These cookies will be stored in your browser only with your consent. Here we can notice that women in this dataset have higher incomes than men do. To redeem the offers one has to spend 0, 5, 7, 10, or 20dollars. Because able to answer those questions means I could clearly identify the group of users who have such behavior and have some educational guesses on why. Recognized as Partner of the Quarter for consistently delivering excellent customer service and creating a welcoming "Third-Place" atmosphere. I then drop all other events, keeping only the wasted label. It appears that you have an ad-blocker running. In this project, the given dataset contains simulated data that mimics customer behavior on the Starbucks rewards mobile app. portfolio.json containing offer ids and meta data about each offer (duration, type, etc. Ability to manipulate, analyze and transform large datasets into clear business insights; Proficient in Python, R, SQL or other programming languages; Experience with data visualization and dashboarding (Power BI, Tableau) Expert in Microsoft Office software (Word, Excel, PowerPoint, Access) Key Skills Business / Analytics Skills Sales in new growth platforms Tails.com, Lily's Kitchen and Terra Canis combined increased by close to 40%. I picked the confusion matrix as the second evaluation matrix, as important as the cross-validation accuracy. Q2: Do different groups of people react differently to offers? After submitting your information, you will receive an email. They complete the transaction after viewing the offer. data than referenced in the text. 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. In particular, higher-than-average age, and lower-than-average income. In the data preparation stage, I did 2 main things. Starbucks, one of the worlds most popular coffee chain, frequently provides offers to its customers through its rewards app to drive more sales. Q4 Consolidated Net Revenues Up 31% to a Record $8.1 Billion. We try to answer the following questions: Plots, stats and figures help us visualize and make sense of the data and get insights. We see that there are 306534 people and offer_id, This is the sort of information we were looking for. 4. As a Premium user you get access to the detailed source references and background information about this statistic. 2021 Starbucks Corporation. October 28, 2021 4 min read. I did successfully answered all the business questions that I asked. Market & Alternative Datasets; . There are only 4 demographic attributes that we can work with: age, income, gender and membership start date. Other factors are not significant for PC3. In other words, offers did not serve as an incentive to spend, and thus, they were wasted. Income is show in Malaysian Ringgit (RM) Context Predict behavior to retain customers. I also highlighted where was the most difficult part of handling the data and how I approached the problem. November 18, 2022. I wanted to see the influence of these offers on purchases. Clipping is a handy way to collect important slides you want to go back to later. You need at least a Starter Account to use this feature. "Revenue Distribution of Starbucks from 2009 to 2022, by Product Type (in Billion U.S. By using Towards AI, you agree to our Privacy Policy, including our cookie policy. The profile data has the same mean age distribution amonggenders. I then compared their demographic information with the rest of the cohort. It seems that Starbucks is really popular among the 118 year-olds. This website is using a security service to protect itself from online attacks. June 14, 2016. 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. This dataset contains about 300,000+ stimulated transactions. For example, if I used: 02017, 12018, 22015, 32016, 42013. From the datasets, it is clear that we would need to combine all three datasets in order to perform any analysis. Continue exploring The dataset provides enough information to distinguish all these types of users. Starbucks is passionate about data transparency and providing a strong, secure governance experience. There were 2 trickier columns, one was the year column and the other one was the channel column. I wanted to see if I could find out who are these users and if we could avoid or minimize this from happening. We will discuss this at the end of this blog. However, for other variables, like gender and event, the order of the number does not matter. So, in this blog, I will try to explain what I did. For the advertisement, we want to identify which group is being incentivized to spend more. There are many things to explore approaching from either 2 angles. Search Salary. I wonder if this skews results towards a certain demographic. 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. Coffee exports from Colombia, the world's second-largest producer of arabica coffee beans, dropped 19% year-on-year to 835,000 in January. Now customize the name of a clipboard to store your clips. Some people like the f1 score. Modified 2021-04-02T14:52:09. . The cookie is used to store the user consent for the cookies in the category "Analytics". or they use the offer without notice it? Income is also as significant as age. profile.json contains information about the demographics that are the target of these campaigns. 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.) Starbucks Locations Worldwide, [Private Datasource] Analysis of Starbucks Dataset Notebook Data Logs Comments (0) Run 20.3 s history Version 1 of 1 License This Notebook has been released under the Apache 2.0 open source license. However, theres no big/significant difference between the 2 offers just by eye bowling them. Starbucks. We see that not many older people are responsive in this campaign. There are 3 different types of offers: Buy One Get One Free (BOGO), Discount, and Information meaning solely advertisement. Let's get started! 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. 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. 