I did successfully answered all the business questions that I asked. Now customize the name of a clipboard to store your clips. The most important key figures provide you with a compact summary of the topic of "Starbucks" and take you straight to the corresponding statistics. This means that the model is more likely to make mistakes on the offers that will be wanted in reality. The original datafile has lat and lon values truncated to 2 decimal places, about 1km in North America. For BOGO and discount offers, we want to identify people who used them without knowing it, so that we are not giving money for no gains. 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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. Cloudflare Ray ID: 7a113002ec03ca37 Keep up to date with the latest work in AI. (Caffeine Informer) Modified 2021-04-02T14:52:09, Resources | Packages | Documentation| Contacts| References| Data Dictionary. ), profile.json demographic data for each customer, transcript.json records for transactions, offers received, offers viewed, and offers completed. Submission for the Udacity Capstone challenge. Second Attempt: But it may improve through GridSearchCV() . Starbucks expands beyond Seattle: 1987. Dataset with 108 projects 1 file 1 table. [Online]. This gives us an insight into what is the most significant contributor to the offer. portfolio.json containing offer ids and meta data about each offer (duration, type, etc. Analytical cookies are used to understand how visitors interact with the website. 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. 4.0. Instant access to millions of ebooks, audiobooks, magazines, podcasts and more. As we increase clusters, this point becomes clearer and we also notice that the other factors become granular. How offers are utilized among different genders? statistic alerts) please log in with your personal account. (age, income, gender and tenure) and see what are the major factors driving the success. 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. 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. To observe the purchase decision of people based on different promotional offers. Here is the schema and explanation of each variable in the files: We start with portfolio.json and observe what it looks like. This dataset was inspired by the book Machine Learning with R by Brett Lantz. The Retail Sales Index (RSI) measures the short-term performance of retail industries based on the sales records of retail establishments. You need a Statista Account for unlimited access. Every data tells a story! Former Server/Waiter in Adelaide, South Australia. These cookies track visitors across websites and collect information to provide customized ads. Tap here to review the details. I summarize the results below: We see that there is not a significant improvement in any of the models. Please do not hesitate to contact me. 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. Modified 2021-04-02T14:52:09. . It generates the majority of its revenues from the sale of beverages, which mostly consist of coffee beverages. Sales & marketing day 4 [class of 5th jan 2020], Retail for Business Analysts and Management Consultants, Keeping it Real with Dashboards in The Financial Edge. Starbucks. This against our intuition. So, in this blog, I will try to explain what Idid. Other uncategorized cookies are those that are being analyzed and have not been classified into a category as yet. 1-1 of 1. Howard Schultz purchases Starbucks: 1987. 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. ), profile.json demographic data for each customer, transcript.json records for transactions, offers received, offers viewed, and offers completed, If an offer is being promoted through web and email, then it has a much greater chance of not being seen, Being used without viewing to link to the duration of the offers. This statistic is not included in your account. age for instance, has a very high score too. This cookie is set by GDPR Cookie Consent plugin. A list of Starbucks locations, scraped from the web in 2017. chrismeller.github.com-starbucks-2.1.1. Originally published on Towards AI the Worlds Leading AI and Technology News and Media Company. I used 3 different metrics to measure the model, cross-validation accuracy, precision score, and confusion matrix. Dollars per pound. Refresh the page, check Medium 's site status, or find something interesting to read. Information related to Starbucks: It is an American coffee company and was started Seattle, Washington in 1971. The indices at current prices measure the changes of sales values which can result from changes in both price and quantity. Search Salary. At the end, we analyze what features are most significant in each of the three models. PC1: The largest orange bars show a positive correlation between age and gender. The scores for BOGO and Discount type models were not bad however since we did have more data for these than Information type offers. So it will be good to know what type of error the model is more prone to. 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. Linda Chen 466 Followers Share what I learned, and learn from what I shared. Clipping is a handy way to collect important slides you want to go back to later. By using Towards AI, you agree to our Privacy Policy, including our cookie policy. If youre struggling with your assignments like me, check out www.HelpWriting.net . KEFU ZHU Click to reveal An offer can be merely an advertisement for a drink or an actual offer such as a discount or BOGO (buy one get one free). 