In this era when traffic peaks and consumers have a lot of choice, e-commerce business can no longer be limited to a single e-commerce platform. Naturally, e-commerce analysis can't just focus on the data of a single platform.

If you only need the data of a single platform, then the official platform of the Amoy Department, Business Advisor, the official platform of the Byte Department, Douyin E-Commerce Compass, Alimama, and Huge Engine are completely sufficient. How to use the data of the platform.

If you involve multiple platforms and have analysis needs for industry data, business data, and e-commerce data, and want to use data analysis to empower your business and find growth points, it is necessary to learn e- commerce API .

Let me tell you how APIs break through the difficulties of e-commerce analysis one by one.

Challenges in e-commerce data analysis

The first problem: the e-commerce platform has no interface, and manually downloading data is a waste of time and has poor lag.

The current e-commerce analysis requires many data sources, such as industry data such as Amoy data, Baidu index data, Mojing market intelligence, etc., business system data such as Wangdiantong, ERP, OA, etc., of course, there are also major e-commerce companies Channel data such as Taobao, Douyin, JD.com, etc. The usual method is to manually download, find an operator to spend half a day to download data from various platforms, and then analyze and sort it out, which is inefficient and has a high error rate.

Using the Fine BI tool can realize automatic data retrieval, and the main method used is RPA+API. It can be understood that RPA is a robot. You can send an instruction to it, such as: according to the provided product and ID list of competing products, enter the business consultant platform competing product analysis to download the analysis report, and perform a chain calculation. Get the data you want within. The robot can work continuously with 100% correct rate, and it is safer than crawlers, so it will not cause the store to be blocked.

Complete analysis process after API access

①Business Overview

Visual form: dashboard + text map
Focus on indicators: sales (year-on-year, quarter-on-quarter, completion rate), gross profit (year-on-year, quarter-on-quarter, completion rate, gross profit margin), number of orders, number of customers, unit price of customers, joint rate, etc.

Taking the daily accumulation on December 30, 2018 as an example, the completion rate of sales of 29,000 yuan exceeded 95%, and the year-on-year increase was substantial, but the gross profit was only 610 yuan. The completion rate was less than 20%, and the year-on-year increase was also substantial. slide. Through the performance overview, we found that the discount amount (6,000 yuan) and the negative gross profit (minus 3,000 yuan) were too large. The main reason may be that the gross profit decreased due to the big promotion at the end of the year.

②Analysis of regional performance:

Visualization form: map
Focus on indicators: sales, gross profit margin, performance distribution, regional manager performance ranking, etc.

Taking the daily accumulation on December 30, 2018 as an example, it can be seen that there was no sales in Southwest China on that day, and the highest sales in North China was 11,000 yuan, a year-on-year increase of 187.9%, of which Hebei accounted for the most 70.1%. From the performance overview, we know that the gross profit is low and the negative gross profit is high. From the map, we find that the gross profit in East China is -1300 yuan, and the negative gross profit is close to half of the total negative gross profit. The sales staff in East China may cut prices to promote sales in order to achieve the year-end target .

Due to laws and regulations, the map was dropped to the mosaic

③Performance trend in recent days/months/years:

Visualization form : line graph, histogram
Focus on indicators : sales, passenger flow, customer unit price, joint rate, etc.

Taking the daily accumulation on December 30, 2018 as an example, the sales volume on that day exceeded the average value, but the gross profit was lower than the average value. The main reason was that the negative gross profit amount was relatively high, which was much higher than the average value. The passenger flow on that day was the largest in the past 30 days, but the unit price per customer was far lower than the average, which may be due to promotions. The return rate on the day is not high at only 6%, which is far below the average.

④Order and customer analysis:

Visualization form: pie chart, histogram
Focus on indicators: the proportion of mailing methods, the proportion of top 10 customers in sales, etc.

Taking the daily accumulation on December 30, 2018 as an example, 38.3% of the sales on that day came from products worth 500-1,000 yuan, and more than half of the orders were shipped by standard mail. Among them, consumers accounted for the most sales and orders. Most of the top ten are consumers, and Hong Qiang of the company type has the highest spending amount.

⑤Member analysis:

Visualization form: RFM model, Boston matrix, pie chart, grouping table
Focus on indicators: per customer sales, member sales, etc.

a) RFM analysis:

The number and proportion of users under each category of RFM are displayed in a rectangular tree diagram, the sales volume and proportion of each category are displayed in a pie chart, and the ranking of consumption per customer of each category is displayed in a bar chart.

From the figure below, it can be seen that the number of retained customers is the largest in general, accounting for 21.1%; the sales of important customers are the highest, accounting for nearly 30%; the highest sales per customer are important customers and important value customers, respectively 40,000 yuan and 39,000 yuan.

b) Price Sensitivity Analysis:

Analyze customers' sensitivity to price through the Boston matrix (the size of the bubble shows the size of the sales), and the pie chart shows the sales and number of members in each quadrant. Quadrant 1: High discount, high price Quadrant 2: Low discount, high price Quadrant 3: High discount, low price Quadrant 4: Low discount, low price

From the figure below, it can be seen that the second quadrant has the largest number of members, close to 40%, and sales accounted for more than 50%. Followed by the first quadrant, the number of members accounted for 22.4%, sales accounted for 25.8%. The common feature of these two quadrants is that they are not sensitive to high-priced products. The difference is that the first quadrant likes discounted products, and the second quadrant is not sensitive to discounts. Sales staff can formulate their own sales strategies according to customer characteristics.

c) Details of member characteristics: The group table displays the main characteristics of members, and the desired members are directly screened out according to the above analysis results.

⑥Category analysis:

Visualization form: rectangular figure diagram, pie chart, schedule
Focus on indicators: sales, sales volume, gross profit margin, return rate, etc.

The performance distribution of each category is displayed through a rectangular tree diagram, the pie chart shows the proportion of category sales, and the group table shows the top 10 products and returned products. Taking the daily accumulation on December 30, 2018 as an example, the highest sales of office supplies was 12,000 yuan (42.6%), of which appliances accounted for the highest 59.6% of office supplies. The top two sales products are Hoover microwave oven and Samsung signal booster, respectively 4313 yuan and 3189 yuan. There are three products that have been returned, and the return rate is 100%.

⑦Analysis and summary

In 2018, sales exceeded RMB 5 million, and the target completion rate was 92.6%. In recent years, sales have increased year by year, but negative gross profit and return rate have also increased year by year. The company needs to pay attention to find specific reasons to improve operations.

The sales share of East China and South China accounted for more than 50%, and the regional distribution is uneven. It is necessary to strengthen the market share of other regions. More than half of the sales come from individual consumers, and the distribution of sales in various categories is relatively even.