Visualizations of the dataset for Forecasting using Tableau and Minitab


This is a snippet of a sample data visualization from a company whose data I processed and made the visualization for. the visualization that I did came from Tableau and there are 2 forecasting from Tableau and Minitab. I compared the forecasting results from the 2 software with the actual data that I had before.

There, it’s evident that the forecasting with Tableau closely aligns with the original data initially, but towards the end, there’s a larger gap compared to the average gap observed earlier.

On the other hand, using Minitab shows the opposite pattern compared to Tableau. Initially, it exhibits a larger gap than Tableau, but towards the end, it closely resembles the original data.

This is evidenced by the values of RMSE (Root Mean Square Error), MAPE (Mean Absolute Percentage Error), and MAE (Mean Absolute Error) between Tableau and Minitab.

As a model, Tableau forecasting performs significantly better than Minitab, as it exhibits lower values of RMSE, MAPE, and MAE. However, when compared to the actual data, it turns out that Minitab’s RMSE, MAPE, and MAE are superior.

Visualizations of Data Customer Survey using Tableau


This is a snippet of a sample data visualization from a company whose data I processed and made the visualization for. the visualization that I did came from Tableau about Customer Survey to get insights for RnD and Marketing division.


In the “where knew & bought” visualization,
I observe that many customers become aware of our product through social media and make purchases on the e-commerce platform Shopee.
Interestingly, some customers make purchases on Lazada after learning about the product from Shopee. Although there are also customers who discover the product through TikTok and purchase it on Shopee, TikTok is primarily a video platform, serving a similar purpose to Instagram.
Additionally, for customers who become aware of the product through relation, it appears they tend to make purchases on Shopee as well.


Moving on to the “consume per Month” visualization
It’s apparent that the majority of customers make fewer than 5 consume per month.


Regarding the “salary & spent for baby” visualization
It illustrates the monthly income of customers and their expenditures on baby products, including food, drinks, and other essentials.
Interestingly, some customers spend over 2 million with incomes ranging from 1 to 6 million on baby necessities. Assuming maximum income and minimum expenditure, with a 6 million income, customers are spending one-third of their income on baby needs.



In the “total transaction counts” visualization,
The “total transaction count” represents the total number of transactions within the span of one year.
we see that most surveyed customers engage in fewer than 5 transactions within a year.



Moving to “product most like,” among all products launched by the company,
two products stand out as favorites: Meat Mix Red Bean Cheese & Beef, and Meat Mix Salmon & Seaweed.


Finally, the “percentage spent from monthly baby stuff” visualization is a continuation of “salary & spent for baby,” depicting customers’ expenditure on our company’s products as a percentage of their total monthly baby-related expenses.

After obtaining the percentage of monthly expenditure on baby stuff from each respondent, I categorized them into four groups to analyze the distribution and majority of spending on our products among respondents’ monthly baby needs.
Originally, the data consisted of numerical values, and these percentages were obtained by dividing the expenditure on our products by the monthly baby-related expenditure of each respondent.



The visualization of the “Average Percentage Spent for us” depicts the mean percentage of expenditure by all respondents on our baby products monthly. This insight provides a comprehensive overview of the average commitment to our offerings within the baby product market.