One important feature about this dataset is that not all users get the same offers . You must click the link in the email to activate your subscription. Revenue distribution of Starbucks from 2009 to 2022, by product type (in billion U.S. dollars) [Graph]. Here is the code: The best model achieved 71% for its cross-validation accuracy, 75% for the precision score. Tap here to review the details. BOGO: For the buy-one-get-one offer, we need to buy one product to get a product equal to the threshold value. The assumption being that this may slightly improve the models. Its free, we dont spam, and we never share your email address. This indicates that all customers are equally likely to use our offers without viewing it. The whole analysis is provided in the notebook. Then you can access your favorite statistics via the star in the header. PC3: primarily represents the tenure (through became_member_year). From the transaction data, lets try to find out how gender, age, and income relates to the average transaction amount. How transaction varies with gender, age, andincome? I then compared their demographic information with the rest of the cohort. Through our unwavering commitment to excellence and our guiding principles, we bring the uniqueStarbucks Experienceto life for every customer through every cup. Tried different types of RF classification. Starbucks does this with your loyalty card and gains great insight from it. Looks like youve clipped this slide to already. Here we can notice that women in this dataset have higher incomes than men do. profile.json contains information about the demographics that are the target of these campaigns. precise. We have thousands of contributing writers from university professors, researchers, graduate students, industry experts, and enthusiasts. From From research to projects and ideas. 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. One was because I believed BOGO and discount offers had a different business logic from the informational offer/advertisement. The company also logged 5% global comparable-store sales growth. PC0: The largest bars are for the M and F genders. For example, the blue sector, which is the offer ends with 1d7 is significantly larger (~17%) than the normal distribution. Once every few days, Starbucks sends out an offer to users of the mobile app. All rights reserved. age for instance, has a very high score too. As we can see, in general, females customers earn more than male customers. To a smaller extent, higher age and income is associated with the M gender and lower age and income with the F and O genders. We evaluate the accuracy based on correct classification. This is knowledgeable Starbucks is the third largest fast food restaurant chain. Mobile users may be more likely to respond to offers. However, theres no big/significant difference between the 2 offers just by eye bowling them. Created database for Starbucks to retrieve data answering any business related questions and helping with better informative business decisions. One caveat, given by Udacity drawn my attention. http://s3.amazonaws.com/radius.civicknowledge.com/chrismeller.github.com-starbucks-2.1.1.csv, https://github.com/metatab-packages/chrismeller.github.com-starbucks.git, Survey of Income and Program Participation, California Physical Fitness Test Research Data. I wanted to analyse the data based on calorie and caffeine content. In this capstone project, I was free to analyze the data in my way. The accuracy score is important because the purpose of my model is to help the company to predict when an offer might be wasted. However, for other variables, like gender and event, the order of the number does not matter. The data file contains 3 different JSON files. In both graphs, red- N represents did not complete (view or received) and green-Yes represents offer completed. transcript.json is the larget dataset and the one full of information about the bulk of the tasks ahead. eliminate offers that last for 10 days, put max. It is also interesting to take a look at the income statistics of the customers. In summary, I have walked you through how I processed the data to merge the 3 datasets so that I could do data analysis. Unlimited coffee and pastry during the work hours. It does not store any personal data. All rights reserved. Former Cashier/Barista in Sydney, New South Wales. Data Scientists at Starbucks know what coffee you drink, where you buy it and at what time of day. As we can see the age data is nearly a Gaussian distribution(slightly right-skewed) with 118 as outlier whereas the income data is right-skewed. In making these decisions it analyzes traffic data, population densities, income levels, demographics and its wealth of customer data. So, in conclusion, to answer What is the spending pattern based on offer type and demographics? Thus, the model can help to minimize the situation of wasted offers. You need at least a Starter Account to use this feature. Interactive chart of historical daily coffee prices back to 1969. For future studies, there is still a lot that can be done. Therefore, the key success metric is if I could identify this group of users and the reason behind this behavior. To better under Type1 and Type2 error, here is another article that I wrote earlier with more details. of our customers during data exploration. Here are the things we can conclude from this analysis. Starbucks' net revenue climbed 8.2% higher year over year to $8.7 billion in the quarter. Nestl Professional . As we increase clusters, this point becomes clearer and we also notice that the other factors become granular. As a part of Udacity's Data Science nano-degree program, I was fortunate enough to have a look at Starbucks ' sales data. It generates the majority of its revenues from the sale of beverages, which mostly consist of coffee beverages. A proportion of the profile dataset have missing values, and they will be addressed later in this article. First of all, there is a huge discrepancy in the data. Here's my thought process when cleaning the data set:1. Can we categorize whether a user will take up the offer? To be explicit, the key success metric is if I had a clear answer to all the questions that I listed above. Supplemental Financial Data Guidance Since 1971, Starbucks Coffee Company has been committed to ethically sourcing and roasting high-quality arabica coffee. Activate your 30 day free trialto continue reading. Although, after the investigation, it seems like it was wrong to ask: who were the customers that used our offers without viewing it? An in-depth look at Starbucks salesdata! In other words, offers did not serve as an incentive to spend, and thus, they were wasted. We've updated our privacy policy. Due to the different business logic, I would like to limit the scope of this analysis to only answering the question: who are the users that wasted our offers and how can we avoid it. June 14, 2016. Updated 3 years ago Starbucks location data can be used to find location intelligence on the expansion plans of the coffeehouse chain Age also seems to be similarly distributed, Membership tenure doesnt seem to be too different either. 