Once every few days, Starbucks sends out an offer to users of the mobile app. Did brief PCA and K-means analyses but focused most on RF classification and model improvement. Upload your resume . Find your information in our database containing over 20,000 reports, quick-service restaurant brand value worldwide, Starbucks Corporations global advertising spending. portfolio.json containing offer ids and meta data about each offer (duration, type, etc. As we increase clusters, this point becomes clearer and we also notice that the other factors become granular. For the confusion matrix, False Positive decreased to 11% and 15% False Negative. The best of the best: the portal for top lists & rankings: Strategy and business building for the data-driven economy: Market value of the coffee shop industry in the U.S. 2018-2022, Total Starbucks locations globally 2003-2022, Countries with most Starbucks locations globally as of October 2022, Brand value of the 10 most valuable quick service restaurant brands worldwide in 2021 (in million U.S. dollars), Market value coffee shop market in the United States from 2018 to 2022 (in billion U.S. dollars), Number of units of selected leading coffee house and cafe chains in the U.S. 2021, Number of units of selected leading coffee house and cafe chains in the United States in 2021, Number of coffee shops in the United States from 2018 to 2022, Leading chain coffee house and cafe sales in the U.S. 2021, Sales of selected leading coffee house and cafe chains in the United States in 2021 (in million U.S. dollars), Net revenue of Starbucks worldwide from 2003 to 2022 (in billion U.S. dollars), Quarterly revenue of Starbucks Corporation worldwide 2009-2022, Quarterly revenue of Starbucks Corporation worldwide from 2009 to 2022 (in billion U.S. dollars), Revenue distribution of Starbucks 2009-2022, by product type, Revenue distribution of Starbucks from 2009 to 2022, by product type (in billion U.S. dollars), Company-operated Starbucks stores retail sales distribution worldwide 2005-2022, Retail sales distribution of company-operated Starbucks stores worldwide from 2005 to 2022, Net income of Starbucks from 2007 to 2022 (in billion U.S. dollars), Operating income of Starbucks from 2007 to 2022 (in billion U.S. dollars), U.S. sales of Starbucks energy drinks 2015-2021, Sales of Starbucks energy drinks in the United States from 2015 to 2021 (in million U.S. dollars), U.S. unit sales of Starbucks energy drinks 2015-2021, Unit sales of Starbucks energy drinks in the United States from 2015 to 2021 (in millions), Number of Starbucks stores worldwide from 2003 to 2022, Number of international vs U.S.-based Starbucks stores 2005-2022, Number of international and U.S.-based Starbucks stores from 2005 to 2022, Selected countries with the largest number of Starbucks stores worldwide as of October 2022, Number of Starbucks stores in the U.S. 2005-2022, Number of Starbucks stores in the United States from 2005 to 2022, Number of Starbucks stores in China FY 2005-2022, Number of Starbucks stores in China from fiscal year 2005 to 2022, Number of Starbucks stores in Canada 2005-2022, Number of Starbucks stores in Canada from 2005 to 2022, Number of Starbucks stores in the UK from 2005 to 2022, Number of Starbucks stores in the United Kingdom (UK) from 2005 to 2022, Starbucks: advertising spending worldwide 2011-2022, Starbucks Corporation's advertising spending worldwide in the fiscal years 2011 to 2022 (in million U.S. dollars), Starbucks's advertising spending in the U.S. 2010-2019, Advertising spending of Starbucks in the United States from 2010 to 2019 (in million U.S. dollars), American Customer Satisfaction Index: Starbucks in the U.S. 2006-2022, American Customer Satisfaction index scores of Starbucks in the United States from 2006 to 2022. To improve the model, I downsampled the majority label and balanced the dataset. View daily, weekly or monthly format back to when Starbucks Corporation stock was issued. PC3: primarily represents the tenure (through became_member_year). Third Attempt: I made another attempt at doing the same but with amount_invalid removed from the dataframe. Using Polynomial Features: To see if the model improves, I implemented a polynomial features pipeline with StandardScalar(). Duplicates: There were no duplicate columns. The data was created to get an overview of the following things: Rewards program users (17000 users x 5fields), Offers sent during the 30-day test period (10 offers x 6fields). I want to end this article with some suggestions for the business and potential future studies. Chart. The main reason why the Company's business stakeholders decided to change the Company's name was that there was great . Performance & security by Cloudflare. We also use third-party cookies that help us analyze and understand how you use this website. The testing score of Information model is significantly lower than 80%. 