Data Processing: Lesson $2$

Case Study: E$-$Commerce Company Optimizes Marketing Strategy Using Big Data

Analytics

Scenario: An e$-$commerce company, "ShopSmart," is facing challenges in increasing

customer engagement and sales. The company has collected vast amounts of data from

various sources, including website traffic, social media interactions, and purchase

histories. They want to use this data to optimize their marketing strategy and better target

their customers.

Objective: The goal of this case study is for students to understand the process of applying

Big Data analytics to solve a real$-$world business problem. The students will analyze how

ShopSmart can leverage Big Data to improve its marketing efforts and increase sales. protocols.

Integration: Combining data from various

sources into a unified system that

supports comprehensive analysis can be

complex and time$-$consuming.

Data Processing

Techniques:

Batch Processing: The processing of large

volumes of data at once, typically at

scheduled times $($e$.$g$.,$ overnight$).$ Tools

like Hadoop's MapReduce are often used

for batch processing in Big Data

environments.

Real$-$time Processing: Continuous

processing of data as it arrives, enabling

immediate analysis and response. Tools

like Apache Kafka and Spark Streaming are

used to handle streaming data in real$-$

time.

Data Cleaning:

Removing Duplicates: Ensuring that each

piece of data is unique to prevent

redundancy in analysis.

Correcting Errors: Identifying and

correcting inaccurate or inconsistent data

to ensure accuracy.

Handling Missing Data: Filling in missing

values or deciding how to handle

incomplete data, which is critical for

maintaining data integrity.

to create visual representations of data,

making it easier to identify trends,

patterns, and insights.

Data Analysis

Techniques:

Descriptive Analytics: Summarizing

historical data to understand what has

happened in the past. This includes basic

statistical analysis, such as calculating

averages, medians, and trends.

Predictive Analytics: Using statistical

models and machine learning algorithms

to forecast future trends based on

historical data. This can include predicting

customer behavior, market trends, or

potential risks.

Prescriptive Analytics: Providing

actionable recommendations based on

data analysis, often using optimization

and simulation techniques. This helps

organizations decide the best course of

action under various scenarios.

Tools:

$R$ and Python: Popular programming

languages used for statistical analysis,

machine learning, and data visualization.

They have extensive libraries and

frameworks for data analysis tasks.

SAS: A powerful software suite used for

advanced analytics, multivariate analysis,

business intelligence, and data

management.

Data Visualization Tools: Tools like

Tableau, Power BI$,$ and QlikView are used

Q$5$: Why is data processing a crucial step before analysis? What methods

can be used to clean and prepare the data for analysis?

Q$6$: How can ShopSmart ensure that the processed data is of high quality

and suitable for analysis?

Data Analysis:

Q$7$: Describe the types of data analysis techniques that ShopSmart can use

to segment its customers. How does customer segmentation benefit the

company?

Q$8$: How can predictive analytics be used by ShopSmart to forecast

customer behavior and product demand? Provide an example based on the

case study.

Please based on the picture that I attached for the answer. Don't use ai$,$ and explain it briefly. thank you