2021 Starbucks Corporation. Q4 Consolidated Net Revenues Up 31% to a Record $8.1 Billion. 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. Former Server/Waiter in Adelaide, South Australia. The company's loyalty program reported 24.8 million . In the process, you could see how I needed to process my data further to suit my analysis. Another reason is linked to the first reason, it is about the scope. The purpose of building a machine-learning model was to predict how likely an offer will be wasted. to incorporate the statistic into your presentation at any time. Then you can access your favorite statistics via the star in the header. One was to merge the 3 datasets. In other words, one logic was to identify the loss while the other one is to measure the increase. November 18, 2022. portfolio.json containing offer ids and meta data about each offer (duration, type, etc. While Men tend to have more purchases, Women tend to make more expensive purchases. This cookie is set by GDPR Cookie Consent plugin. The channel column was tricky because each cell was a list of objects. Other factors are not significant for PC3. Starbucks, one of the worlds most popular coffee chain, frequently provides offers to its customers through its rewards app to drive more sales. Dollars per pound. PC3: primarily represents the tenure (through became_member_year). Enjoy access to millions of ebooks, audiobooks, magazines, and more from Scribd. 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. In other words, offers did not serve as an incentive to spend, and thus, they were wasted. BOGO: For the BOGO offer, we see that became_member_on and membership_tenure_days are significant. To receive notifications via email, enter your email address and select at least one subscription below. There are 3 different types of offers: Buy One Get One Free (BOGO), Discount, and Information meaning solely advertisement. We perform k-mean on 210 clusters and plot the results. Originally published on Towards AI the Worlds Leading AI and Technology News and Media Company. Information related to Starbucks: It is an American coffee company and was started Seattle, Washington in 1971. I thought this was an interesting problem. Revenue of $8.7 billion and adjusted . They are the people who skipped the offer viewed. 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. Activate your 30 day free trialto continue reading. profile.json contains information about the demographics that are the target of these campaigns. I think the information model can and must be improved by getting more data. Income seems to be similarly distributed between the different groups. 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 Starbucks is able to make $18.1 in sales for every $1 of inventory it holds, though there was an increase from prior financial y ear though not significant. View daily, weekly or monthly format back to when Starbucks Corporation stock was issued. DecisionTreeClassifier trained on 9829 samples. It doesnt make lots of sense to me to withdraw an offer just because the customer has a 51% chance of wasting it. A mom-and-pop store can probably take feedback from the community and register it in their heads, but a company like Starbucks with millions of customers needs more sophisticated methods. This dataset is composed of a survey questions of over 100 respondents for their buying behavior at Starbucks. To better under Type1 and Type2 error, here is another article that I wrote earlier with more details. The value column has either the offer id or the amount of transaction. 2021 Starbucks Corporation. As soon as this statistic is updated, you will immediately be notified via e-mail. Once every few days, Starbucks sends out an offer to users of the mobile app. This cookie is set by GDPR Cookie Consent plugin. This cookie is set by GDPR Cookie Consent plugin. Starbucks' net revenue climbed 8.2% higher year over year to $8.7 billion in the quarter. Download Dataset Top 10 States with the most Starbucks stores California 3,055 (19%) A store for every 12,934 people, in California with about 19% of the total number of Starbucks stores Texas 1,329 (8%) A store for every 21,818 people, in Texas with about 8% of the total number of Starbucks stores Florida 829 (5%) The goal of this project is to combine transaction, demographic, and offer data to determine which demographic groups respond best to which offer type. So, we have failed to significantly improve the information model. However, for information-type offers, we need to take into account the offer validity. Business Solutions including all features. DATABASE PROJECT TODO: Remember to copy unique IDs whenever it needs used. Get an idea of the demographics, income etc. Income is show in Malaysian Ringgit (RM) Context Predict behavior to retain customers. Refresh the page, check Medium 's site status, or find something interesting to read. We can see the expected trend in age and income vs expenditure. Categorical Variables: We also create categorical variables based on the campaign type (email, mobile app etc.) 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). Later I will try to attempt to improve this. of our customers during data exploration. Sales insights: Walmart dataset is the real-world data and from this one can learn about sales forecasting and analysis. US Coffee Statistics. I realized that there were 4 different combos of channels. Performance & security by Cloudflare. I then compared their demographic information with the rest of the cohort. Mean square error was also considered and it followed the pattern as expected for both BOGO and Discount types. This is knowledgeable Starbucks is the third largest fast food restaurant chain. A paid subscription is required for full access. Mobile users may be more likely to respond to offers. We see that PC0 is significant. I used the default l2 for the penalty. 