulting in user loss due to unmet needs.
 Case: Conversion rate of an e-commerce website 

In the conversion funnel above, the last step, the conversion rate from clicking on the shopping cart to clicking on payment, is particularly low, but why is it so low? Why did the user click on the shopping cart and show purchase intention, but ultimately failed to pay? Ordinary data analysis products and rough funnels cannot find the reason. You can only see that the conversion rate at this step is low, but you don’t know why. As a result, product managers don’t know how to optimize, and marketing operations staff don’t know where to start making adjustments. We use the [User Segmentation] function to group all users who "enter the shopping cart but do not click to pay" to see what exactly happens to those who are lost at this step. 

 After grouping, let’s look at [User Detailed Check] (a powerful new feature launched by GrowingIO that can accurately check each user’s browsing, clicking, staying, typing and other behaviors on the website). We found a particularly interesting phenomenon: a user in this group opened a certain page, clicked Buy 1 in the shopping cart column, and then planned to check out. But then he found that he was not logged in, and then he entered the login information and found that it was unsuccessful. He then clicked "Forgot Password" and entered the password retrieval page. The key issue is that, according to normal understanding, after setting the password on the password retrieval page, you can log in normally. But this user bounced back and continued to forget the password and retrieve the password. How could this happen? Then we tried it ourselves and found that there was a BUG on the password change page, which meant that the password could not be changed. This meant that the user could not complete this step back and forth, which of course would eventually lead to user loss. In this way, by comparing funnels, segmenting users, and checking users in detail, data is tracked step by step, and the cause of the problem is finally accurately located. Only in this way can the product manager know where to make modifications. Otherwise, the product manager can only keep trying and making mistakes, making all kinds of guesses, and wasting time and energy on useless work. Summarize Think about how to improve the purchase conversion rate from three major dimensions (channel traffic, user marketing, and website/APP experience), then segment it through different dimensions, do a more in-depth analysis, and then combine it with the business, and finally use data to continuously drive business growth, growth, and growth.

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This article was written by @GrowingIO analyst Zhao Xiao and published by (APP Top Promotion). Reprinting this article must be approved by Top Promotion , and please attach the link to this article!

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