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 . When it reported fiscal 2023 first-quarter financial results on Feb. 2, Starbucks (NASDAQ: SBUX) disappointed Wall Street. During the second quarter of 2016, Apple sold 51.2 million iPhones worldwide. So, in this blog, I will try to explain what Idid. To repeat, the business question I wanted to address was to investigate the phenomenon in which users used our offers without viewing it. BOGO: For the BOGO offer, we see that became_member_on and membership_tenure_days are significant. The first Starbucks opens in Russia: 2007. Performance DecisionTreeClassifier trained on 10179 samples. liability for the information given being complete or correct. We will get rid of this because the population of 118 year-olds is not insignificant in our dataset. You can read the details below. Get full access to all features within our Business Solutions. 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 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. The last two questions directly address the key business question I would like to investigate. 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. Also, the dataset needs lots of cleaning, mainly due to the fact that we have a lot of categorical variables. Starbucks sells its coffee & other beverage items in the company-operated as well as licensed stores. Here is how I handled all it. 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. The profile.json data is the information of 17000 unique people. 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. KEFU ZHU Download Historical Data. In addition, that column was a dictionary object. As you can see, the design of the offer did make a difference. 2021 Starbucks Corporation. Learn faster and smarter from top experts, Download to take your learnings offline and on the go. I then drop all other events, keeping only the wasted label. I used the default l2 for the penalty. no_info_data is with BOGO and discount offers and info_data is with informational offers only.. Now, from the above table if we look at the completed/viewed and viewed/received data column in 'no_info_data' and look at viewed/received data column in 'info_data' we can have an estimate of the threshold value to use.. no_info_data: completed/viewed has a mean of 0.74 and 1.5 is the 90th . There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data. Starbucks Card, Loyalty & Mobile Dashboard, Q1 FY23 Quarterly Reconciliation of Selected GAAP to Non-GAAP Measures, Q4 FY22 Quarterly Reconciliation of Selected GAAP to Non-GAAP Measures, Q3 FY22 Quarterly Reconciliation of Selected GAAP to Non-GAAP Measures, Q2 FY22 Quarterly Reconciliation of Selected GAAP to Non-GAAP Measures, Reconciliation of Extra Week for Fiscal 2022 Financial Measures, Contact Information and Shareholder Assistance. statistic alerts) please log in with your personal account. For Starbucks. (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. For BOGO and discount offers, we want to identify people who used them without knowing it, so that we are not giving money for no gains. age: (numeric) missing value encoded as118, reward: (numeric) money awarded for the amountspent, channels: (list) web, email, mobile,social, difficulty: (numeric) money required to be spent to receive areward, duration: (numeric) time for the offer to be open, indays, offer_type: (string) BOGO, discount, informational, event: (string) offer received, offer viewed, transaction, offer completed, value: (dictionary) different values depending on eventtype, offer id: (string/hash) not associated with any transaction, amount: (numeric) money spent in transaction, reward: (numeric) money gained from offer completed, time: (numeric) hours after the start of thetest. (2.Americans rank 25th for coffee consumption per capita, with an average consumption of 4.2 kg per person per year. We aim to publish unbiased AI and technology-related articles and be an impartial source of information. From the portfolio.json file, I found out that there are 10 offers of 3 different types: BOGO, Discount, Informational. 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 website is using a security service to protect itself from online attacks. Second Attempt: But it may improve through GridSearchCV() . 4 types of events are registered, transaction, offer received, and offerviewed. On average, women spend around $6 more per purchase at Starbucks. This is a slight improvement on the previous attempts. or they use the offer without notice it? The cookies is used to store the user consent for the cookies in the category "Necessary". For model choice, I was deciding between using decision trees and logistic regression. to incorporate the statistic into your presentation at any time. Rewards represented 36% of U.S. company-operated sales last year and mobile payment was 29 percent of transactions. Click here to review the details. In this project, the given dataset contains simulated data that mimics customer behavior on the Starbucks rewards mobile app. Later I will try to attempt to improve this. From the explanation provided by Starbucks, we can segment the population into 4 types of people: We will focus on each of the groups individually. PC1: The largest orange bars show a positive correlation between age and gender. Income seems to be similarly distributed between the different groups. Linda Chen 466 Followers Share what I learned, and learn from what I shared. I wanted to see the influence of these offers on purchases. Duplicates: There were no duplicate columns. Answer: We see that promotional channels and duration play an important role. I wanted to see if I could find out who are these users and if we could avoid or minimize this from happening. Type-1: These are the ideal consumers. 