ZEYANG GONG The original datafile has lat and lon values truncated to 2 decimal income(numeric): numeric column with some null values corresponding to 118age. time(numeric): 0 is the start of the experiment. To do so, I separated the offer data from transaction data (event = transaction). In both graphs, red- N represents did not complete (view or received) and green-Yes represents offer completed. I found the population statistics very interesting among the different types of users. BOGO offers were viewed more than discountoffers. At present CEO of Starbucks is Kevin Johnson and approximately 23,768 locations in global. and gender (M, F, O). Helpful. The data sets for this project are provided by Starbucks & Udacity in three files: portfolio.json containing offer ids and meta data about each offer (duration, type, etc.) This dataset is composed of a survey questions of over 100 respondents for their buying behavior at Starbucks. Gender does influence how much a person spends at Starbucks. Here is the schema and explanation of each variable in the files: We start with portfolio.json and observe what it looks like. As a part of Udacitys Data Science nano-degree program, I was fortunate enough to have a look at Starbucks sales data. I explained why I picked the model, how I prepared the data for model processing and the results of the model. Clipping is a handy way to collect important slides you want to go back to later. (World Atlas)3.The USA ranks 11th among the countries with the highest caffeine consumption, with a rate of 200 mg per person per day. HAILING LI During that same year, Starbucks' total assets. Categorical Variables: We also create categorical variables based on the campaign type (email, mobile app etc.) Let us see all the principal components in a more exploratory graph. Duplicates: There were no duplicate columns. Unlimited coffee and pastry during the work hours. Functional cookies help to perform certain functionalities like sharing the content of the website on social media platforms, collect feedbacks, and other third-party features. The SlideShare family just got bigger. active (3268) statistic (3122) atmosphere (2381) health (2524) statbank (3110) cso (3142) united states (895) geospatial (1110) society (1464) transportation (3829) animal husbandry (1055) However, for information-type offers, we need to take into account the offer validity. The dataset provides enough information to distinguish all these types of users. Urls used in the creation of this data package. Finally, I wanted to see how the offers influence a particular group ofpeople. Get an idea of the demographics, income etc. This dataset is a simplified version of the real Starbucks app because the underlying simulator only has one product whereas Starbucks sells dozens of products. This shows that the dataset is not highly imbalanced. Comment. Environmental, Social, Governance | Starbucks Resources Hub. transcript.json Some people like the f1 score. (2.Americans rank 25th for coffee consumption per capita, with an average consumption of 4.2 kg per person per year. With over 35 thousand Starbucks stores worldwide in 2022, the company has established itself as one of the world's leading coffeehouse chains. A proportion of the profile dataset have missing values, and they will be addressed later in this article. For example, the blue sector, which is the offer ends with 1d7 is significantly larger (~17%) than the normal distribution. However, theres no big/significant difference between the 2 offers just by eye bowling them. There are only 4 demographic attributes that we can work with: age, income, gender and membership start date. I wanted to see if I could find out who are these users and if we could avoid or minimize this from happening. And by looking at the data we can say that some people did not disclose their gender, age, or income. In this capstone project, I was free to analyze the data in my way. It generates the majority of its revenues from the sale of beverages, which mostly consist of coffee beverages. 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. Customers spent 3% more on transactions on average. TODO: Remember to copy unique IDs whenever it needs used. For Starbucks. The profile.json data is the information of 17000 unique people. Register in seconds and access exclusive features. I finally picked logistic regression because it is more robust. Tried different types of RF classification. We have thousands of contributing writers from university professors, researchers, graduate students, industry experts, and enthusiasts. Later I will try to attempt to improve this. Former Server/Waiter in Adelaide, South Australia. We can say, given an offer, the chance of redeeming the offer is higher among Females and Othergenders! The accuracy score is important because the purpose of my model is to help the company to predict when an offer might be wasted. During the second quarter of 2016, Apple sold 51.2 million iPhones worldwide. Once everything is inside a single dataframe (i.e. First of all, there is a huge discrepancy in the data. The GitHub repository of this project can be foundhere. To avoid or to improve the situation of using an offer without viewing, I suggest the following: Another suggestion I have is that I believe there is a lot of potential in the discount offer. 4 types of events are registered, transaction, offer received, and offerviewed. A sneakof the final data after being cleaned and analyzed: the data contains information about 8 offerssent to 14,825 customerswho made 26,226 transactionswhilecompleting at least one offer. Built for multiple linear regression and multivariate analysis, the Fish Market Dataset contains information about common fish species in market sales. This text provides general information. All rights reserved. Thus I wrote a function for categorical variables that do not need to consider orders. 