In-Depth Analysis & Data Driven Decision

In-Depth Analysis : In depth analysis is when you explore a topic to its full extent. This means going beyond justifying your research problem and exploring the detail in depth.

Data-Driven Decision making (or DDDM) is the process of making organizational decisions based on actual data rather than intuition or observation alone.

In this article. I have explained how to perform In-Depth analysis & make data driven decision of it.

Scenario : Company ABC is having 100 sellers in their seller platform and they would like to move towards profitable in the coming fiscal year. To reach there they need seller modeling which should shows the seller business overview. From this business overview what are the data driven decision should be taken to make company profitable.

Step 1: The following visualization shows the number of sellers required to reach the desired percentage of sales. Here we can notice that as of now we need 60 sellers to reach 84.29% of our sales rest 40 sellers are accounts for 15.71% sales.

Total Sales = 10324574 60 Sellers Sales accounts for 8702633

Which is equal to 84.29%

Step 2: Before doing further analysis of these 60 sellers. Lets analyze the remaining 40 sellers business overview which accounts for 15.41%.

From the above visualization we can notice that many of these sellers fulfillment rate is below 90% which states that customer are keen of purchasing there products but some of the products are not fulfilling by the sellers. Plus some of the sellers pickup cost is too high which requires some immediate actions to reduce it further down.

From the above In depth analysis we identified that the following 3 sellers are scheduled 2 pick-ups a day hence the pick-up cost per item is more for these sellers.

Based on this In depth analysis, we have taken the data driven decision to send 1 pickup a day.

Step 3 : Sellers Top Category Analysis : (This analysis provide us an information of what is the percentage of sellers Top category contribution to the overall sales of that category).

The following visualization shows how the sellers Top category performs against the total sales of that category.

* This check is very helpful while disabling the sellers.

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Step 4: Conclusion : Based on the above In-depth analysis we can use some threshold to disable the sellers .

> Sales contribution above 85th percentile > Fulfillment Rate is below 80 > Profit ratio nil > Top category contribution is below 5%.

Sellers who are falling under this criteria, we can run some different techniques to move them towards profitable. However in this article our main motto was to show profit in the next fiscal year.

In the next articles, I will explain the some more examples with a sample data. And the data which I have presented above are assumed values.