13, 2016 6 likes 9,465 views Download Now Download to read offline Business Created database for Starbucks to retrieve data answering any business related questions and helping with better informative business decisions Ruibing Ji Follow Advertisement Advertisement Recommended Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. An offer can be merely an advertisement for a drink or an actual offer such as a discount or BOGO ( If you are an admin, please authenticate by logging in again. This is knowledgeable Starbucks is the third largest fast food restaurant chain. Your home for data science. Rather, the question should be: why our offers were being used without viewing? We evaluate the accuracy based on correct classification. To observe the purchase decision of people based on different promotional offers. The gap between offer completed and offer viewed also decreased as time goes by. In this project, the given dataset contains simulated data that mimics customer behavior on the Starbucks rewards mobile app. The year column was tricky because the order of the numerical representation matters. Therefore, I stick with the confusion matrix. Here is how I did it. For Starbucks. DATA SOURCES 1. Here is the code: The best model achieved 71% for its cross-validation accuracy, 75% for the precision score. So, in this blog, I will try to explain what Idid. From I talked about how I used EDA to answer the business questions I asked at the bringing of the article. In addition, that column was a dictionary object. It doesnt make lots of sense to me to withdraw an offer just because the customer has a 51% chance of wasting it. STARBUCKS CORPORATION : Forcasts, revenue, earnings, analysts expectations, ratios for STARBUCKS CORPORATION Stock | SBUX | US8552441094 The SlideShare family just got bigger. Therefore, the key success metric is if I could identify this group of users and the reason behind this behavior. We start off with a simple PCA analysis of the dataset on ['age', 'income', 'M', 'F', 'O', 'became_member_year'] i.e. Please do not hesitate to contact me. Sep 8, 2022. It does not store any personal data. You must click the link in the email to activate your subscription. Here we can notice that women in this dataset have higher incomes than men do. dataset. 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. Sales & marketing day 4 [class of 5th jan 2020], Retail for Business Analysts and Management Consultants, Keeping it Real with Dashboards in The Financial Edge. Dataset with 108 projects 1 file 1 table. Read by thought-leaders and decision-makers around the world. Later I will try to attempt to improve this. Directly accessible data for 170 industries from 50 countries and over 1 million facts: Get quick analyses with our professional research service. Mobile users are more likely to respond to offers. Second Attempt: But it may improve through GridSearchCV() . Submission for the Udacity Capstone challenge. However, I stopped here due to my personal time and energy constraint. Dollars). For BOGO and Discount we have a reasonable accuracy. ** Other includes royalty and licensing revenues, beverage-related ingredients, ready-to-drink beverages and serveware, among other items. Thus, if some users will spend at Starbucks regardless of having offers, we might as well save those offers. Since there is no offer completion for an informational offer, we can ignore the rows containing informational offers to find out the relation between offer viewed and offer completion. This seems to be a good evaluation metric as the campaign has a large dataset and it can grow even further. Forecasting Total amount of Products using time-series dataset consisting of daily sales data provided by one of the largest Russian software firms . We perform k-mean on 210 clusters and plot the results. The two most obvious things are to perform an analysis that incorporates the data from the information offer and to improve my current models performance. 2 Lawrence C. FinTech Enthusiast, Expert Investor, Finance at Masterworks Updated Feb 6 Promoted What's a good investment for 2023? ", Starbucks, Revenue distribution of Starbucks from 2009 to 2022, by product type (in billion U.S. dollars) Statista, https://www.statista.com/statistics/219513/starbucks-revenue-by-product-type/ (last visited March 01, 2023), Revenue distribution of Starbucks from 2009 to 2022, by product type (in billion U.S. dollars) [Graph], Starbucks, November 18, 2022. These cookies ensure basic functionalities and security features of the website, anonymously. So they should be comparable. We've encountered a problem, please try again. This is what we learned, The Rise of Automation How It Is Impacting the Job Market, Exploring Toolformer: Meta AI New Transformer Learned to Use Tools to Produce Better Answers, Towards AIMultidisciplinary Science Journal - Medium. We are happy to help. Expanding a bit more on this. I wonder if this skews results towards a certain demographic. This is a decrease of 16.3 percent, or about 10 million units, compared to the same quarter in 2015. We will discuss this at the end of this blog. