Contributed by: Ashwini Math
Data Science and Business Analytics is a growing industry. Read to know more about Ashwini Math’s journey with Great Learning’s PGP Data Science and Business Analytics Course in her own words.
I did MBA in Finance, and Marketing & I joined as Operations Executive with an E-commerce startup. I found out I was made for something else, and I was not happy with what I was doing. But I’m very grateful for everything as I got exposure to how jobs are designed. We must find what we truly want. I have always aspired to be a Techie, and when I came across the Great Learning program, I got a new ray of hope that it’s never too late to work on your dreams then one thing leads to another. During the course, I have realized there is a plethora of data that can be explored and analyzed to make a better impact on doing things.
I work in an e-commerce startup; I am Senior Executive – Order Management (Operations). In my company, nothing is automated—everything we have to update manually. We only use Excel and Google sheets. And I have applied basic EDA concepts to understand the data. Outliers helped me to focus on those vendors who are between the 2nd, third and fourth quartile. Then treat Outliers as Magnet vendors whose performance with our company is extremely high in terms of number of transactions, number of orders per day, and GMV that they are giving on a monthly, quarterly, and yearly basis.
I have made use of EDA to understand data, then applied Regression to examine the association of (categorical or continuous) independent variables and Time Series to understand the seasonality and trends.
I have to make monthly Sales reports to calculate GMV, which is KAM wise, region wise, Number of Activation. Total targets achieved. There were no proper regions given to KAMs. Using my data, they understood we must divide KAMs based on their performance and the number of activations achieved. I suggested that they be divided into four regions as Northeast, Northwest, Southeast, and Southwest. I used Python for EDA, Regression model, Tableau, KNIME, Time series model to come up with a solution.
My data helped them to focus on vendors that were non-transacting, who have done the least business with us in the last quarter. Our prime focus was to increase the number of activations. And we have reached to activate from more remote places. That was successful in achieving it.
I have suggested BDE’s focus on vendors who have great products due to remote areas they are unable to reach outside the box. Our BDE’s visited their retail shops and educated them on how to use apps and punch orders. This small activity has helped us gain a greater number of activations. And in the month of August, we have reached from 4 Cr to 11 Cr business and from 60 activations to 183 activations.
There was a huge impact on the organization in terms of the number of transactions made. Where cancellation of order was very minimal, because using my data had not only helped KAM’s, BDE’s, it has also helped our logistic and supply chain teams to deliver the stock within the TAT of 3 days. Our customers are happy customers now. They have turned from non-transacting to transacting retail businesses.
Lastly, this exercise has helped me gain a perspective about how we can turn data into useful, meaningful business solutions. I feel more confident suggesting any decisions now.
