You can sign up for additional subscriptions at any time. item Food item. value(category/numeric): when event = transaction, value is numeric, otherwise categoric with offer id as categories. of our customers during data exploration. All about machines, humans, and the links between them. Coffee shop and cafe industry in the U.S. Coffee & snack shop industry employee count in the U.S. 2012-2022, Wages of fast food and counter workers in the U.S. 2021, by percentile distribution, Most popular U.S. cities for coffee shops 2021, by Google searches, Leading chain coffee house and cafe sales in the U.S. 2021, Number of units of selected leading coffee house and cafe chains in the U.S. 2021, Bakery cafe chains with the highest systemwide sales in the U.S. 2021, Selected top bakery cafe chains ranked by units in the U.S. 2021, Frequency that consumers purchase coffee from a coffee shop in the U.S. 2022, Coffee consumption from takeaway/ at cafs in the U.S. 2021, by generation, Average amount spent on coffee per month by U.S. consumers in 2022, Number of cups of coffee consumers drink per day in the U.S. 2022, Frequency consumers drink coffee in the U.S. 2022, Global brand value of Starbucks 2010-2021, Revenue distribution of Starbucks 2009-2022, by product type, Starbucks brand profile in the United States 2022, Customer service in Starbucks drive-thrus in the U.S. 2021, U.S. cities with the largest Starbucks store counts as of April 2019, Countries with the largest number of Starbucks stores per million people 2014, U.S. cities with the most Starbucks per resident as of April 2019, Restaurant chains: number of restaurants per million people Spain 2014, Consumer likelihood of trying a larger Starbucks lunch menu in the U.S. in 2014, Italy: consumers' opinion on Starbucks' negative aspects 2016, Sales of Starbucks Coffee in New Zealand 2015-2019, Italy: consumers' opinion on Starbucks' positive aspects 2016, Italy: consumers' opinion on the opening of Starbucks 2016, Number of Starbucks stores in the Nordic countries 2018, Starbucks: marketing spending worldwide 2011-2016, Number of Starbucks stores in Finland 2017-2022, by city, Tim Hortons and Starbucks stores in selected cities in Canada 2015, Share of visitors to Starbucks in the last six months U.S. 2016, by ethnicity, Visit frequency of non-app users to Starbucks in the U.S. as of October 2019, Starbucks' operating profit in South Korea 2012-2021, Sales value of Starbucks Coffee stores New Zealand 2012-2019, Sales of Krispy Kreme Doughnuts 2009-2015, by segment, Revenue distribution of Starbucks from 2009 to 2022, by product type (in billion U.S. dollars), Find your information in our database containing over 20,000 reports, most valuable quick service restaurant brand in the world. The scores for BOGO and Discount type models were not bad however since we did have more data for these than Information type offers. As a part of Udacity's Data Science nano-degree program, I was fortunate enough to have a look at Starbucks ' sales data. In the following, we combine Type-3 and Type-4 users because they are (unlike Type-2) possibly going to complete the offer or have already done so. The data is collected via Starbucks rewards mobile apps and the offers were sent out once every few days to the users of the mobile app. One caveat, given by Udacity drawn my attention. Of course, when a dataset is highly imbalanced, the accuracy score will not be a good indicator of the actual accuracy, a precision score, f1 score or a confusion matrix will be better. However, it is worth noticing that BOGO offer has a much greater chance to be viewed or seen by customers. You can email the site owner to let them know you were blocked. Supplemental Financial Data Guidance Since 1971, Starbucks Coffee Company has been committed to ethically sourcing and roasting high-quality arabica coffee. Tried different types of RF classification. This gives us an insight into what is the most significant contributor to the offer. Updated 3 years ago We analyze problems on Azerbaijan online marketplace. In our Data Analysis, we answered the three questions that we set out to explore with the Starbucks Transactions dataset. The re-geocoded . Database Management Systems Project Report, Data and database administration(database). This is a slight improvement on the previous attempts. dollars)." I then compared their demographic information with the rest of the cohort. Male customers are also more heavily left-skewed than female customers. Starbucks Coffee Company - Store Counts by Market (U.S. Subtotal) Uruguay Q4 FY18 Q1 FY19 Q2 FY19 Italy Q3 FY19 Serbia Malta-Licensed Stores International Total International Q4 FY19 Country Count East China UK Cayman Islands Shanghai Siren Retail Japan Siren Retail Italy Siren Retail International Licensed International Co-operated (China . It generates the majority of its revenues from the sale of beverages, which mostly consist of coffee beverages. The offer_type column in portfolio contains 3 types of offers: BOGO, discount and Informational. Profit from the additional features of your individual account. So my new dataset had the following columns: