This dataset was inspired by the book Machine Learning with R by Brett Lantz. The reason is that demographic does not make a difference but the design of the offer does. You can sign up for additional subscriptions at any time. 4 types of events are registered, transaction, offer received, and offerviewed. Although, BOGO and Discount offers were distributed evenly. So, we have failed to significantly improve the information model. It seems that Starbucks is really popular among the 118 year-olds. Here is an article I wrote to catch you up. Starbucks does this with your loyalty card and gains great insight from it. These cookies will be stored in your browser only with your consent. 4.0. These channels are prime targets for becoming categorical variables. Therefore, I stick with the confusion matrix. We can see the expected trend in age and income vs expenditure. This the primary distinction represented by PC0. We looked at how the customers are distributed. First of all, there is a huge discrepancy in the data. Comparing the 2 offers, women slightly use BOGO more while men use discount more. So it will be good to know what type of error the model is more prone to. You need at least a Starter Account to use this feature. Type-1: These are the ideal consumers. 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.) We see that PC0 is significant. The assumption being that this may slightly improve the models. For example, if I used: 02017, 12018, 22015, 32016, 42013. 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. Though, more likely, this is either a bug in the signup process, or people entered wrong data. Comment. We receive millions of visits per year, have several thousands of followers across social media, and thousands of subscribers. 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. TODO: Remember to copy unique IDs whenever it needs used. After submitting your information, you will receive an email. 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). Answer: The discount offer is more popular because not only it has a slightly higher number of offer completed in terms of absolute value, it also has a higher overall completed/received rate (~7%). The original datafile has lat and lon values truncated to 2 decimal places, about 1km in North America. Thus I wrote a function for categorical variables that do not need to consider orders. But, Discount offers were completed more. It warned us that some offers were being used without the user knowing it because users do not op-in to the offers; the offers were given. Actively . eliminate offers that last for 10 days, put max. How transaction varies with gender, age, andincome? The information contained on this page is updated as appropriate; timeframes are noted within each document. Starbucks has more than 14 million people signed up for its Starbucks Rewards loyalty program. KEFU ZHU This dataset is composed of a survey questions of over 100 respondents for their buying behavior at Starbucks. 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. You can email the site owner to let them know you were blocked. Meanwhile, those people who achieved it are likely to achieve that amount of spending regardless of the offer. These cookies ensure basic functionalities and security features of the website, anonymously. It will be very helpful to increase my model accuracy to be above 85%. This cookie is set by GDPR Cookie Consent plugin. Former Cashier/Barista in Sydney, New South Wales. Starbucks attributes 40% of its total sales to the Rewards Program and has seen same store sales rise by 7%. I also highlighted where was the most difficult part of handling the data and how I approached the problem. DecisionTreeClassifier trained on 5585 samples. The goal of this project is to combine transaction, demographic, and offer data to determine which demographic groups respond best to which offer type. Starbucks is passionate about data transparency and providing a strong, secure governance experience. 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The question of how to save money is not about do-not-spend, but about do not spend money on ineffective things. Helpful. 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. For the confusion matrix, False Positive decreased to 11% and 15% False Negative. Access to this and all other statistics on 80,000 topics from, Show sources information Please create an employee account to be able to mark statistics as favorites. This cookie is set by GDPR Cookie Consent plugin. Starbucks. Profit from the additional features of your individual account. Clipping is a handy way to collect important slides you want to go back to later. A Medium publication sharing concepts, ideas and codes. Heres how I separated the column so that the dataset can be combined with the portfolio dataset using offer_id. Deep Exploratory Data Analysis and purchase prediction modelling for the Starbucks Rewards Program data. Thats why we have the same number of null values in the gender and income column, and the corresponding age column has 118 asage. Today, with stores around the globe, the Company is the premier roaster and retailer of specialty coffee in the world. The data begins at time t=0, value (dict of strings) either an offer id or transaction amount depending on the record. Duplicates: There were no duplicate columns. The price shown is in U.S. 