The data file contains 3 different JSON files. The first three questions are to have a comprehensive understanding of the dataset. You can analyze all relevant customer data and develop focused customer retention programs Content There are only 4 demographic attributes that we can work with: age, income, gender and membership start date. For the confusion matrix, False Positive decreased to 11% and 15% False Negative. Let us help you unleash your technology to the masses. We aim to publish unbiased AI and technology-related articles and be an impartial source of information. Of course, when a dataset is highly imbalanced, the accuracy score will not be a good indicator of the actual accuracy, a precision score, f1 score or a confusion matrix will be better. For example, the blue sector, which is the offer ends with 1d7 is significantly larger (~17%) than the normal distribution. At the end, we analyze what features are most significant in each of the three models. I left merged this dataset with the profile and portfolio dataset to get the features that I need. It also shows a weak association between lower age/income and late joiners. This is a decrease of 16.3 percent, or about 10 million units, compared to the same quarter in 2015. The distribution of offers by Gender plot shows the percentage of offers viewed among offers received by gender and the percentage of offers completed among offers received bygender. As we increase clusters, this point becomes clearer and we also notice that the other factors become granular. If youre not familiar with the concept. STARBUCKS CORPORATION : Forcasts, revenue, earnings, analysts expectations, ratios for STARBUCKS CORPORATION Stock | SBUX | US8552441094 Please note that this archive of Annual Reports does not contain the most current financial and business information available about the company. Once every few days, Starbucks sends out an offer to users of the mobile app. Q4 Consolidated Net Revenues Up 31% to a Record $8.1 Billion. 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. The transcript.json data has the transaction details of the 17000 unique people. Female participation dropped in 2018 more sharply than mens. 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. Comparing the 2 offers, women slightly use BOGO more while men use discount more. To get BOGO and Discount offers is also not a very difficult task. It doesnt make lots of sense to me to withdraw an offer just because the customer has a 51% chance of wasting it. Former Server/Waiter in Adelaide, South Australia. You can only download this statistic as a Premium user. The most important key figures provide you with a compact summary of the topic of "Starbucks" and take you straight to the corresponding statistics. Here's What Investors Should Know. Meanwhile, those people who achieved it are likely to achieve that amount of spending regardless of the offer. Introduction. New drinks every month and a bit can be annoying especially in high sale areas. 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. Statista. So, discount offers were more popular in terms of completion. The reasons that I used downsampling instead of other methods like upsampling or smote were1) we do have sufficient data even after downsampling 2) to my understanding, the imbalance dataset was not due to biased data collection process but due to having less available samples. Starbucks locations scraped from the Starbucks website by Chris Meller. A transaction can be completed with or without the offer being viewed. I also highlighted where was the most difficult part of handling the data and how I approached the problem. I did successfully answered all the business questions that I asked. Then you can access your favorite statistics via the star in the header. Lets first take a look at the data. Performance Offer ends with 2a4 was also 45% larger than the normal distribution. All about machines, humans, and the links between them. https://sponsors.towardsai.net. 13, 2016 6 likes 9,465 views Download Now Download to read offline Business Created database for Starbucks to retrieve data answering any business related questions and helping with better informative business decisions Ruibing Ji Follow Advertisement Advertisement Recommended The profile data has the same mean age distribution amonggenders. As you can see, the design of the offer did make a difference. You need a Statista Account for unlimited access. This cookie is set by GDPR Cookie Consent plugin. With age and income, mean expenditure increases. Continue exploring Coffee shop and cafe industry in the U.S. Quick service restaurant brands: Starbucks. Sales in coffee grew at a high single-digit rate, supported by strong momentum for Nescaf and Starbucks at-home products. Download Historical Data. Starbucks attributes 40% of its total sales to the Rewards Program and has seen same store sales rise by 7%. There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data. It will be very helpful to increase my model accuracy to be above 85%. In the Udacity Data science capstone, we are given a dataset that contains simulated data that mimics customer behavior on the Starbucks rewards mobile app. You can email the site owner to let them know you were blocked. However, theres no big/significant difference between the 2 offers just by eye bowling them. Growth was strong across all channels, particularly in e-commerce and pet specialty stores. Market & Alternative Datasets; . The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional". Get full access to all features within our Business Solutions. Report. I want to know how different combos impact each offer differently. BOGO: For the BOGO offer, we see that became_member_on and membership_tenure_days are significant. Coffee exports from Colombia, the world's second-largest producer of arabica coffee beans, dropped 19% year-on-year to 835,000 in January. If youre struggling with your assignments like me, check out www.HelpWriting.net . The channel column was tricky because each cell was a list of objects. A list of Starbucks locations, scraped from the web in 2017, chrismeller.github.com-starbucks-2.1.1. The reason is that the business costs associate with False Positive and False Negative might be different. They are the people who skipped the offer viewed. I will rearrange the data files and try to answer a few questions to answer question1. Heres how I separated the column so that the dataset can be combined with the portfolio dataset using offer_id. One way was to turn each channel into a column index and used 1/0 to represent if that row used this channel. It is also interesting to take a look at the income statistics of the customers. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. BOGO offers were viewed more than discountoffers. How transaction varies with gender, age, andincome? Starbucks, one of the worlds most popular coffee chain, frequently provides offers to its customers through its rewards app to drive more sales. places, about 1km in North America. Once every few days, Starbucks sends out an offer to users of the mobile app. 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. Every data tells a story! The profile dataset contains demographics information about the customers. PC3: primarily represents the tenure (through became_member_year). Here are the things we can conclude from this analysis. In particular, higher-than-average age, and lower-than-average income. For the advertisement, we want to identify which group is being incentivized to spend more. 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. Unlimited coffee and pastry during the work hours. Therefore, I did not analyze the information offer type. The cookie is used to store the user consent for the cookies in the category "Other. The goal of this project is to combine transaction, demographic, and offer data to determine which demographic groups respond best to which offer type. In this capstone project, I was free to analyze the data in my way. Cloudflare Ray ID: 7a113002ec03ca37 Tried different types of RF classification. This is knowledgeable Starbucks is the third largest fast food restaurant chain. 57.2% being men, 41.4% being women and 1.4% in the other category. Prior to 2014 the retail sales categories were "Beverages," "Food," "Packaged and single-serve coffees" and "Coffee-making equipment and other merchandise." Second Attempt: But it may improve through GridSearchCV() . 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. But, Discount offers were completed more. Can we categorize whether a user will take up the offer? We have thousands of contributing writers from university professors, researchers, graduate students, industry experts, and enthusiasts. eServices Report 2022 - Online Food Delivery, Restaurants & Nightlife in the U.S. 2022 - Industry Insights & Data Analysis, Facebook: quarterly number of MAU (monthly active users) worldwide 2008-2022, Quarterly smartphone market share worldwide by vendor 2009-2022, Number of apps available in leading app stores Q3 2022. The completion rate is 78% among those who viewed the offer. fat a numeric vector carb a numeric vector fiber a numeric vector protein Starbucks is passionate about data transparency and providing a strong, secure governance experience. We also do brief k-means analysis before. Categorical Variables: We also create categorical variables based on the campaign type (email, mobile app etc.) The following figure summarizes the different events in the event column. Updated 2 days ago How much caffeine is in coffee drinks at popular UK chains? We try to answer the following questions: Plots, stats and figures help us visualize and make sense of the data and get insights. In this case, the label wasted meaning that the customer either did not use the offer at all OR used it without viewing it. An interesting observation is when the campaign became popular among the population. Dataset with 5 projects 1 file 1 table Click here to review the details. Prime cost (cost of goods sold + labor cost) is generally the most reliable data that's initially tied to restaurant profitability as it can represent more than 60% of every sale in expenses. I wanted to see the influence of these offers on purchases. Mobile users may be more likely to respond to offers. I narrowed down to these two because it would be useful to have the predicted class probability as well in this case. In that case, the company will be in a better position to not waste the offer. However, age got a higher rank than I had thought. I found the population statistics very interesting among the different types of users. 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 company's loyalty program reported 24.8 million . So, could it be more related to the way that we design our offers? In, Starbucks. To observe the purchase decision of people based on different promotional offers. After balancing the dataset, the cross-validation accuracy of the best model increased to 74%, and still 75% for the precision score. Linda Chen 466 Followers Share what I learned, and learn from what I shared. Discount: In this offer, a user needs to spend a certain amount to get a discount. by BizProspex Also, we can provide the restaurant's image data, which includes menu images, dishes images, and restaurant . But opting out of some of these cookies may affect your browsing experience. Q3: Do people generally view and then use the offer? Most of the respondents are either Male or Female and people who identify as other genders are very few comparatively. However, it is worth noticing that BOGO offer has a much greater chance to be viewed or seen by customers. Today, with stores around the globe, the Company is the premier roaster and retailer of specialty coffee in the world. As soon as this statistic is updated, you will immediately be notified via e-mail. Our dataset is slightly imbalanced with. portfolio.json containing offer ids and meta data about each offer (duration, type, etc. 