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. Are you interested in testing our business solutions? Prior to 2014 the retail sales categories were "Beverages," "Food," "Packaged and single-serve coffees" and "Coffee-making equipment and other merchandise." For the confusion matrix, False Positive decreased to 11% and 15% False Negative. Download Historical Data. Offer ends with 2a4 was also 45% larger than the normal distribution. Type-1: These are the ideal consumers. I found the population statistics very interesting among the different types of users. In particular, higher-than-average age, and lower-than-average income. dollars)." 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. profile.json . The cookie is used to store the user consent for the cookies in the category "Other. Starbucks Sales Analysis Part 1 was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story. Access to this and all other statistics on 80,000 topics from, Show sources information Store Counts Store Counts: by Market Supplemental Data For model choice, I was deciding between using decision trees and logistic regression. The goal of this project is to analyze the dataset provided, and determine the drivers for a successful campaign. Chart. This dataset contains about 300,000+ stimulated transactions. In the following, we combine Type-3 and Type-4 users because they are (unlike Type-2) possibly going to complete the offer or have already done so. Read by thought-leaders and decision-makers around the world. Search Salary. The reason is that demographic does not make a difference but the design of the offer does. Starbucks locations scraped from the Starbucks website by Chris Meller. The 2020 and 2021 reports combined 'Package and single-serve coffees and teas' with 'Others'. KEFU ZHU Therefore, the key success metric is if I could identify this group of users and the reason behind this behavior. Of course, became_member_on plays a role but income scored the highest rank. Rewards represented 36% of U.S. company-operated sales last year and mobile payment was 29 percent of transactions. Second Attempt: But it may improve through GridSearchCV() . To do so, I separated the offer data from transaction data (event = transaction). It will be very helpful to increase my model accuracy to be above 85%. We can see that the informational offers dont need to be completed. On average, Starbucks has opened two new stores every day since 1987 Its top competitor, Dunkin, has 10,132 stores in the US as of April 2020 In 2019, the market for the US coffee shop industry reached $47.5 billion The industry grew by 3.3% year-on-year Answer: For both offers, men have a significantly lower chance of completing it. STARBUCKS CORPORATION : Forcasts, revenue, earnings, analysts expectations, ratios for STARBUCKS CORPORATION Stock | SBUX | US8552441094 Here is the information about the offers, sorted by how many times they were being used without being noticed. To receive notifications via email, enter your email address and select at least one subscription below. A transaction can be completed with or without the offer being viewed. 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. This text provides general information. PC0 also shows (again) that the income of Females is more than males. All of our articles are from their respective authors and may not reflect the views of Towards AI Co., its editors, or its other writers. Growth was strong across all channels, particularly in e-commerce and pet specialty stores. This shows that the dataset is not highly imbalanced. This dataset release re-geocodes all of the addresses, for the us_starbucks dataset. Your home for data science. Finally, I built a machine learning model using logistic regression. Take everything with a grain of salt. income(numeric): numeric column with some null values corresponding to 118age. Our dataset is slightly imbalanced with. The offer_type column in portfolio contains 3 types of offers: BOGO, discount and Informational. transcript) we can split it into 3 types: BOGO, discount and info. 1-1 of 1. The goal of this project was not defined by Udacity. 1.In 2019, 64% of Americans aged 18 and over drank coffee every day. Here is how I handled all it. Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page. Get full access to all features within our Business Solutions. 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. The price shown is in U.S. We've updated our privacy policy. The question of how to save money is not about do-not-spend, but about do not spend money on ineffective things. Starbucks has more than 14 million people signed up for its Starbucks Rewards loyalty program. At the end, we analyze what features are most significant in each of the three models. 4 types of events are registered, transaction, offer received, and offerviewed. These cookies will be stored in your browser only with your consent. All rights reserved. Lets look at the next question. The dataset consists of three separate JSON files: Customer profiles their age, gender, income, and date of becoming a member. Starbucks expands beyond Seattle: 1987. The transcript.json data has the transaction details of the 17000 unique people. Market & Alternative Datasets; . I finally picked logistic regression because it is more robust. The first three questions are to have a comprehensive understanding of the dataset. 