Watch this tutorial on Marketing and Retail Analytics! Marketing and retail involve a lot of decision making relating to supply chain movement, inventory, customer needs, supply and demand, sales, etc. which in turn depends on analysing patterns of already existing trends in the industry. Marketing and retail decisions have to be well studied and strategized to be able to return extra-ordinary results, which is only possible when the existing data is well analyzed with a thorough statistic approach.
Great Learning brings you this tutorial on Marketing and Retail Analytics to help you understand exactly this and getting started on the journey to learn about it well. This video starts with an overview of marketing, followed by looking at important marketing terminologies. Following this, we will carry out RFM analyses using Tableau and KNIME. We then look at concepts including Cluster Analysis, Logistic Regression, and Churn Rate Prediction using Logistic Regression and Decision Tree and Random Forest. Finally, we look at Market Basket Analysis and CLV Model. This video teaches Marketing and Retail Analytics and its concepts with a variety of demonstrations & examples.
Affiliate Marketing Tutorial for Beginners with Google Ads (Low Budget)
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A famous general is thought to have said, “A good sketch is better than a long speech.” That advice may have come from the battlefield, but it’s applicable in lots of other areas — including data science. “Sketching” out our data by visualizing it using ggplot2 in R is more impactful than simply describing the trends we find.
This is why we visualize data. We visualize data because it’s easier to learn from something that we can see rather than read. And thankfully for data analysts and data scientists who use R, there’s a tidyverse package called ggplot2 that makes data visualization a snap!
In this blog post, we’ll learn how to take some data and produce a visualization using R. To work through it, it’s best if you already have an understanding of R programming syntax, but you don’t need to be an expert or have any prior experience working with ggplot2
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In this tutorial we’ll learn how to begin programming with R using RStudio. We’ll install R, and RStudio RStudio, an extremely popular development environment for R. We’ll learn the key RStudio features in order to start programming in R on our own.
If you already know how to use RStudio and want to learn some tips, tricks, and shortcuts, check out this Dataquest blog post.
[tidyverse](https://www.dataquest.io/blog/tutorial-getting-started-with-r-and-rstudio/#tve-jump-173bb264c2b)Packages into Memory
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What exactly is clean data? Clean data is accurate, complete, and in a format that is ready to analyze. Characteristics of clean data include data that are:
Common symptoms of messy data include data that contain:
In this blog post, we will work with five property-sales datasets that are publicly available on the New York City Department of Finance Rolling Sales Data website. We encourage you to download the datasets and follow along! Each file contains one year of real estate sales data for one of New York City’s five boroughs. We will work with the following Microsoft Excel files:
As we work through this blog post, imagine that you are helping a friend launch their home-inspection business in New York City. You offer to help them by analyzing the data to better understand the real-estate market. But you realize that before you can analyze the data in R, you will need to diagnose and clean it first. And before you can diagnose the data, you will need to load it into R!
Benefits of using tidyverse tools are often evident in the data-loading process. In many cases, the tidyverse package
readxl will clean some data for you as Microsoft Excel data is loaded into R. If you are working with CSV data, the tidyverse
readr package function
read_csv() is the function to use (we’ll cover that later).
Let’s look at an example. Here’s how the Excel file for the Brooklyn borough looks:
The Brooklyn Excel file
Now let’s load the Brooklyn dataset into R from an Excel file. We’ll use the
readxlpackage. We specify the function argument
skip = 4 because the row that we want to use as the header (i.e. column names) is actually row 5. We can ignore the first four rows entirely and load the data into R beginning at row 5. Here’s the code:
library(readxl) # Load Excel files brooklyn <- read_excel("rollingsales_brooklyn.xls", skip = 4)
Note we saved this dataset with the variable name
brooklyn for future use.