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. Perhaps, more data is required to get a better model. The Reward Program is available on mobile devices as the Starbucks app, and has seen impressive membership and growth since 2008, with multiple iterations on its original form. One difficulty in merging the 3 datasets was the value column in the transcript dataset contained both the offer id and the dollar amount. to incorporate the statistic into your presentation at any time. PC4: primarily represents age and income. Starbucks expands beyond Seattle: 1987. Q4: Which group of people is more likely to use the offer or make a purchase WITHOUT viewing the offer, if there is such a group? This shows that the dataset is not highly imbalanced. The following figure summarizes the different events in the event column. TODO: Remember to copy unique IDs whenever it needs used. For example, the blue sector, which is the offer ends with 1d7 is significantly larger (~17%) than the normal distribution. There are three main questions I attempted toanswer. How to Ace Data Science Interview by Working on Portfolio Projects. Number of Starbucks stores in the U.S. 2005-2022, American Customer Satisfaction Index: Starbucks in the U.S. 2006-2022, Market value of the coffee shop industry in the U.S. 2018-2022. KEFU ZHU As we can see, in general, females customers earn more than male customers. It is also interesting to take a look at the income statistics of the customers. In this case, however, the imbalanced dataset is not a big concern. Therefore, I did not analyze the information offer type. Starbucks. So, in this blog, I will try to explain what I did. There are only 4 demographic attributes that we can work with: age, income, gender and membership start date. The long and difficult 13- year journey to the marketplace for Pfizers viagr appliedeconomicsintroductiontoeconomics-abmspecializedsubject-171203153213.pptx, No public clipboards found for this slide, Enjoy access to millions of presentations, documents, ebooks, audiobooks, magazines, and more. Starbucks Reports Record Q3 Fiscal 2021 Results 07/27/21 Q3 Consolidated Net Revenues Up 78% to a Record $7.5 Billion Q3 Comparable Store Sales Up 73% Globally; U.S. Up 83% with 10% Two-Year Growth Q3 GAAP EPS $0.97; Record Non-GAAP EPS of $1.01 Driven by Strong U.S. Q4 Consolidated Net Revenues Up 31% to a Record $8.1 Billion. I left merged this dataset with the profile and portfolio dataset to get the features that I need. Offer ends with 2a4 was also 45% larger than the normal distribution. This means that the company These cookies help provide information on metrics the number of visitors, bounce rate, traffic source, etc. This cookie is set by GDPR Cookie Consent plugin. A list of Starbucks locations, scraped from the web in 2017, chrismeller.github.com-starbucks-2.1.1. Starbucks Reports Q4 and Full Year Fiscal 2021 Results. ), profile.json demographic data for each customer, transcript.json records for transactions, offers received, offers viewed, and offers completed. Longer duration increase the chance. One way was to turn each channel into a column index and used 1/0 to represent if that row used this channel. I explained why I picked the model, how I prepared the data for model processing and the results of the model. In summary, I have walked you through how I processed the data to merge the 3 datasets so that I could do data analysis. Updated 3 years ago We analyze problems on Azerbaijan online marketplace. The company's loyalty program reported 24.8 million . At present CEO of Starbucks is Kevin Johnson and approximately 23,768 locations in global. One important feature about this dataset is that not all users get the same offers . portfolio.json containing offer ids and meta data about each offer (duration, type, etc. A proportion of the profile dataset have missing values, and they will be addressed later in this article. I found the population statistics very interesting among the different types of users. HAILING LI 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 channel column was tricky because each cell was a list of objects. the dataset used here is a simulated data that mimics customer behaviour on the Starbucks rewards mobile app. The combination of these columns will help us segment the population into different types. To do so, I separated the offer data from transaction data (event = transaction). Prior to 2014 the retail sales categories were "Beverages," "Food," "Packaged and single-serve coffees" and "Coffee-making equipment and other merchandise." If youre