Here is the schema and explanation of each variable in the files: We start with portfolio.json and observe what it looks like. Urls used in the creation of this data package. Keep up to date with the latest work in AI. Initially, the company was known as the "Starbucks coffee, tea, and spices" before renaming it as a Starbucks coffee company. Since this takes a long time to run, I ran them once, noted down the parameters and fixed them in the classifier. Updated 3 years ago Starbucks location data can be used to find location intelligence on the expansion plans of the coffeehouse chain During the second quarter of 2016, Apple sold 51.2 million iPhones worldwide. Continue exploring profile.json contains information about the demographics that are the target of these campaigns. Thus, if some users will spend at Starbucks regardless of having offers, we might as well save those offers. Once these categorical columns are created, we dont need the original columns so we can safely drop them. Submission for the Udacity Capstone challenge. The value column has either the offer id or the amount of transaction. Get full access to all features within our Business Solutions. Market & Alternative Datasets; . How offers are utilized among different genders? The Reward Program is available on mobile devices as the Starbucks app, and has seen impressive membership and growth since 2008, with multiple iterations on its original form. You can analyze all relevant customer data and develop focused customer retention programs Content Age also seems to be similarly distributed, Membership tenure doesnt seem to be too different either. or they use the offer without notice it? Lets first take a look at the data. 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. As soon as this statistic is updated, you will immediately be notified via e-mail. We will get rid of this because the population of 118 year-olds is not insignificant in our dataset. Of course, became_member_on plays a role but income scored the highest rank. Performance For the advertisement, we want to identify which group is being incentivized to spend more. The company also logged 5% global comparable-store sales growth. This seems to be a good evaluation metric as the campaign has a large dataset and it can grow even further. Type-1: These are the ideal consumers. It seems that Starbucks is really popular among the 118 year-olds. Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page. We also use third-party cookies that help us analyze and understand how you use this website. This text provides general information. Interactive chart of historical daily coffee prices back to 1969. Introduction. The whole analysis is provided in the notebook. 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. Therefore, if the company can increase the viewing rate of the discount offers, theres a great chance to incentivize more spending. Portfolio Offers sent during the 30-day test period, via web,. Data Sets starbucks Return to the view showing all data sets Starbucks nutrition Description Nutrition facts for several Starbucks food items Usage starbucks Format A data frame with 77 observations on the following 7 variables. Data visualization: Visualization of the data is an important part of the whole data analysis process and here along with seaborn we will be also discussing the Plotly library. So it will be good to know what type of error the model is more prone to. Perhaps, more data is required to get a better model. PC0 also shows (again) that the income of Females is more than males. Sales insights: Walmart dataset is the real-world data and from this one can learn about sales forecasting and analysis. economist makeover monday economy mcdonalds big mac index +1. Currently, you are using a shared account. by BizProspex Also, we can provide the restaurant's image data, which includes menu images, dishes images, and restaurant . Most of the offers as we see, were delivered via email and the mobile app. US Coffee Statistics. The goal of this project is to analyze the dataset provided, and determine the drivers for a successful campaign. So they should be comparable. The cookie is used to store the user consent for the cookies in the category "Performance". Modified 2021-04-02T14:52:09, Resources | Packages | Documentation| Contacts| References| Data Dictionary. Discount: In this offer, a user needs to spend a certain amount to get a discount. 2 Lawrence C. FinTech Enthusiast, Expert Investor, Finance at Masterworks Updated Feb 6 Promoted What's a good investment for 2023? Directly accessible data for 170 industries from 50 countries and over 1 million facts: Get quick analyses with our professional research service. fat a numeric vector carb a numeric vector fiber a numeric vector protein Unbeknown to many, Starbucks has invested significantly in big data and analytics capabilities in order to determine the potential success of its stores and products, and grow sales. We try to answer the following questions: Plots, stats and figures help us visualize and make sense of the data and get insights. The data begins at time t=0, value (dict of strings) either an offer id or transaction amount depending on the record. The accuracy score is important because the purpose of my model is to help the company to predict when an offer might be wasted. A listing of all retail food stores which are licensed by the Department of Agriculture and Markets. The original datafile has lat and lon values truncated to 2 decimal places, about 1km in North America. Through our unwavering commitment to excellence and our guiding principles, we bring the uniqueStarbucks Experienceto life for every customer through every cup. data than referenced in the text. Through this, Starbucks can see what specific people are ordering and adjust offerings accordingly. 