7 Steps To Make A Quick Sales Report With Sidetable
Panda Sidetable can upgrade your sales report with just a few Python lines
Besides Google Data Studio and Google Sheets, Python is also one of the best tools to analyze and visualize your sales insights.
It’s free of charge. If you have basic knowledge of programming language, it’s quite easy to handle data fast and quick.
Pandas is one of the software libraries which is written in Python. The library is the basis for data analysis and manipulation. Its name originates from an econometrics term called ” panel data”, which contain observations over multiple periods for the same individuals.
Pandas offer high-performance, user-friendly and intuitive data structures and analysis tools
Sidetable, a new pandas utility library was introduced last month, can help you do pre-processing your e-commerce sales data or marketing data for missing values, frequency counts, normalized parameters, subtotals, and more.
Steps are following,
- pip Sidetable
Firstly we need to install with pip Sidetable for your notebook
pip install Sidetable
2. Import library and read data
Import functions from python file into Jupyter notebook
Check your data’s size with “.shape” function
The dataset contains 523684 rows with 4 columns. Features include product_id, city_id, and orders. Let’s see how many unique product ID in the dataset.
3. Find out missing value
Previously if you want to find out any missing data, you need to write through arithmetic operations between pandas objects for the missing percentage.
With the usage of “missing” function, we can build a simple missing value table with count and ratios as follow
Luckily, there are no missing values in this dataset
4. Check the number of unique products and sales location
Let’s find out how many unique products and cities in this dataset
5. Top 10 markets
Let’s check the top 10 city that generate most sales
If you want to visualize it, you can use the following coding line
With visualization, it’s much easier to identify your top sales channels that you can focus on.
Let check the ratio of each city with using normalized parameter of value_counts
6. Get the frequency ratios with normalized parameters
Almost 11% of the sales come from the city no. 13. Focusing those resources on those top cities are vital to drive profitability for the whole business.
Up until now, we have been looking top cites that generate most sales revenue, and what is more interesting is the top 5 products that generate most of the sales
7. Find out the best 5 products with cumulative counts and frequency
This view gives us insight that the top 5 products, product id #7025,#6091, #5034, #3576, and #2323 constitute a cumulative 45% of the sales.
According to the above insights, you can draw conclusions for
- your future product planning at the physical store
- your future store promo
- improving product recommendation engine on your e-commerce site.
How can you use Sidetable for your marketing analytics?
Python is not only useful for data scientists but also marketers can better understand their top clients, top products, top sales channels, and top strategy.
1. eCommerce Site: We can use frequency tables to analyze the top 5 products, top pricing strategy or top sales channels, improving product recommendation engine, upselling, and cross-selling strategy.
2. PPC campaign: We can use it for finding top keywords that drive traffic, or filter out the lowest impression share or search lost IS rank and put priority to look at, or dig up the highest ad spend that you should alert on
3. Your sales funnels campaign: We can use it for finding top organic keywords that you can use on for your blog or PPC campaign, or find the most relevant webpage that can drive more relevant leads
I hope you have some basic ideas of how Sidetable can help to enrich your analytic report. If you have more ideas, email me for further discussions.