struggling with your assignments like me, check out www.HelpWriting.net . We receive millions of visits per year, have several thousands of followers across social media, and thousands of subscribers. Mobile users may be more likely to respond to offers. For example, if I used: 02017, 12018, 22015, 32016, 42013. Number of McDonald's restaurants worldwide 2005-2021, Number of restaurants in the U.S. 2011-2018, Average daily rate of hotels in the U.S. 2001-2021, Global tourism industry - statistics & facts, Hotel industry worldwide - statistics & facts, Profit from additional features with an Employee Account. The re-geocoded . The reason is that we dont have too many features in the dataset. Customers spent 3% more on transactions on average. These cookies will be stored in your browser only with your consent. The whole analysis is provided in the notebook. We will get rid of this because the population of 118 year-olds is not insignificant in our dataset. Howard Schultz purchases Starbucks: 1987. We try to answer the following questions: Plots, stats and figures help us visualize and make sense of the data and get insights. Interactive chart of historical daily coffee prices back to 1969. Analytical cookies are used to understand how visitors interact with the website. Environmental, Social, Governance | Starbucks Resources Hub. Another reason is linked to the first reason, it is about the scope. In the following article, I will walk through how I investigated this question. active (3268) statistic (3122) atmosphere (2381) health (2524) statbank (3110) cso (3142) united states (895) geospatial (1110) society (1464) transportation (3829) animal husbandry (1055) Every data tells a story! Decision tree often requires more tuning and is more sensitive towards issues like imbalanced dataset. The cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. Tap here to review the details. A Medium publication sharing concepts, ideas and codes. 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. Finally, I built a machine learning model using logistic regression. Tagged. I thought this was an interesting problem. Preprocessed the data to ensure it was appropriate for the predictive algorithms. fat a numeric vector carb a numeric vector fiber a numeric vector protein Available: https://www.statista.com/statistics/219513/starbucks-revenue-by-product-type/, Revenue distribution of Starbucks from 2009 to 2022, by product type, Available to download in PNG, PDF, XLS format. Market & Alternative Datasets; . US Coffee Statistics. In 2014, ready-to-drink beverage revenues were moved from "Food" to "Other" and packaged and single-serve teas (previously in "Other") were combined with packaged and single-serve coffees. As a Premium user you get access to the detailed source references and background information about this statistic. Take everything with a grain of salt. Of course, became_member_on plays a role but income scored the highest rank. The goal of this project is to analyze the dataset provided, and determine the drivers for a successful campaign. Answer: For both offers, men have a significantly lower chance of completing it. The reason is that the business costs associate with False Positive and False Negative might be different. The ideal entry-level account for individual users. June 14, 2016. Modified 2021-04-02T14:52:09. . Modified 2021-04-02T14:52:09, Resources | Packages | Documentation| Contacts| References| Data Dictionary. However, I found the f1 score a bit confusing to interpret. and gender (M, F, O). Show Recessions Log Scale. It will be very helpful to increase my model accuracy to be above 85%. First of all, there is a huge discrepancy in the data. Performance cookies are used to understand and analyze the key performance indexes of the website which helps in delivering a better user experience for the visitors. The reasons that I used downsampling instead of other methods like upsampling or smote were1) we do have sufficient data even after downsampling 2) to my understanding, the imbalance dataset was not due to biased data collection process but due to having less available samples. liability for the information given being complete or correct. All rights reserved. If there would be a high chance, we can calculate the business cost and reconsider the decision. We also do brief k-means analysis before. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Today, with stores around the globe, the Company is the premier roaster and retailer of specialty coffee in the world. Search Salary. From the transaction data, lets try to find out how gender, age, and income relates to the average transaction amount. I realized that there were 4 different combos of channels. Clicking on the following button will update the content below. Now customize the name of a clipboard to store your clips. Let us look at the provided data.