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. If youre not familiar with the concept. Lets look at the next question. We can see the expected trend in age and income vs expenditure. Learn faster and smarter from top experts, Download to take your learnings offline and on the go. 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. Take everything with a grain of salt. age for instance, has a very high score too. After balancing the dataset, the cross-validation accuracy of the best model increased to 74%, and still 75% for the precision score. The long and difficult 13- year journey to the marketplace for Pfizers viagr appliedeconomicsintroductiontoeconomics-abmspecializedsubject-171203153213.pptx, No public clipboards found for this slide, Enjoy access to millions of presentations, documents, ebooks, audiobooks, magazines, and more. 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. of our customers during data exploration. Starbucks Offer Dataset Udacity Capstone | by Linda Chen | Towards Data Science 500 Apologies, but something went wrong on our end. In this capstone project, I was free to analyze the data in my way. Categorical Variables: We also create categorical variables based on the campaign type (email, mobile app etc.) This dataset contains about 300,000+ stimulated transactions. Q4 GAAP EPS $1.49; Non-GAAP EPS of $1.00 Driven by Strong U.S. Performanc e. Former Server/Waiter in Adelaide, South Australia. We start off with a simple PCA analysis of the dataset on ['age', 'income', 'M', 'F', 'O', 'became_member_year'] i.e. So, in conclusion, to answer What is the spending pattern based on offer type and demographics? You need a Statista Account for unlimited access. precise. Read by thought-leaders and decision-makers around the world. (November 18, 2022). They also analyze data captured by their mobile app, which customers use to pay for drinks and accrue loyalty points. An offer can be merely an advertisement for a drink or an actual offer such as a discount or BOGO ( 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. Starbucks Offers Analysis The capstone project for Udacity's Data Scientist Nanodegree Program Project Overview This is a capstone project of the Data Scientist Nanodegree Program of Udacity. However, for other variables, like gender and event, the order of the number does not matter. I realized that there were 4 different combos of channels. Looks like youve clipped this slide to already. I summarize the results below: We see that there is not a significant improvement in any of the models. Income is also as significant as age. We receive millions of visits per year, have several thousands of followers across social media, and thousands of subscribers. Originally published on Towards AI the Worlds Leading AI and Technology News and Media Company. 754. It also appears that there are not one or two significant factors only. In the data preparation stage, I did 2 main things. The dataset consists of three separate JSON files: Customer profiles their age, gender, income, and date of becoming a member. As a part of Udacity's Data Science nano-degree program, I was fortunate enough to have a look at Starbucks ' sales data. As we can see, in general, females customers earn more than male customers. Medical insurance costs. The downside is that accuracy of a larger dataset may be higher than for smaller ones. The original datafile has lat and lon values truncated to 2 decimal One was because I believed BOGO and discount offers had a different business logic from the informational offer/advertisement. From the Average offer received by gender plot, we see that the average offer received per person by gender is nearly thesame. I will rearrange the data files and try to answer a few questions to answer question1. This was the most tricky part of the project because I need to figure out how to abstract the second response to the offer. the mobile app sends out an offer and/or informational material to its customer such as discounts (%), BOGO Buy one get one free, and informational . Starbucks Reports Q4 and Full Year Fiscal 2021 Results. Performance & security by Cloudflare. Dataset with 108 projects 1 file 1 table. This means that the company 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%). | 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? To receive notifications via email, enter your email address and select at least one subscription below. Although, BOGO and Discount offers were distributed evenly. 195.242.103.104 We looked at how the customers are distributed. It also shows a weak association between lower age/income and late joiners. 2017 seems to be the year when folks from both genders heavily participated in the campaign. We can see that the informational offers dont need to be completed. I used 3 different metrics to measure the model, cross-validation accuracy, precision score, and confusion matrix. As we increase clusters, this point becomes clearer and we also notice that the other factors become granular. How transaction varies with gender, age, andincome? There were 2 trickier columns, one was the year column and the other one was the channel column. The output is documented in the notebook. Thus, the model can help to minimize the situation of wasted offers. Dollars). 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 Let us help you unleash your technology to the masses. Second Attempt: But it may improve through GridSearchCV() . Since 1971, Starbucks Coffee Company has been committed to ethically sourcing and roasting high-qualityarabicacoffee. PC3: primarily represents the tenure (through became_member_year). 4 types of events are registered, transaction, offer received, and offerviewed. I will follow the CRISP-DM process. 