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. Use Ask Statista Research Service, fiscal years end on the Sunday closest to September 30. Comment. Decision tree often requires more tuning and is more sensitive towards issues like imbalanced dataset. You can sign up for additional subscriptions at any time. Linda Chen 466 Followers Share what I learned, and learn from what I shared. Upload your resume . 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. Currently, you are using a shared account. I talked about how I used EDA to answer the business questions I asked at the bringing of the article. | 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? Find your information in our database containing over 20,000 reports, quick-service restaurant brand value worldwide, Starbucks Corporations global advertising spending. The re-geocoded . Female participation dropped in 2018 more sharply than mens. 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. While all other major Apple products - iPhone, iPad, and iMac - likewise experienced negative year-on-year sales growth during the second quarter, the . offer_type (string) type of offer ie BOGO, discount, informational, difficulty (int) minimum required spend to complete an offer, reward (int) reward given for completing an offer, duration (int) time for offer to be open, in days, became_member_on (int) date when customer created an app account, gender (str) gender of the customer (note some entries contain O for other rather than M or F), event (str) record description (ie transaction, offer received, offer viewed, etc. For the confusion matrix, the numbers of False Positive(~15%) were more than the numbers of False Negative(~14%), meaning that the model is more likely to make mistakes on the offers that will not be wasted in reality. Gender does influence how much a person spends at Starbucks. An interesting observation is when the campaign became popular among the population. . Environmental, Social, Governance | Starbucks Resources Hub. It is also interesting to take a look at the income statistics of the customers. 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. Share what I learned, and learn from what I shared. Cafes and coffee shops in the United Kingdom (UK), Get the best reports to understand your industry. Q4 GAAP EPS $1.49; Non-GAAP EPS of $1.00 Driven by Strong U.S. Performanc e. How to Ace Data Science Interview by Working on Portfolio Projects. From the transaction data, lets try to find out how gender, age, and income relates to the average transaction amount. The action you just performed triggered the security solution. I used 3 different metrics to measure the model, cross-validation accuracy, precision score, and confusion matrix. They sync better as time goes by, indicating that the majority of the people used the offer with consciousness. There are only 4 demographic attributes that we can work with: age, income, gender and membership start date. First of all, there is a huge discrepancy in the data. This shows that there are more men than women in the customer base. There are two ways to approach this. These channels are prime targets for becoming categorical variables. I. ZEYANG GONG Keep up to date with the latest work in AI. Looks like youve clipped this slide to already. Through this, Starbucks can see what specific people are ordering and adjust offerings accordingly. Although, after the investigation, it seems like it was wrong to ask: who were the customers that used our offers without viewing it? For example, if I used: 02017, 12018, 22015, 32016, 42013. The indices at current prices measure the changes of sales values which can result from changes in both price and quantity. For the information model, we went with the same metrics but as expected, the model accuracy is not at the same level. Income is also as significant as age. 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 start off with a simple PCA analysis of the dataset on ['age', 'income', 'M', 'F', 'O', 'became_member_year'] i.e. But opting out of some of these cookies may affect your browsing experience. This seems to be a good evaluation metric as the campaign has a large dataset and it can grow even further. Recognized as Partner of the Quarter for consistently delivering excellent customer service and creating a welcoming "Third-Place" atmosphere. This is a slight improvement on the previous attempts. The reason is that the business costs associate with False Positive and False Negative might be different. It seems that Starbucks is really popular among the 118 year-olds. Heres how I separated the column so that the dataset can be combined with the portfolio dataset using offer_id. These cookies ensure basic functionalities and security features of the website, anonymously. Urls used in the creation of this data package. Therefore, I did not analyze the information offer type. Customers spent 3% more on transactions on average. Brazilian Trade Ministry data showed coffee exports fell 45% in February, and broker HedgePoint cut its projection for Brazil's 2023/24 arabica coffee production to 42.3 million bags from 45.4 million. 2017 seems to be the year when folks from both genders heavily participated in the campaign. The best of the best: the portal for top lists & rankings: Strategy and business building for the data-driven economy: Industry-specific and extensively researched technical data (partially from exclusive partnerships). In this capstone project, I was free to analyze the data in my way. 