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Explore data analysis in electronics retail through inspecting, cleaning, and modeling data to discover patterns, support decision-making, and predict customer behavior. Gain insights into customer spending habits, age demographics, and online vs. in-store preferences in different regions using WEKA tools and visualization techniques. Discover key correlations between customer age, transaction types, and regional trends, and leverage these insights to develop targeted marketing strategies for improved sales and growth in the eCommerce sector.
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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: • discovering useful information (patterns) • suggesting conclusions • supporting decision-making.
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?
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.
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
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.
Are there differences in the age of customers between regions? Only Region 2 > 51 24 < customer age <51 No Region 2
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)
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
Online/in-store transactions by Region Region 1 / in-store Region 2/ online
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
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.