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. You can sign up for additional subscriptions at any time. Number of McDonald's restaurants worldwide 2005-2021, Number of restaurants in the U.S. 2011-2018, Average daily rate of hotels in the U.S. 2001-2021, Global tourism industry - statistics & facts, Hotel industry worldwide - statistics & facts, Profit from additional features with an Employee Account. However, I used the other approach. Comparing the 2 offers, women slightly use BOGO more while men use discount more. 1.In 2019, 64% of Americans aged 18 and over drank coffee every day. Plotting bar graphs for two clusters, we see that Male and Female genders are the major points of distinction. 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? This shows that there are more men than women in the customer base. Learn more about how Statista can support your business. DATA SOURCES 1. RUIBING JI You must click the link in the email to activate your subscription. Therefore, the higher accuracy, the better. Elasticity exercise points 100 in this project, you are asked. Sep 8, 2022. 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. Lets recap the columns for better understanding: We can make a plot of what percentage of the distributed offer was BOGO, Discount, and Informational and finally find out what percentage of the offers were received, viewed, and completed. Expanding a bit more on this. Share what I learned, and learn from what I shared. One way was to turn each channel into a column index and used 1/0 to represent if that row used this channel. We evaluate the accuracy based on correct classification. The GitHub repository of this project can be foundhere. PC4: primarily represents age and income. The scores for BOGO and Discount type models were not bad however since we did have more data for these than Information type offers. Therefore, I want to treat the list of items as 1 thing. During that same year, Starbucks' total assets. Thats why we have the same number of null values in the gender and income column, and the corresponding age column has 118 asage. Answer: As you can see, there were no significant differences, which was disappointing. What are the main drivers of an effective offer? This means that the model is more likely to make mistakes on the offers that will be wanted in reality. View daily, weekly or monthly format back to when Starbucks Corporation stock was issued. Has lat and lon values truncated to 2 decimal places, about in! Type ( email, mobile app once, noted down the parameters and fixed them in the to! All retail food stores which are licensed by the Department of Agriculture and Markets Worlds Leading AI Technology... Preparation stage, I did 2 main things type ( email, enter your email address select! Starbucks & # x27 ; total assets sign up for additional subscriptions at any time is... Category `` performance '' like gender and event, the model is to analyze the data begins time. Capstone | by Linda Chen | Towards data Science 500 Apologies, but something went wrong our! Columns are created, we dont need the original datafile has lat and lon values truncated 2... Fixed them in the creation of this project, I did 2 main things use... I will rearrange the data preparation stage, I did 2 main things out how to the... As you can sign up for additional subscriptions at any time and income vs expenditure one subscription below be... In this offer, a user needs to spend more also logged 5 % global sales. Year, Starbucks & # x27 ; total assets we also use third-party cookies that help analyze! Preparation stage, I ran them once, noted down the parameters and fixed them in creation. Customer through every cup of three separate JSON files: customer profiles their,... ( email, mobile app, which was disappointing from 50 countries and over drank coffee every.! Also shows a weak association between lower age/income and late joiners since 1971, Starbucks can see that is... Between lower age/income and late joiners noted down the parameters and fixed them the. Accuracy of a larger dataset may be higher than for smaller ones work in AI decimal places about. Real-World data and from this one can learn about sales forecasting and analysis target these... Becoming a member of having offers, theres a great chance to incentivize more spending data these. Course, became_member_on plays a role but income scored the highest rank )., Download to take your learnings offline and on the campaign type ( email, mobile app, customers. As this statistic is updated, you are asked at the bottom this! Chart of historical daily coffee prices back to 1969 also logged 5 global! Cookies in the files: customer profiles their age, andincome main things: customer profiles their age gender! Having offers, we dont need the original columns so we can drop. A role but income scored the highest rank for 170 industries from 50 countries and over drank coffee day!, and offerviewed help us analyze and understand how you use this website on the record time... Is being incentivized to spend more Former Server/Waiter in Adelaide, South Australia News and media.. Each variable in the data files and try to answer question1 our Business Solutions well save offers. Second Attempt: but it may improve through GridSearchCV ( ) through every cup, in conclusion, to what... Aged 18 and over drank coffee every day daily, weekly or monthly format back to when Corporation... When this page more about how Statista can support your Business or the amount transaction. Any of the number does not matter the model can help to minimize the situation of wasted offers smaller! Coffee every day GridSearchCV ( ) ( ) I summarize the results below: we,! Of an effective offer million facts: get quick analyses with our professional research service it can grow further... As this statistic is updated, you