Important factors in cross-border e-commerce data analysis:
1. Cross-border e-commerce data analysis requires business sensitivity
A business-sensitive data analyst knows what kind of data to use to achieve the company’s goals. For example, when Lekutian competed with Taobao, they focused not on transaction volume, but on traffic: how many new sellers came in every day and how many things were sold. Because the core of competition at this stage is popularity, not actual transaction volume. For another example, a B company that has just entered the market and a B2B company that has occupied most of the market have different goals. The former values traffic and popularity, while the latter does not value traffic much, but rather transaction conversion rate and return rate.
2. E-commerce website conversion rate is the key, and return on investment is the ultimate goal
The purpose of the cross-border e-commerce B2B website platform is to serve enterprises, reduce the market sales costs of sellers, thereby reducing transaction costs and increasing order profits. Therefore, for e-commerce websites, conversion rate is the key, and return on investment is the ultimate goal. Return on investment (ROI) refers to the value that should be returned through investment. It covers the profit target of the enterprise, also known as accounting rate of return and investment profit rate. ROI is often time-sensitive – the return is usually based on certain specific years. The calculation formula of ROI is as follows: ROI = annual profit or average annual profit / total investment X100% 3. Setting of e-commerce data analysis measurement indicators Indicators can allow us to better understand the status of operations from the perspective of data quantification. Visits, page views, conversion rate, etc. are indicators of operation supervision; website analysis can have many different indicators depending on the website goals and website customers. Commonly used website analysis indicators include content indicators and business indicators. Content indicators refer to indicators that measure visitor activities, and business indicators refer to indicators that measure the conversion of visitor activities into commercial profits. E-commerce data can be divided into two categories: front-end behavioral data and back-end business data. Front-end behavioral data refers to data that reflects user behavior, such as visits, page views, click streams, and in-site searches; back-end business data includes transaction volume, return on investment, and full life cycle management.
4. Analysis of the reasons for abnormal changes in certain indicators
The abnormal changes in certain indicators of the website are an objective reflection of some changes in the external market, and the data analysts of the website must pay more attention to it. For example, if the number of page views decreases (abnormal), then we need to analyze whether the user’s search source decreases or the direct visit decreases. If the search decreases, we need to observe the keywords and search engines used by users.
5. Use data to analyze user behavior habits
Data analysis can be used to speculate on the user’s psychology and some habits, so as to grasp the user’s needs more accurately, which can be achieved through voting surveys and question submission. Of course, it is inevitable to use data integration analysis, and then weigh the pros and cons, improve the user experience and formulate some basic product positioning and activities.
Website data analysis generally includes two levels: First, for products, it mainly focuses on the closed path analysis of how the product operates, and whether the product’s clicks are smooth and the function display is perfect. Second, study the customer’s visit focus and tap into the customer’s potential needs. If it is a transaction-oriented cross-border e-commerce website, it is necessary to study how to efficiently facilitate transactions and whether joint orders can appear.
6. Customer purchase behavior analysis
When a user makes a purchase on a cross-border e-commerce website, he or she will be transformed from a potential customer to a valuable customer of the website. Cross-border e-commerce websites generally store the user’s transaction information, including purchase time, purchased goods, purchase quantity, payment amount, etc. in their own database. Enterprises can analyze their transaction behavior based on the website’s operating data to estimate the value of each user and the possibility of extended marketing for each user.
7. Cross-border e-commerce data analysis should focus on practical experience
Cross-border e-commerce data analysis is more about practical experience. The essence of website analysis is to understand user needs and behaviors, develop functions and services with good user experience, formulate extended marketing strategies and promotion services with additional functions, etc.