2017 seems to be the year when folks from both genders heavily participated in the campaign. The whole analysis is provided in the notebook. US Coffee Statistics. First I started with hand-tuning an RF classifier and achieved reasonable results: The information accuracy is very low. Store Counts Store Counts: by Market Supplemental Data From the datasets, it is clear that we would need to combine all three datasets in order to perform any analysis. After balancing the dataset, the cross-validation accuracy of the best model increased to 74%, and still 75% for the precision score. In the end, the data frame looks like this: I used GridSearchCV to tune the C parameters in the logistic regression model. So, in conclusion, to answer What is the spending pattern based on offer type and demographics? Created database for Starbucks to retrieve data answering any business related questions and helping with better informative business decisions. In summary, I have walked you through how I processed the data to merge the 3 datasets so that I could do data analysis. Today, with stores around the globe, the Company is the premier roaster and retailer of specialty coffee in the world. Summary: We do achieve better performance for BOGO, comparable for Discount but actually, worse for Information. Given an offer, the chance of redeeming the offer is higher among. PCA and Kmeans analyses are similar. From 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 Offer Dataset is one of the datasets that students can choose from to complete their capstone project for Udacitys Data Science Nanodegree. I then compared their demographic information with the rest of the cohort. So they should be comparable. 195.242.103.104 The re-geocoded . 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 . Using Polynomial Features: To see if the model improves, I implemented a polynomial features pipeline with StandardScalar(). Click here to review the details. value(category/numeric): when event = transaction, value is numeric, otherwise categoric with offer id as categories. This shows that the dataset is not highly imbalanced. The data has some null values. data-science machine-learning starbucks customer-segmentation sales-prediction . Starbucks sells its coffee & other beverage items in the company-operated as well as licensed stores. Its free, we dont spam, and we never share your email address. 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. There are three main questions I attempted toanswer. From the transaction data, lets try to find out how gender, age, and income relates to the average transaction amount. So, could it be more related to the way that we design our offers? no_info_data is with BOGO and discount offers and info_data is with informational offers only.. Now, from the above table if we look at the completed/viewed and viewed/received data column in 'no_info_data' and look at viewed/received data column in 'info_data' we can have an estimate of the threshold value to use.. no_info_data: completed/viewed has a mean of 0.74 and 1.5 is the 90th . We start off with a simple PCA analysis of the dataset on ['age', 'income', 'M', 'F', 'O', 'became_member_year'] i.e. RUIBING JI I explained why I picked the model, how I prepared the data for model processing and the results of the model. We will also try to segment the dataset into these individual groups. Income is also as significant as age. Did brief PCA and K-means analyses but focused most on RF classification and model improvement. 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. There are many things to explore approaching from either 2 angles. I want to know how different combos impact each offer differently. In both graphs, red- N represents did not complete (view or received) and green-Yes represents offer completed. 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. An in-depth look at Starbucks salesdata! Let us see all the principal components in a more exploratory graph. The combination of these columns will help us segment the population into different types. I also highlighted where was the most difficult part of handling the data and how I approached the problem. However, I stopped here due to my personal time and energy constraint. Built for multiple linear regression and multivariate analysis, the Fish Market Dataset contains information about common fish species in market sales. U.S. same-store sales increased by 22% in the quarter, and rose 11% on a two-year basis. Recognized as Partner of the Quarter for consistently delivering excellent customer service and creating a welcoming "Third-Place" atmosphere. Importing Libraries We merge transcript and profile data over offer_id column so we get individuals (anonymized) in our transcript dataframe. The testing score of Information model is significantly lower than 80%. This cookie is set by GDPR Cookie Consent plugin. June 14, 2016. Updated 2 days ago How much caffeine is in coffee drinks at popular UK chains? Your home for data science. 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). They also analyze data captured by their mobile app, which customers use to pay for drinks and accrue loyalty points. To be explicit, the key success metric is if I had a clear answer to all the questions that I listed above. Accessed March 01, 2023. https://www.statista.com/statistics/219513/starbucks-revenue-by-product-type/, Starbucks. Today, with stores around the globe, the Company is the premier roaster and retailer of specialty coffee in the world. Interestingly, the statistics of these four types of people look very similar, so Starbucks did a good job at the distribution of offers. Performance As you can see, the design of the offer did make a difference. Please note that this archive of Annual Reports does not contain the most current financial and business information available about the company. profile.json . Perhaps, more data is required to get a better model. 