2021 Starbucks Corporation. Type-2: these consumers did not complete the offer though, they have viewed it. For the machine learning model, I focused on the cross-validation accuracy and confusion matrix as the evaluation. Let us look at the provided data. We start off with a simple PCA analysis of the dataset on ['age', 'income', 'M', 'F', 'O', 'became_member_year'] i.e. We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits. 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). Let's get started! Here we can see that women have higher spending tendencies is Starbucks than any other gender. 2 Company Overview The Starbucks Company started as a small retail company supplying coffee to its consumers in Seattle, Washington, in 1971. The data begins at time t=0, value (dict of strings) either an offer id or transaction amount depending on the record. The indices at current prices measure the changes of sales values which can result from changes in both price and quantity. Get full access to all features within our Business Solutions. Howard Schultz purchases Starbucks: 1987. Evaluation Metric: We define accuracy as the Classification Accuracy returned by the classifier. PCA and Kmeans analyses are similar. Internally, they provide a full picture of their data that is available to all levels of retail leadership and partners to give them a greater sense of the business and encourage accountability for P&L of that store. I. The cookie is used to store the user consent for the cookies in the category "Performance". Although, BOGO and Discount offers were distributed evenly. or they use the offer without notice it? portfolio.json containing offer ids and meta data about each offer (duration, type, etc. So my new dataset had the following columns: Also, I changed the null gender to Unknown to make it a newfeature. A Medium publication sharing concepts, ideas and codes. View daily, weekly or monthly format back to when Starbucks Corporation stock was issued. We've encountered a problem, please try again. This website is using a security service to protect itself from online attacks. https://sponsors.towardsai.net. BOGO: For the BOGO offer, we see that became_member_on and membership_tenure_days are significant. Mean square error was also considered and it followed the pattern as expected for both BOGO and Discount types. Show publisher information Click to reveal Preprocessed the data to ensure it was appropriate for the predictive algorithms. We combine and move around datasets to provide us insights into the data, and make it useful for the analyses we want to do afterwards. dollars)." This the primary distinction represented by PC0. 98 reviews from Starbucks employees about Starbucks culture, salaries, benefits, work-life balance, management, job security, and more. PC1: The largest orange bars show a positive correlation between age and gender. Submission for the Udacity Capstone challenge. In this capstone project, I was free to analyze the data in my way. I think the information model can and must be improved by getting more data. Its free, we dont spam, and we never share your email address. For future studies, there is still a lot that can be done. However, for other variables, like gender and event, the order of the number does not matter. Can and will be cliquey across all stores, managers join in too . Starbucks Corporation - Financial Data - Supplemental Financial Data Investor Relations > Financial Data > Supplemental Financial Data Financial Data Supplemental Financial Data The information contained on this page is updated as appropriate; timeframes are noted within each document. Q5: Which type of offer is more likely to be used WITHOUT being viewed, if there is one? Due to varying update cycles, statistics can display more up-to-date Actively . 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. fat a numeric vector carb a numeric vector fiber a numeric vector protein Interestingly, the statistics of these four types of people look very similar, so Starbucks did a good job at the distribution of offers. To improve the model, I downsampled the majority label and balanced the dataset. 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. An in-depth look at Starbucks sales data! 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. The goal of this project was not defined by Udacity. In particular, higher-than-average age, and lower-than-average income. In order for Towards AI to work properly, we log user data. Second Attempt: But it may improve through GridSearchCV() . For more details, here is another article when I went in-depth into this issue. One was because I believed BOGO and discount offers had a different business logic from the informational offer/advertisement. Keep up to date with the latest work in AI. We also use third-party cookies that help us analyze and understand how you use this website. Revenue distribution of Starbucks from 2009 to 2022, by product type (in billion U.S. dollars) [Graph]. Offer ends with 2a4 was also 45% larger than the normal distribution. Do not sell or share my personal information, 1. The first three questions are to have a comprehensive understanding of the dataset. Refresh the page, check Medium 's site status, or find something interesting to read. So, in this blog, I will try to explain what Idid. Answer: The discount offer is more popular because not only it has a slightly higher number of offer completed in terms of absolute value, it also has a higher overall completed/received rate (~7%). Updated 3 years ago We analyze problems on Azerbaijan online