When it comes to enterprise-level self-service analysis platforms, everyone will naturally think of Tableau, which is described in Garnter's latest BI platform Magic Quadrant.
“Tableau is a Leader in this Magic Quadrant. It offers a visual-based exploration experience that enables business users to access, prepare, analyze and present findings in their data.”
Tableau is the leader in this Magic Quadrant. It provides a vision-based exploration experience that enables business users to access, prepare, analyze, and present the findings in their data.
But Tableau is expensive. A user charges 70 US dollars a month, which is almost 6,000 yuan a year. In the current economic situation, it is really not cheap. Large and wealthy companies can buy genuine copies for a few analysts, but they cannot use them on a large scale.
In addition to being expensive, Tableau has the following disadvantages:
1. Habit problem, users are more familiar with Excel, and switching from Excel to Tableau has to learn too many new things.
2. Template reuse problem, a large number of Excel templates accumulated in daily work, with many formulas, can not be transferred to Tableau.
3. To share the problem, watch the report made by Tableau also cost money, 12 USD/month.
Why not use Excel? Many users report that Tableau is very much like an upgraded version of Excel's pivot table, except for richer graphics. The problem lies in Excel's weak database capabilities. Without a database, Excel can only provide static data and cannot display dynamic data. Excel's functions are greatly reduced.
Of course, Excel can also directly connect to the database through the user/password, but any enterprise with a bit of data security awareness cannot do this. For the reasons of Microsoft's overall product strategy, the power bi server can centrally control database connections.
Now, Smartbi, a leading domestic business intelligence brand, has launched Excel intelligent analysis products, which include centralized control data services, Excel plug-ins, plus Excel's own functions, which can completely replace Tableau as a self-service analysis platform for enterprises. The price of this set of products is much lower than Tableau, and it can be promoted on a large scale in the enterprise, truly "everyone is a data analyst".
The advantages of using Excel are as follows:
1. No longer rely on IT staff to provide data, business staff can handle the data themselves.
2. The user's Excel skills can be fully utilized, formulas/graphs/pivot tables, etc. The more you use Excel, the more skilled you become.
3. The company's original analysis template can continue to be reused.
4. Support more than 1 million pieces of data, with the help of server computing power, Excel can also handle very large data.
5. Support comprehensive data authority management and control.
6. Sharing is more convenient, excel files can be published as web links, and leaders can also see the analysis results on WeChat/DingTalk.
The following is a case to illustrate the use of Sematic software Smartbi Excel intelligent analysis:
Company A is a software company. At the beginning of 2019, it will analyze the sales data of the past 3 years and make a strategic plan for this year.
The sales data is as follows, there are 6 fields.
We use an iterative analysis process to gain insights into the data after rounds of analysis, continue to generate valuable derivative data, and finally form the analysis results, which are implemented as corporate management actions.
Analyzing ideas is the most important. Sematic's Smartbi analysis plug-in and Excel skills are good tools for ideas to be implemented.
Sematic software Smartbi intelligent analysis Excel plug-in can efficiently fetch data into Excel. Excel skills include Excel formulas, graphs, pivot tables, Power Query, etc.
Analysis ideas: The data can be analyzed from the following 4 angles.
The data connection is managed uniformly by the enterprise. After the data analyst installs the Excel intelligent analysis plug-in, log in to the Sematic software Smartbi intelligent analysis server, and then the data can be retrieved from the database to Excel.
industry analysis
Use the pivot table to get the number of contracts in each industry in the past 3 years. It can be seen that the number of education customers is small and the shrinkage is serious, and there is no need to list them separately. Can be merged into government industry.
Add derived fields and merge the education industry into the government industry.
The new pivot table.
Growth analysis
Using the perspective table to summarize the three-year contract amount, it can be seen that sales in 2018 have risen sharply. What is the reason for the growth? How about the unit price of new customers, old customers, and customers?
An old customer is a customer who makes a second purchase. The Minifs function can calculate the customer's first purchase year, and compare the customer's first purchase year with the contract purchase year to determine whether the customer is an old customer or a new customer.
It can be seen that the main source of growth is old customers, and the growth of new customers is not significant.
There is no change in the customer unit price.
Repurchase analysis
Using the pivot table, the first purchase year is the row and the contract year is the column. In 2016, 27 new customer contracts were obtained, the following year old customers contributed 5 contracts, and another year contributed 9 contracts; in 2017, 31 new contracts were obtained. The customer’s contract, the following year old customer contributed 13 contracts. The picture on the right shows the percentage of repurchase.
If calculated according to the amount, the contribution of old customers is higher. Overall, the repurchase rate of customers is very good.
Customer value analysis
RFM is a conventional customer value analysis model that divides customer groups through Recency, Frequency, and Monetary, and finds out the company's business focus.
Use the pivot table to summarize the range of customer unit price, and subjectively divide 5 value categories.
Add derived fields to get R-layer, F-layer, and M-layer.
Using the data perspective, it can be seen that the value of lost customers is not great, and loyal customers (more than 3 purchases) contribute the most.
The whole process of data analysis
All the original fields and derived fields will be updated at the same time when the data is refreshed by the Smart Analysis Excel plug-in.
Analysis conclusion:
Through the above data exploration, the following conclusions can be drawn:
1. The company has healthy operations, rapid sales growth and high repurchase rate.
2. The growth of new customers is slow and there are hidden worries.
3. To vigorously explore new customers, the breakthrough direction is the enterprise industry.
4. Continue to repurchase old customers.
Management actions:
In 2019, the company's business strategy can be adjusted as follows.
1. Cancel the Education Department and transfer relevant personnel to the Government Department.
2. Expand the organization of the corporate business department and significantly increase the sales target for 2019.
3. The management discusses the characteristics of repurchase companies and optimizes the sales strategies of old customers.