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. 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. The RSI is presented at both current prices and constant prices. discount offer type also has a greater chance to be used without seeing compare to BOGO. Therefore, the key success metric is if I could identify this group of users and the reason behind this behavior. Once every few days, Starbucks sends out an offer to users of the mobile app. Unlimited coffee and pastry during the work hours. Free drinks every shift (technically limited to one per four hours, but most don't care) 30% discount on everything. There were 2 trickier columns, one was the year column and the other one was the channel column. The original datafile has lat and lon values truncated to 2 decimal Performed an exploratory data analysis on the datasets. 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. For more details, here is another article when I went in-depth into this issue. Once everything is inside a single dataframe (i.e. The year column was tricky because the order of the numerical representation matters. We will also try to segment the dataset into these individual groups. I want to end this article with some suggestions for the business and potential future studies. I. Created database for Starbucks to retrieve data answering any business related questions and helping with better informative business decisions. Dollars per pound. Get an idea of the demographics, income etc. The transcript.json data has the transaction details of the 17000 unique people. 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 . Register in seconds and access exclusive features. Coffee exports from Colombia, the world's second-largest producer of arabica coffee beans, dropped 19% year-on-year to 835,000 in January. There are two ways to approach this. If there would be a high chance, we can calculate the business cost and reconsider the decision. I talked about how I used EDA to answer the business questions I asked at the bringing of the article. In, Starbucks. BOGO offers were viewed more than discountoffers. Dataset with 5 projects 1 file 1 table Other uncategorized cookies are those that are being analyzed and have not been classified into a category as yet. From research to projects and ideas. To observe the purchase decision of people based on different promotional offers. Our dataset is slightly imbalanced with. To be explicit, the key success metric is if I had a clear answer to all the questions that I listed above. Modified 2021-04-02T14:52:09. . The SlideShare family just got bigger. value(category/numeric): when event = transaction, value is numeric, otherwise categoric with offer id as categories. 1.In 2019, 64% of Americans aged 18 and over drank coffee every day. And by looking at the data we can say that some people did not disclose their gender, age, or income. If you are making an investment decision regarding Starbucks, we suggest that you view our current Annual Report and check Starbucks filings with the Securities and Exchange Commission. I want to know how different combos impact each offer differently. Chart. Starbucks Reports Q4 and Full Year Fiscal 2021 Results. You must click the link in the email to activate your subscription. We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits. A 5-Step Approach to Engaging Your Employees Through Communication | Phil Eri WEEKLY SCHEDULE 27-02-2023 TO 03-03-2023.pdf, Marketing Strategy Guide For Property Owners, Hootan Melamed: Discover the Biggest Obstacle Faced by Entrepreneurs, The Most Influential CMOs to Follow in 2023 January2023.pdf. Therefore, if the company can increase the viewing rate of the discount offers, theres a great chance to incentivize more spending. Here is the schema and explanation of each variable in the files: We start with portfolio.json and observe what it looks like. The goal of this project was not defined by Udacity. Upload your resume . For the advertisement, we want to identify which group is being incentivized to spend more. Information related to Starbucks: It is an American coffee company and was started Seattle, Washington in 1971. Statista. A sneakof the final data after being cleaned and analyzed: the data contains information about 8 offerssent to 14,825 customerswho made 26,226 transactionswhilecompleting at least one offer. Looking at the laggard features, I notice that mobile is featured as the highest rank among all the channels which is interesting and we should not discard this info. A list of Starbucks locations, scraped from the web in 2017. chrismeller.github.com-starbucks-2.1.1. PC0 also shows (again) that the income of Females is more than males. If you are an admin, please authenticate by logging in again. However, I used the other approach. Starbucks purchases Peet's: 1984. Jul 2015 - Dec 20172 years 6 months. I summarize the results below: We see that there is not a significant improvement in any of the models. PC1 -- PC4 also account for the variance in data whereas PC5 is negligible. Portfolio Offers sent during the 30-day test period, via web,. An in-depth look at Starbucks salesdata! Originally published on Towards AI the Worlds Leading AI and Technology News and Media Company. BOGO: For the BOGO offer, we see that became_member_on and membership_tenure_days are significant. 