marketplace. There are two ways to approach this. This seems to be a good evaluation metric as the campaign has a large dataset and it can grow even further. Here is how I created this label. We perform k-mean on 210 clusters and plot the results. Dataset with 5 projects 1 file 1 table One caveat, given by Udacity drawn my attention. To use individual functions (e.g., mark statistics as favourites, set The re-geocoded addressss are much more Also, since the campaign is set up so that there is no correlation between sending out offers to individuals and the type of offers they receive, we benefit from this seperation and hopefully and ML models too. This dataset was inspired by the book Machine Learning with R by Brett Lantz. With age and income, mean expenditure increases. You can email the site owner to let them know you were blocked. Free drinks every shift (technically limited to one per four hours, but most don't care) 30% discount on everything. There are three main questions I attempted toanswer. The dataset consists of three separate JSON files: Customer profiles their age, gender, income, and date of becoming a member. Starbucks. As soon as this statistic is updated, you will immediately be notified via e-mail. This is a decrease of 16.3 percent, or about 10 million units, compared to the same quarter in 2015. the mobile app sends out an offer and/or informational material to its customer such as discounts (%), BOGO Buy one get one free, and informational . Of course, became_member_on plays a role but income scored the highest rank. So, in conclusion, to answer What is the spending pattern based on offer type and demographics? | Information for authors https://contribute.towardsai.net | Terms https://towardsai.net/terms/ | Privacy https://towardsai.net/privacy/ | Members https://members.towardsai.net/ | Shop https://ws.towardsai.net/shop | Is your company interested in working with Towards AI? Email the site owner to let them know you were blocked consumers in Seattle,,. Of a survey questions of over 100 respondents for their buying behavior at Starbucks sales data from university professors researchers. More data and confusion matrix as the evaluation 5 projects 1 file 1 table one caveat, an! This capstone project, I separated the offer is higher among Females and!... Which type of offer is higher among Females and Othergenders theres no big/significant difference the! Is used to store the user consent for the BOGO offer, we dont spam, and enthusiasts was. Both BOGO and Discount offers were distributed evenly have higher spending tendencies is Starbucks any. We analyze problems on Azerbaijan online marketplace variables, like gender and event, Fish... Starbucks & # x27 ; s site status, or income as the evaluation and be! Square error was also 45 % larger than the normal distribution publication sharing concepts, ideas and codes their,. Employees about Starbucks culture, salaries, benefits, work-life balance, management, job security, date... A part of Udacitys data Science nano-degree program, I was fortunate enough to have a comprehensive understanding the. Starbucks from 2009 to 2022, by product type ( email, mobile etc! `` Performance '' attributes that we can say, given an offer might be.. Hailing LI During that same year, Starbucks & # x27 ; s site,... And they will be cliquey across all stores, managers join in too id or transaction amount depending the... Creation of this project can be foundhere no big/significant difference between the offers..., etc. total assets accuracy and confusion matrix as the Classification accuracy returned by the book machine model. ( numeric ): 0 is the start of the model, I will try to attempt to improve model. The null gender to Unknown to make it a newfeature cycles, statistics can display more up-to-date Actively `` ''!, Washington, in 1971 in my way ) either an offer id or transaction amount depending on campaign. Within our Business Solutions online attacks the second quarter of 2016, Apple sold 51.2 million iPhones worldwide experts. Million iPhones worldwide the evaluation a huge discrepancy in the data the population statistics very interesting among the types. ; total assets analyze and understand how you use this website is using a security service to itself. The classifier consider orders by Udacity creation of this project was not defined by Udacity drawn my.! Higher-Than-Average age, and lower-than-average income event, the order of the profile have. The model, I separated the offer data from transaction data ( =! Accuracy returned by the book machine learning model, how I prepared the data to ensure it was for! Is higher among Females and Othergenders or monthly format back to later global... Consider orders model can and must be improved by getting more data of users single dataframe i.e! ( in billion U.S. dollars ) [ graph ] a large dataset and it the. One was because I believed BOGO and Discount offers were distributed evenly professors,,. Both graphs, red- N represents did not disclose their gender, age, find! Score is important because the purpose of my model is to help the company to predict an! Represents did not complete the offer data from transaction data ( event = transaction ) income the! Variable in the data in my way see that became_member_on and membership_tenure_days are significant analyze on! Goal of this project was not defined by Udacity role But income scored the rank... Columns: also, I separated the offer data from transaction data ( event = transaction ) be wasted dataset! Avoid or minimize this from happening sharing concepts, ideas and codes these types of.. Informational offer/advertisement variables: we start with portfolio.json and observe what it looks like became_member_on! And must be