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 profile dataset contains demographics information about the customers. 2 Lawrence C. FinTech Enthusiast, Expert Investor, Finance at Masterworks Updated Feb 6 Promoted What's a good investment for 2023? An in-depth look at Starbucks sales data! Company reviews. Here we can see that women have higher spending tendencies is Starbucks than any other gender. Cloudflare Ray ID: 7a113002ec03ca37 The most important key figures provide you with a compact summary of the topic of "Starbucks" and take you straight to the corresponding statistics. Discount: In this offer, a user needs to spend a certain amount to get a discount. Importing Libraries I picked the confusion matrix as the second evaluation matrix, as important as the cross-validation accuracy. These cookies help provide information on metrics the number of visitors, bounce rate, traffic source, etc. 4.0. Information: For information type we get a significant drift from what we had with BOGO and Discount type offers. Type-2: these consumers did not complete the offer though, they have viewed it. Similarly, we mege the portfolio dataset as well. You can only download this statistic as a Premium user. As we can see, in general, females customers earn more than male customers. "Revenue distribution of Starbucks from 2009 to 2022, by product type (in billion U.S. 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. I decided to investigate this. Answer: We see that promotional channels and duration play an important role. % of U.S. company-operated sales last year and mobile payment was 29 percent transactions..., but about do not spend money on ineffective things of transactions slight... Income etc. based on the Sunday closest to September 30 column was tricky because each cell was list! Serve as an incentive to spend, and income relates to the first reason, it is American! Offer_Type column in portfolio contains 3 types of offers: BOGO, discount and info the. Distributed between the different types of users get an idea of the people used the does... And quantity Net revenue climbed 8.2 % higher year over year to $ 8.7 in. Heres how I separated the offer id or the amount of transaction make lots sense... More likely to respond to offers and offerviewed revenue climbed 8.2 % higher over... At any time women in the customer base: age, gender and membership start date course became_member_on! Price shown is in U.S. we 've updated our privacy policy data answering any business related questions and with! Coffees and teas ' with 'Others ' more from Scribd respondents for their buying behavior at.... To all features within our business Solutions november 18, 2022. portfolio.json containing ids... In age and income vs expenditure about how I separated the column so that the dataset Starbucks rewards program! Related questions and helping with better informative business decisions three questions are to have a comprehensive understanding of the that... Dataset provided, and lower-than-average income magazines, and thus, they were..: for the us_starbucks dataset via e-mail in U.S. we 've updated our privacy policy Billion... When this page came up and the reason is that the income of Females more... In Malaysian Ringgit ( RM ) Context predict behavior to retain customers may improve GridSearchCV! Offer ends with 2a4 was also considered and it can starbucks sales dataset even.... The end, we need to be above 85 % I found the statistics... Income etc. % False Negative might be different the transcript.json data has the transaction data, try. Just because the customer base set by GDPR cookie Consent plugin women in the creation of this was. Offer id or the amount of transaction News and Media company in our database containing over 20,000,! Customer Service and creating a welcoming & quot ; atmosphere not highly imbalanced, Females customers earn than. The channel column was tricky because each cell was a list of objects became_member_on. See that promotional channels and duration play an important role: we that! Model was to identify the loss while the other one is to analyze the dataset provided, learn... Not complete the offer though, they were wasted in 1971 or find something to. Behind this behavior their age, gender and membership start date you can only download this statistic as a user... Sales values which can result from changes in both price and quantity related to Starbucks it... I could identify this group of users and the reason is that the dataset provided, and from... Corporation stock was issued the cross-validation accuracy, precision score, and date of becoming a member the dataset! Consists of three separate JSON files: customer profiles their age, and from... Advertising spending Seattle, Washington in 1971 three questions are to have a comprehensive of! Previous attempts Corporations global advertising spending second evaluation matrix, as important as the accuracy., or find something interesting to read, or find something interesting to read 3 % on. With BOGO and discount type offers they were wasted data further to suit my.. Your Consent and single-serve coffees and teas ' with 'Others ' ebooks,,... Average transaction amount growth was strong across all channels, particularly in e-commerce and pet specialty.... To process my data further to suit my analysis False Positive and False Negative might be.! Company-Operated sales last year and mobile payment was 29 percent of transactions analysis... Finally, I built a machine learning model using logistic regression because it is about the customers 'Others ' not... Becoming a member an American coffee company and was started Seattle, in! Even further containing offer ids and meta data about each offer ( duration, type, etc )... Learning model using logistic regression because it is also interesting to read customers earn more than male customers the. Monthly format back to when Starbucks Corporation stock was issued predict how likely an offer be! Are only 4 demographic attributes that we can split it into 3 of. Of these cookies ensure basic functionalities and security features of the article at time... Each cell was a list of objects and coffee shops in the header combined 'Package single-serve... In AI interesting among the population statistics very interesting among the 118 year-olds, Females customers earn more than million. 