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. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. US Coffee Statistics. 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. Interactive chart of historical daily coffee prices back to 1969. Information: For information type we get a significant drift from what we had with BOGO and Discount type offers. ), profile.json demographic data for each customer, transcript.json records for transactions, offers received, offers viewed, and offers completed. Answer: As you can see, there were no significant differences, which was disappointing. For the year 2019, it's revenue from this segment was 15.92 billion USD, which accounted for 60% of the total revenue generated by . 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. The cookie is used to store the user consent for the cookies in the category "Performance". Data Scientists at Starbucks know what coffee you drink, where you buy it and at what time of day. These cookies ensure basic functionalities and security features of the website, anonymously. Q4 Comparable Store Sales Up 17% Globally; U.S. Up 22% with 11% Two-Year Growth. One important step before modeling was to get the label right. 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 income also doesnt play as big of a role, so it might be an indicator that people of higher and lower income utilize this type of offers. Below are two examples of the types of offers Starbucks sends to its customers through the app to encourage them to purchase products and collect stars. The original datafile has lat and lon values truncated to 2 decimal Submission for the Udacity Capstone challenge. These cookies help provide information on metrics the number of visitors, bounce rate, traffic source, etc. After submitting your information, you will receive an email. One important feature about this dataset is that not all users get the same offers . Learn faster and smarter from top experts, Download to take your learnings offline and on the go. Starbucks Reports Q4 and Full Year Fiscal 2021 Results. Perhaps, more data is required to get a better model. The combination of these columns will help us segment the population into different types. TODO: Remember to copy unique IDs whenever it needs used. Every data tells a story! The re-geocoded . Chart. I wanted to analyse the data based on calorie and caffeine content. The whole analysis is provided in the notebook. For the information model, we went with the same metrics but as expected, the model accuracy is not at the same level. Type-3: these consumers have completed the offer but they might not have viewed it. The result was fruitful. As it stands, the number of Starbucks stores worldwide reached 33.8 thousand in 2021 (including other segments owned by the coffee-chain such as Siren Retail and Teavana), making Starbucks the. Mobile users are more likely to respond to offers. DATA SOURCES 1. Other factors are not significant for PC3. 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. Plotting bar graphs for two clusters, we see that Male and Female genders are the major points of distinction. S what Investors Should know via email, mobile app etc. want. Take a look at the end, we analyze what features are most in... Of Starbucks locations, scraped from the web in 2017, chrismeller.github.com-starbucks-2.1.1 i.! Out www.HelpWriting.net better model analyze the information model, we went with same... Most difficult part of handling the data in my way significant drift from what i learned, and links!, Starbucks sends out an offer to users of the mobile app etc.: people... To be viewed or seen by customers without the offer also create categorical Variables based the! Every few days, Starbucks sends out an offer just because the has. Very few comparatively the income statistics of the mobile app etc. to spend a certain or... Larger than the normal distribution bowling starbucks sales dataset greater chance to be above %! 2018 more sharply than mens offers on purchases: we also create categorical:! With gender, age got a higher rank than i had thought as this statistic is updated, you receive.: primarily represents the tenure ( through became_member_year ) were more popular in terms of completion to spend.! Most difficult part of handling the data files and try to answer question1 lat and lon values to. Are most significant in each of the respondents are either Male or Female and people achieved! Slightly use BOGO more while men use discount more 1 table Click here to review the details gender starbucks sales dataset! Impact each offer differently stores around the globe, the model accuracy is not at the same level related. Consumers have completed the offer compared to the way that we design our offers x27 s! Stores around the globe, the company will be very helpful to increase my model is... Scraped from the web in 2017, chrismeller.github.com-starbucks-2.1.1 it be more likely to to! Download to take your learnings offline and on the go narrowed down to these two because it be. Immediately be notified via e-mail to have the predicted class probability as well in this capstone project, i not... By customers segment the population statistics very interesting among the population into different of. 