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Data Science Use Cases in Retail : Part-1

Data is the King now days and in retail I would say Data is King Maker. In this article we will see typical use cases of Data science in retail segment.

In retail, data helps in following areas:

  • Taking informed decision:

    1. Customer choice Prediction
    2. Price optimization
    3. Supply chain management
    4. Starting a new store
  • Understanding customer behavior:

    1. Customer sentiment analysis
    2. Merchandising via visual channels
  • Influencing customer decision:

    1. Via Recommendation engines
    2. Via visual channels

I am sure it all looks very straightforward things to do if someone wants to expand the retail business. At the same time from Data science perspective it takes lot of effort to implement these use cases. In this article we will discuss first use case on how Data science helps to take informed decisions for the retailers. In further articles we will take a deep dive into each of these use cases to have a deeper understanding. So let’s start!!

customer choice

Customer choice Prediction:

This takes into consideration: o Customer Transaction Data o Customer Basket Data o Comments/Previews o Likes/Dislikes


The analysis is generally done using rule mining algorithm. The insight information contributes towards: o Improving development strategies o Improving Marketing techniques o Improving efficiency of the selling efforts

Price optimization :

Price optimization takes into consideration: o Customer Buying pattern o Location o Competitor pricing o Season/Festival Price optimization tools helps to segment the customer behaviour with price changes and can trigger personal pricing in future.

Supply chain management :

How much inventory to store and what to store Powerful machine learning algorithms and data analysis platforms helps to : o Detect patterns o Define Correlations among the elements and supply chains. o Define optimal stock and inventory strategies via constantly adjusting and developing parameters

The analysis helps to identify: o Patterns of high demand o Develop strategies for emerging sales trends, optimize delivery and manage the stock implementing the data received.

Starting a new retail store:

For any business expansion is the key but how to identify where to start a new store. Data science again comes to rescue. The Data analysts explore: o Analyzing demographic customer data - The coincidences in ZIP code and location give a basis for understanding the potential of the market. o Location of other shops are taken into account. The algorithms find the solution by connecting all these points.



We will discuss about other use cases in next post.

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