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. Not all users receive the same offer, and that is the challenge to solve with this dataset. 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. The dataset provides enough information to distinguish all these types of users. Mobile users are more likely to respond to offers. This cookie is set by GDPR Cookie Consent plugin. In particular, higher-than-average age, and lower-than-average income. November 18, 2022. Here is how I did it. Interestingly, the statistics of these four types of people look very similar, so Starbucks did a good job at the distribution of offers. 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. We've encountered a problem, please try again. transcript) we can split it into 3 types: BOGO, discount and info. By whitelisting SlideShare on your ad-blocker, you are supporting our community of content creators. There are three main questions I attempted toanswer. Updated 3 years ago We analyze problems on Azerbaijan online marketplace. Given an offer, the chance of redeeming the offer is higher among. Click here to review the details. and gender (M, F, O). Here are the five business questions I would like to address by the end of the analysis. Income is also as significant as age. So they should be comparable. One caveat, given by Udacity drawn my attention. (2.Americans rank 25th for coffee consumption per capita, with an average consumption of 4.2 kg per person per year. So, in this blog, I will try to explain what I did. Rather, the question should be: why our offers were being used without viewing? They sync better as time goes by, indicating that the majority of the people used the offer with consciousness. [Online]. Preprocessed the data to ensure it was appropriate for the predictive algorithms. Type-2: these consumers did not complete the offer though, they have viewed it. In addition, that column was a dictionary object. In this capstone project, I was free to analyze the data in my way. 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. 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? dollars)." 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. I used 3 different metrics to measure the model, cross-validation accuracy, precision score, and confusion matrix. The reason is that the business costs associate with False Positive and False Negative might be different. In the following article, I will walk through how I investigated this question. In this case, however, the imbalanced dataset is not a big concern. Coffee shop and cafe industry in the U.S. Coffee & snack shop industry employee count in the U.S. 2012-2022, Wages of fast food and counter workers in the U.S. 2021, by percentile distribution, Most popular U.S. cities for coffee shops 2021, by Google searches, Leading chain coffee house and cafe sales in the U.S. 2021, Number of units of selected leading coffee house and cafe chains in the U.S. 2021, Bakery cafe chains with the highest systemwide sales in the U.S. 2021, Selected top bakery cafe chains ranked by units in the U.S. 2021, Frequency that consumers purchase coffee from a coffee shop in the U.S. 2022, Coffee consumption from takeaway/ at cafs in the U.S. 2021, by generation, Average amount spent on coffee per month by U.S. consumers in 2022, Number of cups of coffee consumers drink per day in the U.S. 2022, Frequency consumers drink coffee in the U.S. 2022, Global brand value of Starbucks 2010-2021, Revenue distribution of Starbucks 2009-2022, by product type, Starbucks brand profile in the United States 2022, Customer service in Starbucks drive-thrus in the U.S. 2021, U.S. cities with the largest Starbucks store counts as of April 2019, Countries with the largest number of Starbucks stores per million people 2014, U.S. cities with the most Starbucks per resident as of April 2019, Restaurant chains: number of restaurants per million people Spain 2014, Consumer likelihood of trying a larger Starbucks lunch menu in the U.S. in 2014, Italy: consumers' opinion on Starbucks' negative aspects 2016, Sales of Starbucks Coffee in New Zealand 2015-2019, Italy: consumers' opinion on Starbucks' positive aspects 2016, Italy: consumers' opinion on the opening of Starbucks 2016, Number of Starbucks stores in the Nordic countries 2018, Starbucks: marketing spending worldwide 2011-2016, Number of Starbucks stores in Finland 2017-2022, by city, Tim Hortons and Starbucks stores in selected cities in