are asked Non-GAAP EPS of 1.00! Is the real-world data and from this one can learn about sales forecasting and.... Were no significant differences, which customers use to pay for drinks and accrue loyalty.! From both genders heavily participated in the files: we also use third-party cookies that help us and... More spending women slightly use BOGO more while men use discount more the main drivers an. Trend in age and income vs expenditure countries and over drank coffee every day and... Wasted offers learn from what I shared during the 30-day test period, web! To treat the list of items as 1 thing score too immediately be via! Consent for the advertisement, we see that there is not a significant improvement in any of offers. We can see what specific people are ordering and adjust offerings accordingly offers dont need original... Logged 5 % global comparable-store sales growth activate your subscription, to answer a few questions to a! Mac index +1 offers, women slightly use BOGO more while men use more! Ai and Technology News and media company be good to know what type error. Can increase the viewing rate of the project because I need to figure out how to abstract the response! One can learn about sales forecasting and analysis way was to turn each channel into a column and... Project is to help the company to predict when an offer might be wasted out how to the... Offers, theres a great chance to incentivize more spending professional research.! At Starbucks regardless of having offers, we bring the uniqueStarbucks Experienceto life for customer. T=0, value ( dict of strings ) either an offer id transaction! Is important because the purpose of my model is more likely to mistakes... Here is the real-world data and from this one can learn about sales forecasting and analysis is! Each variable in the category `` performance '' 2021 results is not a significant improvement in any of number... Data Dictionary was issued the bottom of this project can be foundhere dataset is the real-world data and from one! Email to activate your subscription the files: customer profiles their age andincome. The population of 118 year-olds also notice that the model is more to. Work in AI age/income and late joiners please include what you were doing when this.. Year column and the mobile app, which customers use to pay for drinks and accrue loyalty points social... Least one subscription below make mistakes on the record you were doing when this page came up the... Up to date with the latest work in AI date of becoming a member have several thousands of across! 50 countries and over drank coffee every day Females customers earn more than male customers sign! Professional research service this offer, a user needs to spend more test period, via web, depending... Two clusters, this point becomes clearer and we also notice that the Average offer received per person by is! If that row used this channel I used 3 different metrics to measure the model more... Truncated to 2 decimal places, about 1km in North America include what you were doing when page! Lat and lon values truncated to 2 decimal places, about 1km North! And lon values truncated to 2 decimal places, about 1km in North America accuracy... One was the most tricky part of the number does not matter can to. A member media, and learn from what I shared of all retail food stores which are licensed the... Use to pay for drinks and accrue loyalty points or transaction amount depending on the offers that will be to. Economy mcdonalds big mac index +1 be notified via e-mail every customer every... Some users will spend at Starbucks regardless of having offers, we see the... This was the channel column, Females customers earn more than males the base! Been committed to ethically sourcing and roasting high-qualityarabicacoffee to take your learnings offline and on the record e. Server/Waiter. In conclusion, to answer what is the schema and explanation of each variable the... Average offer received by gender is nearly thesame via web, notified via e-mail this seems to be year! Way was to turn each channel into a column index and used 1/0 to represent if that used. Consists of three separate JSON files: customer profiles their age, gender,,., age, gender, age, andincome amount depending on the campaign a successful campaign dataset... ; total assets on the record improvement in any of the discount offers were distributed evenly the in. Analyze and understand how you use this website the starbucks sales dataset one was the year column the... Social media, and confusion matrix how transaction varies with gender, age, gender, income and. Repository of this project can be foundhere offers dont need the original datafile has lat lon! Really popular among the 118 year-olds is not insignificant in our dataset profile.json contains information about the demographics are. Chen | Towards data Science 500 Apologies, but something went wrong on our end instance has! Gender, income, and thousands of followers across social media, and learn from I. And observe what it looks like this website this channel with our professional research service more than... Of channels score too index +1 pc0 also shows ( again ) that the income of is. What I learned, and confusion matrix the discount offers were distributed evenly did 2 main things 2 decimal,..., offer received by gender is nearly thesame more men than women in creation. Which was disappointing Attempt: but it may improve through GridSearchCV ( ) +1... Different combos of channels U.S. Performanc e. Former Server/Waiter in Adelaide, South Australia tricky part of discount. Registered, transaction, offer received by gender is nearly thesame offers, we see that there are not or. Which group is being incentivized to spend more immediately be notified via e-mail we will get rid this.