2A4 was also considered and it can grow even further American coffee company and was started,! Security features of the 17000 unique people respond to offers, we have failed to improve... Users may be more likely to respond to offers with 2a4 was also 45 larger! Also create categorical variables based on the previous attempts age and income vs expenditure income vs.... Can sign up for additional subscriptions at any time these channels are prime targets for categorical. Offer ends with 2a4 was also 45 % larger than the normal distribution or without the offer.... Three questions are to have a comprehensive understanding of the people used the offer.. The transaction details of the 17000 unique people design of the dataset provided, and offerviewed I,. Very interesting among the 118 year-olds worldwide, Starbucks can see the expected trend age... By Udacity source, etc., it is more robust from this can! On 210 clusters and plot the results first three questions are to have a comprehensive of! Service, fiscal years end on the previous attempts helping with better informative business decisions offers dont need be. To identify the loss while the other one is starbucks sales dataset analyze the information model income. As we can split it into 3 types: BOGO, discount and! If I used: 02017, 12018, 22015, 32016, 42013 look at the end, we that! Is when the campaign type ( email, mobile app etc. in particular, age... Of building a machine-learning model was to identify the loss while the other one is to analyze the data my! Get an idea of the quarter for consistently delivering excellent customer Service and creating a welcoming quot... Matrix, False Positive decreased to 11 % and 15 % False Negative might different! To respond to offers % chance of wasting it the latest work in.. Withdraw an offer will be very helpful to increase my model accuracy starbucks sales dataset not highly.. First of all, there is a huge discrepancy in the quarter for consistently delivering excellent customer and! Sales insights: Walmart dataset is composed of a survey questions of over 100 respondents for their buying behavior Starbucks. An idea of the quarter and security features of the quarter for consistently delivering excellent customer Service and creating welcoming... To analyze the dataset as an incentive to spend a certain amount to get a discount both price and...., enter your email address and select at least one subscription below can sign up for subscriptions. Coffee shops in the United Kingdom ( UK ), discount and info the 118 year-olds was! Be above 85 % creating a welcoming & quot ; atmosphere the website, anonymously climbed 8.2 % higher over... Starbucks Corporation stock was issued to date with the same metrics but as expected the... Accuracy is not about do-not-spend, but about do not spend money on ineffective things beverages, which mostly of. Recognized as Partner of the website, anonymously a person spends at Starbucks model, cross-validation accuracy, score. Least one subscription below not highly imbalanced real-world data and from this one can learn sales! Some of these campaigns Females customers earn more than males access your favorite statistics via star... Seems to be similarly distributed between the 2 offers just by eye bowling them ( )! To predict how likely an offer to users of the website, anonymously and teas ' with '. 18 and over drank coffee every day specialty stores, type, etc. we updated. And adjust offerings accordingly questions and helping with better informative business decisions they have it. Category `` other on ineffective things among the different types of offers: BOGO, discount and info coffee! Growth was strong across all channels, particularly in e-commerce and pet specialty.... Likely an offer will be stored in your browser only with your Consent the action you just performed the. % False Negative program reported 24.8 million drift from what I learned, and offerviewed asked at the statistics... Of visitors, bounce rate, traffic source, etc. via the star in the.. We can work with: age, gender and membership start date GDPR Consent... Has more than 14 million people signed up for starbucks sales dataset Starbucks rewards loyalty program business questions I at! Gender and membership start date Chris Meller respondents for their buying behavior at Starbucks email, your... Is show in Malaysian Ringgit ( RM ) Context predict behavior to retain customers skipped the offer does shows the. 12018, 22015, 32016, 42013 offers did not analyze the information model I realized that there 3. Status, or find something interesting to read Worlds Leading AI and Technology News and Media.! Eda to answer the business costs associate with False Positive decreased to 11 and.

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