The tidyverse offers a user-friendly way to view this data with the
glimpse() function that is part of the
tibble package. To use this package, we will need to load it for use in our current session. But rather than loading this package alone, we can load many of the tidyverse packages at one time. If you do not have the tidyverse collection of packages, install it on your machine using the following command in your R or R Studio session:
Once the package is installed, load it to memory:
tidyverse is loaded into memory, take a “glimpse” of the Brooklyn dataset:
glimpse(brooklyn) ## Observations: 20,185 ## Variables: 21 ## $ BOROUGH <chr> "3", "3", "3", "3", "3", "3", "… ## $ NEIGHBORHOOD <chr> "BATH BEACH", "BATH BEACH", "BA… ## $ `BUILDING CLASS CATEGORY` <chr> "01 ONE FAMILY DWELLINGS", "01 … ## $ `TAX CLASS AT PRESENT` <chr> "1", "1", "1", "1", "1", "1", "… ## $ BLOCK <dbl> 6359, 6360, 6364, 6367, 6371, 6… ## $ LOT <dbl> 70, 48, 74, 24, 19, 32, 65, 20,… ## $ `EASE-MENT` <lgl> NA, NA, NA, NA, NA, NA, NA, NA,… ## $ `BUILDING CLASS AT PRESENT` <chr> "S1", "A5", "A5", "A9", "A9", "… ## $ ADDRESS <chr> "8684 15TH AVENUE", "14 BAY 10T… ## $ `APARTMENT NUMBER` <chr> NA, NA, NA, NA, NA, NA, NA, NA,… ## $ `ZIP CODE` <dbl> 11228, 11228, 11214, 11214, 112… ## $ `RESIDENTIAL UNITS` <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1… ## $ `COMMERCIAL UNITS` <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0… ## $ `TOTAL UNITS` <dbl> 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1… ## $ `LAND SQUARE FEET` <dbl> 1933, 2513, 2492, 1571, 2320, 3… ## $ `GROSS SQUARE FEET` <dbl> 4080, 1428, 972, 1456, 1566, 22… ## $ `YEAR BUILT` <dbl> 1930, 1930, 1950, 1935, 1930, 1… ## $ `TAX CLASS AT TIME OF SALE` <chr> "1", "1", "1", "1", "1", "1", "… ## $ `BUILDING CLASS AT TIME OF SALE` <chr> "S1", "A5", "A5", "A9", "A9", "… ## $ `SALE PRICE` <dbl> 1300000, 849000, 0, 830000, 0, … ## $ `SALE DATE` <dttm> 2020-04-28, 2020-03-18, 2019-0…
glimpse() function provides a user-friendly way to view the column names and data types for all columns, or variables, in the data frame. With this function, we are also able to view the first few observations in the data frame. This data frame has 20,185 observations, or property sales records. And there are 21 variables, or columns.
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“What is the day-to-day really, truly like in marketing analytics?”. I’m over halfway through my summer internship as a marketing automation and analytics intern at a company in the Dallas-Fort Worth area. I’ve had a lot of my fellow peers ask me what it’s really like because many of them had their internships cancelled or moved to an online format.
The skills I’ve acquired during this internship have been amazing! I did a lot of research beforehand however, and honestly didn’t find that much information on what it’s like to really be in marketing analytics during the day-to-day operations. I’ve enjoyed talking about my experience with my peers, however, I wanted to put my experience on a platform where more people who are curious about what it is like can see for themselves, and show what it is like from the very beginning to the very end of my day. So, let’s get ready to wake up!
Disclaimer: I am not a full time marketing analytics professional, I am simply relaying my observations of what I did and saw. This is not meant to teach you all the skills and tools used in marketing analytics, as I am still learning. For this information, please check out Towards Data Science. If you are interested in what it’s like to be in a marketing analytics intern or entry-level role, keep reading!
I typically will wake up around 6:15 AM every morning before the workday. This gives me about an hour to read Medium articles from other amazing writers, and gives me some things to learn in small, manageable chunks! Other than the basic morning routine that we all do, I also make a cup of coffee, because this is a role that requires you to think critically most of the day, and I promise you will get tired at some point! After some coffee, I get changed, pack my bag, and head to work!
I walk through the doors, find my way to my desk, and get ready for the work day! Here are a couple things that I do at the very beginning of the day:
In the midst of COVID-19, email and video calls are now the primary method of communication, even while in the office. We are required to wear masks and social distance, but we also try our best to stick to email and video calls if at all possible. During the summer, interns are given two major projects for the whole summer, one in teams where you present a solution to a problem that the executive team gives you, and one where you do a project on your own and present it to your department, which in my case is marketing. I also always try to spend some time with the marketing analyst, and watch him work through a problem and how he approaches it.
After I’m finished getting ready for the day, I will typically spend some time practicing my Python and analytical skills by applying them to a dataset that the marketing analyst gives me. This is not meant for a client or stakeholder, but is rather a way to solidify what I’ve learned from the marketing analyst that morning. I might whip up a linear regression model using sklearn, try to make my data cleaning more efficient with Pandas, or create quick visualizations with matplotlib. What I’ve noticed is that it doesn’t really matter how you get the job done. It is perfectly acceptable to have Stack Overflow opened on my left screen and a Jupyter Notebook on my right. I have found that analysts are always working on so many different projects, that knowing how to be efficient in your code is the skill to refine as you move through your internship or entry-level position.
Normally around the middle of the morning, all of the interns will meet via a Zoom call, or a very large conference room that allows for social distancing, and will listen to different speakers from within the company. These speakers are typically Vice Presidents or Senior Vice Presidents, and will talk about their experiences, offer their advice, and allow time for questions. For an entry-level position, the next part of the day would normally start around this time!
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