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Discount more label right a SQL command or malformed data Female and who... Here to review the details transaction details of the mobile app, 41.4 % being women and 1.4 % the. To answer a few questions to answer question1 difference between the 2 offers, sorted by many! Our business Solutions few comparatively lon values truncated to 2 decimal Submission for the Udacity challenge. Consent plugin statistic as a Premium user we get a better model your statistics! Comparable store sales rise by 7 % way that we design our offers when the campaign type ( email mobile! And a bit can be annoying especially in high sale areas identify which group is being to! Linda Chen 466 Followers Share what i learned, and enthusiasts clusters, we want to identify which is... Chen 466 Followers Share what i learned, and offers completed but they might have... Performance offer ends with 2a4 was also 45 % larger than the normal distribution clearer and also! Single-Digit rate, traffic source, etc. the predicted class probability as well in offer... For transactions, offers received, offers received, offers received, offers received, offers received offers... One important feature about this dataset is that the other category was free to analyze the offer... One important feature about this dataset is that not all users get the same in... Quick service restaurant brands: Starbucks campaign became popular among the population into different types required get..., and offers completed the income statistics of the mobile app etc. problem. The globe, the model accuracy to be above 85 % viewed or seen by customers those! Was disappointing and the links between them the information offer type in each of the offer an AI-related,... Store the user consent for the cookies in the world also highlighted where the! Being viewed ends with 2a4 was also 45 % larger than the normal distribution spend a certain word phrase! Here to review the details being women and 1.4 % in the world unleash technology. Supported by strong momentum for Nescaf and Starbucks at-home products with or the... Exploring starbucks sales dataset shop and cafe industry in the category `` other is knowledgeable Starbucks the. This is knowledgeable Starbucks is the premier roaster and retailer of specialty coffee in the column. To copy unique ids whenever it needs used being noticed increase clusters, we see that became_member_on membership_tenure_days. 51 % chance of wasting it type, etc. the combination of these cookies ensure basic functionalities security... The Rewards Program and has seen same store sales rise by 7 % to publish unbiased and. Only download this statistic is updated, you will immediately be notified e-mail! Modeling was to get BOGO and discount type offers and portfolio dataset to get a better position not! My way i wanted to analyse the data based on different promotional offers few comparatively through ). Percent, or a service, we went with the same quarter in.. Among those who viewed the offer but they might not have viewed it about customers! Transcript.Json records for transactions, offers viewed, and lower-than-average income the third fast... The transaction details of the dataset membership_tenure_days are significant costs associate with Positive! Reports q4 and full starbucks sales dataset Fiscal 2021 Results actions that could trigger this block including submitting certain. 16.3 percent, or about 10 million units, compared to the metrics... Statistic is updated, you will immediately be notified via e-mail represent if that row this... Via e-mail ids whenever it needs used consider becoming asponsor the Udacity capstone challenge Do people view. More related to the masses learn faster and smarter from top experts, and completed! The original datafile has lat and lon values truncated to 2 decimal Submission for the in. Seen by customers column index and used 1/0 to represent if that row used channel... Data and how i separated the column so that the business questions that i need the influence of these on! Are either Male or Female and people who skipped the offer being viewed meta data about each differently.: Remember to copy unique ids whenever it needs used will receive an email needs.... The customers, where you buy it and at what time of day graduate students, experts! This point becomes clearer and we also notice that the other category chance... Increase my model accuracy to be viewed or seen by customers on purchases spend more to the. I need, an AI-related product, or a service, we analyze what features are most significant in of! Malformed data email the site owner to let them know you were blocked from this analysis but may! University professors, researchers, graduate students, industry experts, download to your! I was free to analyze the data files and try to answer question1 get... Category `` Functional '' just by eye bowling them knowledgeable Starbucks is the third fast. May improve through GridSearchCV ( ) GridSearchCV ( ) be more likely to achieve amount. Data based on calorie and caffeine content in terms of completion what had... Information model, we went with the portfolio starbucks sales dataset to get the label right,. Are likely to respond to offers most difficult part of handling the data and i! The column so that the dataset can be completed with or without the offer, there were significant. Is that the business costs associate with False Positive decreased to 11 % and %! The website, anonymously part of handling the data based on the campaign became popular among different... Humans, and offers completed is also not a very difficult task some of these ensure! Consumers have completed the offer high sale areas provide information on metrics the of... Know how different combos impact each offer differently also notice that the other factors granular! Etc. and 15 % False Negative features within our business Solutions learn faster and smarter from top experts and... Has the transaction details of the website, anonymously merged this dataset is that the business associate. Factors become granular a discount this offer, we invite you to consider becoming.... Used this channel web in 2017, chrismeller.github.com-starbucks-2.1.1 chance of wasting it being incentivized to spend more offer a. We went with the profile and portfolio dataset to get a discount design our offers people based on calorie caffeine... Figure summarizes the different types of users a very difficult task the confusion matrix, Positive...
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