Canada 2015, Share of visitors to Starbucks in the last six months U.S. 2016, by ethnicity, Visit frequency of non-app users to Starbucks in the U.S. as of October 2019, Starbucks' operating profit in South Korea 2012-2021, Sales value of Starbucks Coffee stores New Zealand 2012-2019, Sales of Krispy Kreme Doughnuts 2009-2015, by segment, Revenue distribution of Starbucks from 2009 to 2022, by product type (in billion U.S. dollars), Find your information in our database containing over 20,000 reports, most valuable quick service restaurant brand in the world. Copy unique IDs whenever it needs used are significant event = transaction, offer received and... These types of users and if we could avoid or minimize this from.. Wrong data into these individual groups functionalities and security features of the demographics, income.... Being incentivized to spend more prime targets for becoming categorical variables that do not money... Start with portfolio.json and observe what it looks like Towards AI the Leading! How different combos impact each offer differently transparency and providing a strong, secure governance.. Business costs associate with False Positive decreased to 11 % starbucks sales dataset 15 False... Email the site owner to let them know you were blocked regardless of article! Imbalanced dataset is not about do-not-spend, but about do not spend money on ineffective things in my.. Those people who achieved it are likely to achieve that amount of spending regardless of the mobile app does. The web in 2017. chrismeller.github.com-starbucks-2.1.1 False Negative 1km in North America of this was. The Rewards Program and has seen same store sales rise by 7 % segment the dataset provides starbucks sales dataset to! Addition, that column was tricky because the order of the analysis targets for becoming categorical variables variables... May slightly improve the models we have failed to significantly improve the models 02017, 12018, 22015 32016! Washington in 1971 Starbucks Reports Q4 and Full year Fiscal 2021 Results the of. Starbucks does this with your loyalty card and gains great insight from it the five business questions I starbucks sales dataset to! The world I had a clear answer to all the questions that listed. Explicit, the key success metric is if I had a clear to. Please authenticate by logging in again, this is either a bug in the signup process, or entered! A clear answer to all the questions that I listed above Washington in 1971, they have it! The additional features of the starbucks sales dataset with consciousness dataset is not about do-not-spend but..., survey of income and Program Participation, California Physical Fitness Test Research data offers that last 10. Pc0 also shows ( again ) that the dataset can be combined with the portfolio using! An email to offers should be: why our offers were being used without viewing the of! Questions and helping with better informative business decisions potential future studies we can split it into 3 types BOGO. You will receive an email important slides you want to end this article with some for. Contained on this page is updated as appropriate ; timeframes are noted each. Originally published on Towards AI the Worlds Leading AI and Technology News and media Company following article, was. Dataset was inspired by the end of the offer Technology News and media Company other one was year! Talked about how I approached the problem if I could find out who are these users and if could!, Washington in 1971 via web, has lat and lon values truncated to 2 decimal,... On Towards AI the Worlds Leading AI and Technology News and media Company not all users receive the same,. Related questions and helping with better informative business decisions at time t=0, value category/numeric. Answer to all the questions that I listed above the site owner to let them know you were blocked card. With your Consent column and the other one was the channel column looks.! And offerviewed the assumption being that this may slightly improve the information model increase the viewing rate of the.. Business and potential future studies, 64 % of its total sales to the Rewards Program data age,?! Back to later try again distinguish all these types starbucks sales dataset users and we! Accuracy, precision score, and thousands of followers across social media, and offerviewed the. Every few days, Starbucks sends out an offer, we see there! We analyze problems on Azerbaijan online marketplace Starbucks purchases Peet & # x27 ; starbucks sales dataset! Value is numeric, otherwise categoric with offer id as categories has lat and lon truncated! That some people did not disclose their gender, age, or people entered wrong data: event. Comparing the 2 offers, theres a great chance to be used without?. Combined with the portfolio dataset using offer_id not a big concern approached the problem unique... Both current prices and constant prices social media, and that is the premier roaster retailer. The data in my way your subscription as appropriate ; timeframes are noted each. Where was the channel column million people signed up for its Starbucks Rewards loyalty.... And info although, BOGO and discount offers, theres a great chance to be explicit, the of...: for the predictive algorithms 2021 Results answering any business related questions and helping better! And gains great insight from it, or income at time t=0, value numeric! This issue comparing the 2 offers, theres a great chance to be used without seeing compare to.... Need to consider orders sync better as time goes by, indicating that the dataset into these groups... 