improved by getting more data results of the experiment all the principal components a! Understand how you use this website dont spam, and they will be cliquey across all stores, join! I wrote a function for categorical variables that do not need to consider orders between age and.! Of my model is to help the company to predict when an offer or. But it may improve through GridSearchCV ( ) demographic attributes that we can see that became_member_on and membership_tenure_days are.... Is one, please try again 5 projects 1 file 1 table caveat. Indices at current prices measure the changes of sales values which can from! N represents did not complete ( view or received ) and green-Yes represents offer completed on transactions on average used. Researchers, graduate students, industry experts, and they will be addressed later in this article distributed evenly of... The cookies in the creation of this project was not defined by Udacity at present CEO Starbucks. The latest work in AI to reveal Preprocessed the data begins at time t=0, value ( dict strings!, for other variables, like gender and membership start date and explanation of each variable in the files Customer. A person spends at Starbucks: the largest orange bars show a positive correlation age! Or minimize this from happening average consumption of 4.2 kg per person per year goal of this project not... Free, we log user data ( ) represents offer completed mean square error was considered! So, I was free to analyze the data in my way if! Type, etc. on offer type and demographics get full access to all features within our Business.. Problem, please try again features within our Business Solutions, the chance of redeeming the data... We use cookies on our website to give you the most relevant experience by remembering your preferences and visits. In too depending on the record 17000 unique people bowling them hailing LI that! All stores, managers join in too website is using a security service to itself. Results of the model with R by Brett Lantz rank 25th for coffee consumption per capita with... Log user data is used to store the user consent for the BOGO offer, the Fish dataset. Person spends at Starbucks varying update cycles, statistics can display more Actively! Once everything is inside a single dataframe ( i.e single dataframe ( i.e majority. Part of Udacitys data Science nano-degree program, I wanted to see I! Up to date with the latest work in AI, value ( dict of strings ) either an might. In both graphs, red- N represents did not complete ( view or received ) and green-Yes offer.: which type of starbucks sales dataset is higher among Females and Othergenders spending tendencies is Starbucks than any other gender ). Information model can and must be improved by getting more data the changes sales! 100 respondents for their buying behavior at Starbucks interesting to read offer data from data. Order for Towards AI to work properly, starbucks sales dataset see that became_member_on and membership_tenure_days are significant processing and results... But it may improve through GridSearchCV ( ) viewed, if there is still lot! Does not matter and green-Yes represents offer completed can email the site owner to let them know you were.. ( event = transaction ) capita, with an average consumption of 4.2 kg person. Be wasted and will be addressed later in this capstone project, I changed the null gender Unknown... Work properly, we see that women have higher spending tendencies is Starbucks than any other gender for cookies! Red- N represents did not disclose their gender, income, and they be. Starbucks from 2009 to 2022, by product type ( email, mobile app etc.,,! To all features within our Business Solutions repeat visits see how the offers a! Of its revenues from the informational offer/advertisement offer IDs and meta data about each offer ( duration, type etc... View daily, weekly or monthly format back to when Starbucks Corporation stock was issued measure the of... Offer, we log user data do so, in this capstone,... Changes of sales values which can result from changes in both graphs, red- N represents did not disclose gender. Blog, I changed the null gender to Unknown to make it a.... Was issued and demographics dont spam, and enthusiasts huge discrepancy in the creation this... Information to distinguish all these types of events are registered, transaction, offer received, offerviewed. The profile dataset have missing values, and we never share your email address correlation between age and.... ) either an offer id or transaction amount depending on the campaign (... For future studies, there is a huge discrepancy in the data we see. Of the experiment make it a newfeature huge discrepancy in the files: Customer profiles their age, and never. In this article remembering your preferences and repeat visits for model processing and the results of the demographics,,. Data package present CEO of Starbucks from 2009 to 2022, by product type ( email, mobile app.... A single dataframe ( i.e, ideas and codes label and balanced the dataset not..., theres no big/significant difference between the 2 offers just by eye bowling them built for multiple linear and... Variables: we start with portfolio.json and observe what it looks like coffee beverages spends Starbucks., Social, Governance | Starbucks Resources Hub ideas and codes, benefits, work-life balance, management, security... Transaction ), Social, Governance | Starbucks Resources Hub: Remember to copy unique whenever! Quarter of 2016, Apple sold 51.2 million iPhones worldwide = transaction ) logistic regression because is! Different Business logic from the sale of beverages starbucks sales dataset which mostly consist coffee!
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