Week 4 work

This week chapter 5 was assigned to be read.

Chapter 5 explored data-driven marketing, predictive analysis, customer segmentation, supplying chain management, and risk management. Data-driven marketing Uses data from customers to make personalized marketing campaigns. An example would be a beauty company improving their email marketing by sending personalized emails. Predictive analysis uses historical data To predict what might happen in the future. For example, A grocery store could analyze its past sales and predict what kind of demand they have for certain products. Customer segmentation divides customers up based on different characteristics, like their age, or gender. An example of this could be a fashion organization grouping customers based on their interests, and creating personalized shopping experiences for them. Supply chain management uses data to track inventory levels, managed logistics, and forecast demand, which helps products be available when they’re needed, saving money for the company. A solar panel company using data to analyze their suppliers, production capacity, and shipping times could reduce delivery delays. Risk management was the final explored topic this chapter, it is used to identify and reduce risks that are potential in business operations and finances. An example might be a firm in investing could analyze their market trends and economic data in order to manage the risks and protect their clients.

Comments

Post a Comment

Popular posts from this blog

Week 1 - Sadie's Introduction Blog Post

     Hello! My name is Mercedez, but I also go by Sadie, and it is much easier to write "Sadie's Blog" rather than "Mercedez's Blog".  I enjoy watching all kinds of TV shows, and lots of movies too! I love spending time with my dog, Tommy, and shopping, but I am trying to work on being better with my money. I am looking forward to learning more about AI, and I'm hoping it'll be helpful to me in my future career! I don't have much experience with AI, so it's all exciting and new to me!
Read more

Week 2 work

This week chapters 1, and 2 in the "Discovering Data" were assigned this week. Chapter 1 of the book explains how analyzing, interpreting, and presenting data are essential skills needed for organizations, and individuals in an organizations should possess those skills for the organization to run well. If a person does possess those skills, they are likely in high demand. These skills are so important because of the data in the world, and the ability to use that data is important. Many different industries use data, some uses of it would be decision-making, market analysis, customer segmentation, risk management, and performance measurement. Since data is so important to so many different industries there have been jobs made focusing on data collection. Some of those jobs would be data analysis, data scientist, data engineers, and data intelligence analysts. These jobs focus on using data to improve efficiency, optimize operations, and improve growth within a business.   Chap...
Read more

Week 3 work

This week chapters 3 and 4 were assigned. Chapter 3 focused on understanding where data comes from, the types of data, and how it is managed. Data is sourced from internal or external sources. Internal sources are within the organization, like sales data, or employee data, while external sources are from outside of the organization, Like social media platforms, or research firms. Some ways that data is collected is with surveys and questionnaires, getting data from websites, sharing data through systems and applications, and collecting data through sensors. The quality of data matters. The accuracy, complete Ness, consistency, timelines, and relevance are all important to the quality of data collected.  Chapter 4 introduced data pipelines, warehousing, and modern data infrastructure. Data pipelines are series of steps, which move data to a data warehouse from its original source. The function of a pipeline is to extract data, change it into a usable, format, and load it into storag...
Read more