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Electronics Retail & eCommerce

Electronics Retail & eCommerce. Data Analysis Project Diana Amador. What do you see?. If you can visualize large amounts of data in a compact way, you get the big picture!. What is Data Analysis ?. It is a process of inspecting, cleaning, transforming, and modeling data with the goal of:

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Electronics Retail & eCommerce

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  1. Electronics Retail & eCommerce Data Analysis Project Diana Amador

  2. What do you see? If you can visualize large amounts of data in a compact way, you get the big picture!

  3. What is Data Analysis ? • It is a process of inspecting, cleaning, transforming, and modeling data with the goal of: • discovering useful information (patterns) • suggesting conclusions • supporting decision-making.

  4. Where do we start? • Do customers in different regions spend more per transaction? • Which regions spend the most/least? • Are there differences in the age of customers between regions? • Can we predict the amount a customer will spend per transaction based on other data we have collected about that customer? • Is there any correlation between age of a customer and if the transaction was made online or in the store?

  5. How do we do it? • WEKA (pronounced Weh-Kuh) is a collection of statistical and visualization tools for data analysis. • Heuristics is a technique for solving a problem more quickly when classic methods are too slow, or for finding an approximate solution.

  6. Do customers in different regions spend more per transaction? 1.East = Blue 2.West = Rojo 3.South = Light Blue 4.Central = Gray Region 3 & 4 spend more per transaction Region 2 Region 1

  7. Which regions spend the most/least? • Methodology: WEKA Visualization • Observations: • Customers in the Central and South Regions (Gray/4 & Light Blue/3) are the ones who spend the largest amount per transaction. • Customers in the East Region (Blue/1) are the ones who spend the least amount in total. • Customers in the West Region (Red/2) spend the least per transaction. • Region 4 spends the most.

  8. Are there differences in the age of customers between regions? Only Region 2 > 51 24 < customer age <51 No Region 2

  9. Can we predict the amount a customer will spend per transaction based on other data we have collected about that customer? • Observations: • In-store/online and region attributes don’t have a significant impact on the amount spent or the number of products a customer buys. • Age has the largest impact in determining the amount spent. • We can predict the amount a customer will spend based on his age, region and how he buys (online/in-store)

  10. Is there any correlation between age of a customer and if the transaction was made online or in the store? Age groups that prefer in-store buying > 51 Age groups that prefer online < 51 Age Groups

  11. Online/in-store transactions by Region Region 1 / in-store Region 2/ online

  12. Is there any correlation between age of a customer and if the transaction was made online or in the store? • Observations: • The oldest age group buys exclusively in-store • Age groups <51 buy more online • Age groups > 51 and <78 buy more in-store • Region 2 buys exclusively online

  13. Recommedations • Developing age-target marketing strategies will have the highest growth impact on sales on every region. • Growth potential is perceived for online/in-store buying patterns for certain regions; • Developing an age-target/online marketing strategy would provide the best results for the ecommerce team.

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