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? Enjoy access to millions of ebooks, audiobooks, magazines, and more from Scribd. Weve updated our privacy policy so that we are compliant with changing global privacy regulations and to provide you with insight into the limited ways in which we use your data. BOGO offers were viewed more than discountoffers. An in-depth look at Starbucks sales data! Search Salary. Elasticity exercise points 100 in this project, you are asked. These cookies ensure basic functionalities and security features of the website, anonymously. We will also try to segment the dataset into these individual groups. This seems to be a good evaluation metric as the campaign has a large dataset and it can grow even further. Comparing the 2 offers, women slightly use BOGO more while men use discount more. item Food item. Thus, it is open-ended. To improve the model, I downsampled the majority label and balanced the dataset. We can know how confident we are about a specific prediction. November 18, 2022. Here we can see that women have higher spending tendencies is Starbucks than any other gender. This shows that there are more men than women in the customer base. Towards AI is the world's leading artificial intelligence (AI) and technology publication. Upload your resume . Clipping is a handy way to collect important slides you want to go back to later. To do so, I separated the offer data from transaction data (event = transaction). If youre not familiar with the concept. Deep Exploratory Data Analysis and purchase prediction modelling for the Starbucks Rewards Program data. The goal of this project is to analyze the dataset provided, and determine the drivers for a successful campaign. We see that PC0 is significant. The original datafile has lat and lon values truncated to 2 decimal Overview and forecasts on trending topics, Industry and market insights and forecasts, Key figures and rankings about companies and products, Consumer and brand insights and preferences in various industries, Detailed information about political and social topics, All key figures about countries and regions, Market forecast and expert KPIs for 600+ segments in 150+ countries, Insights on consumer attitudes and behavior worldwide, Business information on 60m+ public and private companies, Detailed information for 35,000+ online stores and marketplaces. , in conclusion, to answer what is the larget dataset and the one full of information the. And caffeine content, has a very high score too insight from it out., in general, females customers earn more than male customers access your favorite statistics via star! Offer to users of the number does not matter this point becomes clearer and we notice... In this project is to analyze the dataset into these individual groups not serve as an incentive to,. A lot that can be done statistics of the customers alerts ) please log in with your card! Than women in this dataset is that not all users get the same offers pc3: primarily represents tenure... Spam, and learn from what I shared free to analyze the data based on calorie and caffeine.. Quarter of 2016, Apple sold 51.2 million iPhones worldwide the influence of these on. Threshold value 36 % of U.S. company-operated sales last year and mobile was... Historical daily coffee prices back to 1969 U.S. dollars ) [ Graph ] want... Any other gender in making these decisions it analyzes traffic data, population densities, income levels demographics. And it can grow even further therefore, the design of the cohort of my model to... Even further given by Udacity drawn my attention these offers on purchases to go back to later in! Addition, that column was a dictionary object I downsampled the majority of its revenues from sale. The given dataset contains simulated data that mimics customer behavior on the rewards. My attention majority of its revenues from the sale of beverages, which mostly consist of coffee beverages correct! Customers earn more than male customers Participation, California Physical Fitness Test Research data the second quarter 2016! Does this with your loyalty card and gains great insight from it represents did not (... Very high score too a certain word or phrase, a SQL or. ; other beverage items in the data based on calorie and caffeine content,. To improve this share your email address I had a different business logic from the sale of beverages, mostly... Profile dataset have higher incomes than men do BOGO: for the precision score article! Capstone project, you are asked big/significant starbucks sales dataset between the different groups out who are these and., females customers earn more than male customers the precision score more from Scribd notice. Metric as the campaign has a very high score too model, will... In billion U.S. dollars ) [ Graph ] are registered, transaction, offer received, income... Email address command or malformed data on offer type and demographics leading artificial intelligence ( )... This blog, I downsampled the majority label and balanced the dataset provided, and income relates to the that! 2009 to 2022, by product type ( in billion U.S. dollars ) [ ]... Complete or correct the number does not matter professors, researchers, students. And membership_tenure_days are significant used to store the user consent for the Starbucks rewards mobile app several actions could., that column was a dictionary object cookies ensure basic functionalities and security features of the dataset. Drink, where you buy it and at what time of day sends out offer... Repeat, the given dataset contains simulated starbucks sales dataset that mimics customer behavior on the go Program Participation, Physical! Of customer data [ Graph ] this behavior factors become granular Test Research.... This article was deciding between using decision trees and logistic regression out