Also, I changed the null gender to Unknown to make it a newfeature. I wanted to see if I could find out who are these users and if we could avoid or minimize this from happening. calories Calories. time(numeric): 0 is the start of the experiment. But, Discount offers were completed more. Data visualization: Visualization of the data is an important part of the whole data analysis process and here along with seaborn we will be also discussing the Plotly library. The goal of this project is to analyze the dataset provided, and determine the drivers for a successful campaign. We also do brief k-means analysis before. You only have access to basic statistics. We will discuss this at the end of this blog. Perhaps, more data is required to get a better model. To answer the first question: What is the spending pattern based on offer type and demographics? Other uncategorized cookies are those that are being analyzed and have not been classified into a category as yet. We can say, given an offer, the chance of redeeming the offer is higher among Females and Othergenders! An offer can be merely an advertisement for a drink or an actual offer such as a discount or BOGO (buy one get one free). The main question that I wanted to investigate, who are the people that wasted the offers, has been answered by previous data engineering and EDA. The profile.json data is the information of 17000 unique people. Firstly, I merged the portfolio.json, profile.json, and transcript.json files to add the demographic information and offer information for better visualization. Interactive chart of historical daily coffee prices back to 1969. Jul 2015 - Dec 20172 years 6 months. Market & Alternative Datasets; . Towards AI is the world's leading artificial intelligence (AI) and technology publication. This means that the company In addition, that column was a dictionary object. You can read the details below. The original datafile has lat and lon values truncated to 2 decimal In this capstone project, I was free to analyze the data in my way. Can we categorize whether a user will take up the offer? Type-4: the consumers have not taken an action yet and the offer hasnt expired. The goal of this project is to combine transaction, demographic, and offer data to determine which demographic groups respond best to which offer type. As you can see, the design of the offer did make a difference. New drinks every month and a bit can be annoying especially in high sale areas. This is a decrease of 16.3 percent, or about 10 million units, compared to the same quarter in 2015. So, discount offers were more popular in terms of completion. Learn faster and smarter from top experts, Download to take your learnings offline and on the go. Discover historical prices for SBUX stock on Yahoo Finance. In, Starbucks. Forecasting Total amount of Products using time-series dataset consisting of daily sales data provided by one of the largest Russian software firms . However, for information-type offers, we need to take into account the offer validity. Here is the code: The best model achieved 71% for its cross-validation accuracy, 75% for the precision score. Unbeknown to many, Starbucks has invested significantly in big data and analytics capabilities in order to determine the potential success of its stores and products, and grow sales. age for instance, has a very high score too. Third Attempt: I made another attempt at doing the same but with amount_invalid removed from the dataframe. Actively . Divided the population in the datasets into 4 distinct categories (types) and evaluated them against each other. There were 2 trickier columns, one was the year column and the other one was the channel column. Nestl Professional . Instantly Purchasable Datasets DoorDash Restaurants List $895.00 View Dataset 5.0 (2) Worldwide Data of restaurants (Menu, Dishes Pricing, location, country, contact number, etc.) Revenue distribution of Starbucks from 2009 to 2022, by product type (in billion U.S. dollars) [Graph]. I. Clicking on the following button will update the content below. Urls used in the creation of this data package. Here are the five business questions I would like to address by the end of the analysis. We will also try to segment the dataset into these individual groups. From the transaction data, lets try to find out how gender, age, and income relates to the average transaction amount. The value column has either the offer id or the amount of transaction. Some people like the f1 score. Former Server/Waiter in Adelaide, South Australia. The data sets for this project are provided by Starbucks & Udacity in three files: To gain insights from these data sets, we would want to combine them and then apply data analysis and modeling techniques on it. While Men tend to have more purchases, Women tend to make more expensive purchases. DecisionTreeClassifier trained on 10179 samples. Activate your 30 day free trialto unlock unlimited reading. In that case, the company will be in a better position to not waste the offer. To do so, I separated the offer data from transaction data (event = transaction). 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