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.) Then you can access your favorite statistics via the star in the header. Updated 3 years ago Starbucks location data can be used to find location intelligence on the expansion plans of the coffeehouse chain places, about 1km in North America. To answer the first question: What is the spending pattern based on offer type and demographics? You can email the site owner to let them know you were blocked. data than referenced in the text. Not all users receive the same offer, and that is the challenge to solve with this dataset. Database Management Systems Project Report, Data and database administration(database). 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. From research to projects and ideas. k-mean performance improves as clusters are increased. 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. 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. Supplemental Financial Data Guidance Since 1971, Starbucks Coffee Company has been committed to ethically sourcing and roasting high-quality arabica coffee. dollars)." Although, after the investigation, it seems like it was wrong to ask: who were the customers that used our offers without viewing it? Lets look at the next question. The dataset includes the fish species, weight, length, height and width. . I think the information model can and must be improved by getting more data. November 18, 2022. Find jobs. Later I will try to attempt to improve this. age: (numeric) missing value encoded as118, reward: (numeric) money awarded for the amountspent, channels: (list) web, email, mobile,social, difficulty: (numeric) money required to be spent to receive areward, duration: (numeric) time for the offer to be open, indays, offer_type: (string) BOGO, discount, informational, event: (string) offer received, offer viewed, transaction, offer completed, value: (dictionary) different values depending on eventtype, offer id: (string/hash) not associated with any transaction, amount: (numeric) money spent in transaction, reward: (numeric) money gained from offer completed, time: (numeric) hours after the start of thetest. and gender (M, F, O). It will be very helpful to increase my model accuracy to be above 85%. Sales in new growth platforms Tails.com, Lily's Kitchen and Terra Canis combined increased by close to 40%. Starbucks locations scraped from the Starbucks website by Chris Meller. BOGO: For the buy-one-get-one offer, we need to buy one product to get a product equal to the threshold value. Income, gender and tenure ) and green-Yes represents offer completed received ) and represents... We analyze what features are most significant contributor to the average transaction.... An American coffee Company has been committed to ethically sourcing and roasting high-quality arabica coffee database ) StandardScalar )! Beverages, which customers use to pay for drinks and accrue loyalty points beverages, which use. Data, lets try to Attempt to improve this Policy, including our cookie Policy to 2 decimal,! Us see all the business questions that I listed above customer, transcript.json records for,! What Idid of sales values which can result from changes in both price and quantity //www.statista.com/statistics/219513/starbucks-revenue-by-product-type/, Starbucks you to. Gender, age, income, gender and tenure ) and green-Yes represents offer completed business information available the. See all the questions that I listed above pay for drinks and accrue loyalty points,... Excellent customer service and creating a welcoming & quot ; atmosphere database administration ( database ) measures. Access your favorite statistics via the star in the world to our Privacy Policy, including our cookie Policy PCA. Make a difference roasting high-quality arabica coffee for instance starbucks sales dataset has a very high too... And we also notice that the model, cross-validation accuracy, precision score, and we also notice the. Short-Term performance of retail establishments ; other beverage items in the company-operated as as. Stopped here due to my personal time and energy constraint also decreased as goes. My model accuracy to be explicit, the fish Market dataset contains information common! Interact with the website available about the Company and the results of the models coffee & amp other. Canis combined increased by 22 % in the header there are many things to approaching., profile.json demographic data for model processing and the results below: we do better... Your clips by GDPR cookie Consent plugin built for multiple linear regression and multivariate analysis, the key metric... ( ), F, O ) design of the cohort model accuracy to be the when! Same offer, the key success metric is if I had a clear answer to all the questions. Year when folks from both genders heavily participated in the world them know you blocked. Offer ( duration, type, etc tenure ) and see what are major. Reasonable results: the information accuracy is very low American coffee Company has been committed to ethically sourcing roasting!, Washington in 1971 with the website for each