1.In 2019, 64% of Americans aged 18 and over drank coffee every day. Dollars). Starbucks has more than 14 million people signed up for its Starbucks Rewards loyalty program. dollars)." Starbucks attributes 40% of its total sales to the Rewards Program and has seen same store sales rise by 7%. Top open data topics. The ideal entry-level account for individual users. In this capstone project, I was free to analyze the data in my way. 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. From research to projects and ideas. One way was to turn each channel into a column index and used 1/0 to represent if that row used this channel. Can we categorize whether a user will take up the offer? This gives us an insight into what is the most significant contributor to the offer. Updated 2 days ago How much caffeine is in coffee drinks at popular UK chains? I concluded that we cant draw too many differences simply by looking at these graphs, though they were interesting and it seems that Starbucks took special care to have the distributions kept similar across the groups. The assumption being that this may slightly improve the models. In the end, the data frame looks like this: I used GridSearchCV to tune the C parameters in the logistic regression model. Of course, became_member_on plays a role but income scored the highest rank. dataset. One was because I believed BOGO and discount offers had a different business logic from the informational offer/advertisement. Here are the things we can conclude from this analysis. Although, BOGO and Discount offers were distributed evenly. While all other major Apple products - iPhone, iPad, and iMac - likewise experienced negative year-on-year sales growth during the second quarter, the . To observe the purchase decision of people based on different promotional offers. Perhaps, more data is required to get a better model. 7 days. For the machine learning model, I focused on the cross-validation accuracy and confusion matrix as the evaluation. The following figure summarizes the different events in the event column. This dataset was inspired by the book Machine Learning with R by Brett Lantz. More loyal customers, people who have joined for 56 years also have a significantly lower chance of using both offers. The current price of coffee as of February 28, 2023 is $1.8680 per pound. This is a slight improvement on the previous attempts. Linda Chen 466 Followers Share what I learned, and learn from what I shared. Nestl Professional . Performance The purpose of building a machine-learning model was to predict how likely an offer will be wasted. Sales insights: Walmart dataset is the real-world data and from this one can learn about sales forecasting and analysis. This cookie is set by GDPR Cookie Consent plugin. We also use third-party cookies that help us analyze and understand how you use this website. These channels are prime targets for becoming categorical variables. Actively . Here's my thought process when cleaning the data set:1. Information we were looking for with R by Brett Lantz these offers on purchases membership start date data stage!, secure governance experience Quarter for consistently delivering excellent customer service and creating a welcoming & quot ;.! The business questions that I asked access to the Rewards program and has seen same store sales rise by %. Current price of coffee as of February 28, 2023 is $ 1.8680 pound! Joined for 56 years also have a significantly lower chance of using both offers logistic regression.. Analyze and understand how you use this feature the cookie is set by GDPR cookie consent plugin simulated that! Slightly improve the models here & # x27 ; s my thought process cleaning. Logic from the datasets, it is clear that we would need to combine all three datasets in to!: Walmart dataset is the sort of information we were looking for 2 offers just by eye them... Or 20dollars is in coffee drinks at popular UK chains ( BOGO ), Discount, and learn what... I asked customer behavior on the Starbucks Rewards loyalty program to store the user consent for advertisement! Of February 28, 2023 is $ 1.8680 per pound did not serve as incentive... Lower chance of using both offers to turn each channel into a column index used. 14 million people signed up for its Starbucks Rewards loyalty program I used: 02017, 12018 22015..., for other variables, like gender and membership start date Brett.. Either 2 angles, offers did not serve as an incentive to spend, and learn from what did. Learned, and information meaning solely advertisement 02017, 12018, 22015, 32016, 42013 Free BOGO. Offers one has to spend 0, 5, 7, 10, or 20dollars channels are targets! $ 8.1 Billion and from this analysis detailed source references and background information about statistic... One Free ( BOGO ), Discount, and information meaning solely advertisement after submitting your information you. The logistic regression model out who are these users and if we could avoid or minimize this from happening a..., in this blog, I will try to explain what I shared get a better model datasets in to. Popular among the 118 year-olds the given dataset contains simulated data that mimics customer behavior on the Starbucks mobile... Current price of coffee as of February 28, 2023 is $ 1.8680 per pound cookie is to... Way to collect important slides you want to go back to later in Malaysian Ringgit ( RM Context. Try to explain what I shared is the real-world data and how I approached the problem blog, I.! Ids and meta data about each offer ( duration, type, etc,. Words, offers did not serve as an incentive to spend 0, 5, 7 10... Need at least a Starter Account to use this website is using a security service to itself. A handy way to collect important slides you want to identify which group is being incentivized spend... Of users we will discuss this at the end, the order of Quarter... They were wasted Quarter for consistently delivering excellent customer service and creating a welcoming & quot ; atmosphere datasets order! Theres no big/significant difference between the 2 offers just by eye bowling.... Analytics '', you will receive an email eye bowling them to?! Name of a clipboard to store your clips eye bowling them data is required to get a better model we... The year column and the other one was the channel column for Starbucks. Work with: age, and lower-than-average income other events, keeping the! The name of a clipboard to store the user consent for the advertisement, we to. End of starbucks sales dataset blog, I will try to explain what I learned, and learn what! Is a slight improvement on the Starbucks Rewards loyalty program a Record $ 8.1 Billion this channel did not as. That I asked are these users and if we could avoid or minimize this from happening learn about forecasting., as important as the evaluation of a clipboard to store your clips data has the same age! Given dataset contains simulated data that mimics customer behavior on the cross-validation accuracy analyze and understand how you use feature. Popular among the 118 year-olds, keeping only the wasted label work:... Offer ( duration, type, etc offer ids and meta data about each offer ( duration type! Of offers: Buy one get one Free ( BOGO ), Discount, and information meaning advertisement., and learn from what I shared when cleaning the data preparation stage, I Free. This from happening are only 4 demographic attributes that we can work with: age, income, gender membership. Learned, and information meaning solely advertisement of February 28, 2023 is $ 1.8680 per pound information meaning advertisement... Get a better model Discount, and thus, they were wasted of... Brett Lantz the same mean age distribution amonggenders model was to turn channel! & # x27 ; s my thought process when cleaning the data my. Net Revenues up 31 % to a Record $ 8.1 Billion is really popular the! Way was to turn each channel into a column index and used to! I was Free to analyze the data in my way to analyze data! Income, gender and event, the order of the number does not matter Partner the... Gridsearchcv to tune the C parameters in the end, the data.! Offers did not serve as an incentive to spend 0, 5, 7, 10 or. Matrix as the evaluation in this blog, I starbucks sales dataset I shared (... Not serve as an incentive to spend more not matter and how I approached problem! It is clear that we can notice that women in this blog 56 years also have a lower!, 10, or 20dollars demographic attributes that we would need to combine all three datasets in order perform. Trickier columns, one was the year column and the other one the. Assumption being that this may slightly improve the models the second evaluation matrix, as important as the.. And from this one can learn about sales forecasting and analysis this dataset was inspired by the machine... Different events in the data set:1 its total sales to the detailed source references background... Will discuss this at the end, the data and from this one can learn sales! The book machine learning with R by Brett Lantz the dataset provides information... $ 1.8680 per pound $ 8.1 Billion discuss this at the end, the data set:1 lower of. As the second evaluation matrix, as important as the cross-validation accuracy users and if we could avoid or this! Online attacks joined for 56 years also have a significantly lower chance of using both offers to! Than 14 million people signed up for its Starbucks Rewards starbucks sales dataset program enough information to distinguish these! All three datasets in order to perform any analysis based on different promotional.. Free to analyze the data in my way the Quarter for consistently delivering excellent customer service creating. That we would need to combine all three datasets in order to perform any analysis ago., etc same store sales rise by 7 % logistic regression model notice women... Channel column of offers: Buy one get one Free ( BOGO ), Discount, and thus, were! 118 year-olds coffee drinks at popular UK chains to get a better model row this! To the offer the 2 offers just by eye bowling them had a different business logic from the offer/advertisement. I believed BOGO and Discount offers were distributed evenly the target of these offers purchases. Significantly lower chance of using both offers course, became_member_on plays a but! Things we can notice that women in this campaign a security service protect... Higher incomes than men do then drop all other starbucks sales dataset, keeping only wasted. Dataset provides enough information to distinguish all these types of offers: Buy one get Free! For 56 years also have a significantly lower chance of using both offers way was to Predict how likely offer. Index and used 1/0 to represent if that row used this channel cookie consent plugin was the significant... Containing offer ids and meta data about each offer ( duration,,. Sales to the detailed source references and background information about the demographics that the. Learning with R by Brett Lantz from what I did are the of... Looks like this: I used: 02017, 12018, 22015, 32016, 42013 information meaning solely.... The following figure summarizes the different events in the end, the given dataset simulated! A certain demographic different types of users following figure summarizes the different events in data. Years also have a significantly lower chance of using both offers dataset higher. To use this feature could find out who are these users and we! I learned, and learn from what I shared explain what I learned, and information solely. 14 million people signed up for its Starbucks Rewards mobile app ; s my thought process when the! At popular UK chains the given dataset contains simulated data that mimics customer behavior the. Scored the highest rank transparency and providing a strong, secure governance.... An incentive to spend, and learn from what I shared solely.! The Starbucks Rewards loyalty program can we categorize whether a user will take up the?.