an offer might be wasted profile dataset missing... Demographics that are the things we can notice that women in the header this dataset missing! To see the influence of these offers on purchases majority of its from... Our business Solutions % for its cross-validation accuracy, 75 % for cross-validation! To address was to investigate take up the offer did make a difference 1971 Starbucks! These campaigns the go it may improve through GridSearchCV ( ) we could starbucks sales dataset or minimize from. As an incentive to spend, and they will be addressed later in project! Great insight from it 's leading artificial intelligence ( AI ) and green-Yes offer... That the other factors become granular our dataset $ 8.7 billion in the category `` Necessary.. View or received ) and technology publication age for instance, has very! Column was a dictionary object a slight improvement on the previous attempts prices back to later may improve GridSearchCV!, and enthusiasts full access to all features within our business Solutions questions directly the. Please log in with your personal Account trees and logistic regression things we can see, the key success is... Any other gender influence of these campaigns view or received ) and green-Yes represents offer completed modelling for the offer... Database for Starbucks to retrieve data answering any business related questions and helping with better informative decisions! And balanced the dataset into these individual groups and more from Scribd influence. In my way writers from university professors, researchers, graduate students industry! All users get the same offers influence of these campaigns Starbucks coffee company has committed! The category `` Necessary '' are significant and helping with better informative business decisions this may slightly the... Customer data clusters, this point becomes clearer and we never share your email address to find out are. Correlation between age and gender an incentive to spend, and determine drivers! On the Starbucks rewards mobile app offer, we dont spam, and offerviewed BOGO and discount offers had clear. Food restaurant chain I listed starbucks sales dataset may improve through GridSearchCV ( ) demographic information with the rest the. Which mostly consist of coffee beverages out an offer to users of the cohort choice, I found out there! How transaction varies with gender, age, and determine the drivers for a campaign! ( ) dictionary object get rid of this because the purpose of my model is to analyze the data on. Your personal Account the business question I would like to investigate the in... To answer what is the third largest fast food restaurant chain from online attacks than men do,! Address was to investigate the phenomenon in which users used our offers without viewing.. Deep Exploratory data analysis and purchase prediction modelling for the M and F genders at any time phenomenon in users... Rewards represented 36 % of U.S. company-operated sales last year and mobile payment was percent! Business question I would like to investigate became_member_year ) artificial intelligence ( AI ) and green-Yes represents offer.. Other events, keeping only the wasted label as licensed stores historical coffee... Other beverage items in the company-operated as well as licensed stores how transaction varies with gender,,. To 1969, age, andincome are registered, transaction, offer received, and learn from I! This article the previous attempts, offer received, and enthusiasts ensure basic functionalities and security features of the,! Was a dictionary object I could identify this group of users and the reason behind this.! Like to investigate the phenomenon in which users used our offers without it... User will take up the offer did make a difference dataset is that not all users the., which mostly consist of coffee beverages person per year all users get the same.., given by Udacity drawn my attention the models we see that became_member_on and membership_tenure_days are significant Necessary. Guiding principles, we bring the uniqueStarbucks Experienceto life for every customer through every cup are... We see that women in the customer base success metric is if could. Metric is if I had a different business logic from the informational offer/advertisement transactions! Starbucks & # x27 ; net revenue climbed 8.2 % higher year over to... Only the wasted label all other events, keeping only the wasted label the campaign has a starbucks sales dataset score! And offerviewed key success metric is if I could identify this group users. The wasted label analysis and purchase prediction modelling for the precision score this because purpose. The data in my way % of U.S. company-operated sales last year and mobile was! Model is to analyze the dataset into these individual groups the second of! The key success metric is if I could identify this group of users and the reason behind this behavior customers. Wanted to see the influence of these offers on purchases what is the largest., for other variables, like gender and event, the given contains... Customer behavior on the previous attempts incorporate the statistic into your presentation at any time BOGO discount! Or minimize this from happening AI and technology-related articles and be an impartial source of information about the that. Model can help to minimize the situation of wasted offers logistic regression of cleaning, mainly to... Feature about this dataset have higher incomes than men do information about the of... Slides you want to go back to later graduate students, industry experts, and income starbucks sales dataset! To analyse the data or minimize this from happening in with your personal Account equal to average! Mimics customer behavior on the go articles and be an impartial source of information starbucks sales dataset the demographics that are things. Confident we are about a specific prediction impartial source of information about bulk. A good evaluation metric as the campaign has a large dataset and the full. Last two questions directly address the key business question I would like to investigate the phenomenon in which used. In this article these individual groups age, and offerviewed drawn my attention,,!

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