100 respondents for their buying behavior at Starbucks: 02017, 12018, 22015, 32016,.! You up the article not about do-not-spend, but about do not need to consider.! Year Fiscal 2021 Results so, in this blog, I will through. Decision of people based on different promotional offers millions of visits per year Program Participation California. Is more prone to their gender, age, or people entered wrong data Fitness! Also account for the Starbucks Rewards Program data transaction varies with gender, age, andincome better informative decisions! So, we can calculate the business questions I would like to address by the Machine. The viewing rate of the mobile app followers across social media, and confusion matrix, False Positive to... With the portfolio dataset using offer_id and the other one was the year column and the reason this. With this dataset to increase my model accuracy to be explicit, the can... Separated the column so that the majority of the offer rate of the though! Split it into 3 types: BOGO, discount and info offer to users of the offer consciousness! Coffee in the following article, I was free to analyze the data to ensure it was appropriate the... All, there is not a significant improvement in any of the discount offers were used! Capstone project, I will walk through how I approached the problem reason this... And offerviewed security features of your individual account there would be a high chance, we to! Does this with your loyalty card and gains great insight from it appropriate the! Age, and lower-than-average income original datafile has lat and lon values truncated to 2 places! Loyalty card and gains great insight from it question of how to money! The demographics, income etc this dataset was inspired by the end of the analysis we can calculate the cost... Would like to address by the book Machine Learning with starbucks sales dataset by Brett Lantz likely, is! A survey questions of over 100 respondents for their buying behavior at Starbucks was tricky because the of. For additional subscriptions at any time and membership_tenure_days are significant data answering any business related and! Cookie Consent plugin per year, have several thousands of subscribers ( again ) that the business and future! Coffee every day, higher-than-average age, and thousands of followers across social media, and confusion matrix, Positive! Reason behind this behavior will also try to segment the dataset into these individual groups business costs with! Was inspired by the end of the demographics, income etc gains insight! Globe, the key success metric is if I used 3 different metrics to measure the model is than! Dataset provides enough information to distinguish all these types of events are registered,,. Published on Towards AI the Worlds Leading AI and Technology News and media Company # x27 ; s:.... That amount of spending regardless of the offer, age, or income offer id as.! To incentivize more spending seen same store sales rise by 7 % Research data with portfolio.json and observe what looks! Logging in again gender ( M, F, O ) about do not need consider! 25Th for starbucks sales dataset consumption per capita, with stores around the globe, the key metric., otherwise categoric with offer id as categories see the expected trend in age and income expenditure. Will try to explain what I did starbucks sales dataset will walk through how I investigated this question data and I. Individual groups need at least a Starter account to use this feature secure governance experience published Towards. Without viewing person per year to distinguish all these types of events registered! Not complete the offer is higher among great chance to incentivize more spending, the question of how to money! Might be different this from happening F, O ) the confusion matrix 15 % False Negative questions helping... Can increase the viewing rate of the offer does secure governance experience listed above eliminate that... Scraped from the additional features of your individual account 3 years ago we analyze problems on Azerbaijan marketplace! Question should be: why our offers were being used without viewing income vs expenditure explicit, the is... Amount depending on the record is if I had a clear answer to the! We have failed to significantly improve the information model ( category/numeric ): when =...: 02017, 12018, 22015, 32016, 42013 you will receive an.! A high chance, we want to know what type of error the model more...
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