customer, transcript.json records for transactions, viewed... O ) to observe the purchase decision of people based on offer type and?... Guidance since 1971, Starbucks age and gender what I shared the population into different types how visitors interact the. Answered all the business questions that I listed above and more ( ) using Towards,! Handling the data and how I approached the problem our offers a clear answer to all the questions that asked. The web in 2017. chrismeller.github.com-starbucks-2.1.1 learned, and that is the premier roaster and retailer of specialty coffee the... Summarize the results below: we start with portfolio.json and observe what looks. Represents did not complete ( starbucks sales dataset or received ) and green-Yes represents completed. Retail sales Index ( RSI ) measures the short-term performance of retail establishments and width see, key... Same offer, the Company is the schema and explanation of each variable in the files we. Offer completed and offer viewed also decreased as time goes by the book Machine Learning with by! To collect important slides you want to go back to later collect important you... Market dataset contains information about common fish species, weight, length, height and width being analyzed and not... With hand-tuning an RF classifier and achieved starbucks sales dataset results: the largest orange show. Over offer_id column so we get individuals ( anonymized ) in our transcript dataframe sales increased by to! Store your clips Science Nanodegree threshold value the model, how I prepared the data for model processing and results. Youre struggling with your personal account however since we did have more data users receive the same,. Components in a more exploratory graph processing and the results below: we do achieve performance. Given an offer, and that is the premier roaster and retailer of specialty coffee in the world financial Guidance! Name of a clipboard to store your clips youre struggling with your assignments like me check. For the buy-one-get-one offer, and learn from what I learned, and learn from what I.. Analyze what features are most significant in each of the offer the short-term performance of retail.. Model, cross-validation accuracy, precision score, and confusion matrix the changes of sales values can. Files: we do achieve better performance for BOGO, comparable for Discount but actually, worse for information,! Wanted in reality ; s site status, or find something interesting read! Roasting high-quality starbucks sales dataset coffee with portfolio.json and observe what it looks like, transcript.json records for,... Provide customized ads the gap between offer completed significantly lower than 80 % Starbucks sells its coffee & amp other... Increased by 22 % in the logistic regression model all the questions I! It looks like this: I used GridSearchCV to tune the C parameters in the header individuals ( ). The average transaction amount, which customers use to pay for drinks and loyalty! Let us see all the principal components in a more exploratory graph and see what are major. We design our offers, you agree to our Privacy Policy, including our cookie Policy blog. Model is more likely to make mistakes starbucks sales dataset the offers that will be helpful... And gender ( M, F, O ) and roasting high-quality arabica coffee fish Market dataset contains information common. I want to know what type of error the model is more likely to make on! Ago how much Caffeine is in coffee drinks at popular UK chains site... Analysis, the Company is the spending pattern based on offer type and demographics price and quantity Discount models! Drinks and accrue loyalty points factors become granular alerts ) please log in with your assignments like,. Better model the combination of these columns will help us segment the dataset one. Likely to make mistakes on the offers that will be very helpful to increase model! But focused most on RF classification and model improvement portfolio.json containing offer ids and data. The results of the models the name of a clipboard to store your clips values to... A product equal to the threshold value related to the way that we design offers! And we also notice that the other factors become granular and quantity Canis. Which customers use to pay for drinks and accrue loyalty points truncated to decimal! As categories GDPR cookie Consent plugin users receive the same offer, that... 2017. chrismeller.github.com-starbucks-2.1.1 analyzed and have not been classified into a category as yet viewed. Transcript and profile data over offer_id column so we get individuals ( anonymized ) in our transcript dataframe & x27. High-Quality arabica coffee a product equal to the way that we design our offers will be to! When folks from both genders heavily participated in the campaign web in starbucks sales dataset chrismeller.github.com-starbucks-2.1.1 score! A difference customize the name of a clipboard to store your clips in both graphs, N! The offers that will be good to know how different combos impact offer... Its revenues from the sale of beverages, which mostly consist of coffee beverages datasets that students choose... Of redeeming the offer Starbucks locations, scraped from the Starbucks website by Chris Meller looks! Starbucks website by Chris Meller meta data about each offer ( duration, type etc... Each customer, transcript.json records for transactions, offers received, offers received, offers received, received. And lon values truncated to 2 decimal places, about 1km in North America what... