Inventory Data Analysis

Inventory data is a gold mine for information based on which organisations can make critical business decisions. The available data can often be quite huge and it has to be formatted and presented in a way decision makers can make sense of it. This is where data analysis comes into play. Data analysis essentially extracts useful insights into customer behaviour, product movement and other important indicators from the vast amount of data.

Elementary Analysis

An elementary analysis on the data itself can reveal a lot of useful information about sales trends and customer behaviour. The above plot shows the profit margins from top selling products of the company over the years. Rice, Sugar and Edible oil remain the top three grossers. Products like home care, packaged foods etc are starting to contribute more in 2020 mainly due to people being confined to their homes during the pandemic.

The two plots here,reveal a very interesting story. The first plot shows the sales trend and margins over the last couple of years. the sales and margin have been steadily increasing until March 2020 after which there is a significant dip in sales. The dip in sales coincides with the start of country wide lock down in India due to the Corvid-19 pandemic situation. But the story doesn’t end there.

The second chart reveals that the sales of rice has gone down during the lockdown period. Rice is one thing that people would like to stock during uncertain periods like these, but still its sales has dropped, why? The reason for this cannot be found with the data that we have in hand. A closer look at other sources brings to light the fact that government has issued free ration of rice to all the people of the state during lockdown and it has impacted the sales figures of rice. This also highlights the need to correlate other sources like new articles with the data to bring out facts we otherwise would miss.

Suggested action points

  • To overcome the sales dip due to lockdown, the company should give more focus on products that showing growing customer interest like homeware, packaged food etc
  • The company should gather information from news and other external sources to forecast trends and initiate strategies to counter changes.

Recency, Frequency, Monetary Analysis

RFM analysis provides many details about customer behaviour and it facilitates targeted action for different kinds of customers. RFM analysis is build around a customer’s recency of purchase (R), frequency of purchase (F)and gross monetary value of his purchases (M). Based on the values of R. F and M customers can be divided into different classes and a separate set of actions can be advised for each of these classes.

The below figure shows the customers based on RFM analysis on the sales data.

Suggested action points

  • Around 44% of the customers belong to either champions, loyal or potential loyal customers. It is important for the company to make sure that these 44% remain as customers.
  • It is also important to bring back the 23% hibernating customers which could make a potential 67% champion/loyal customers.

Technology road-map

Data analytics can reveal a lot of hidden information about the business and can lead to action plans for more efficient practices in the future. Besides these insights and action plans we also advice a detailed technology road map which would let the company execute the action plans in the most productive manner. Technology road map include the execution of the following tools.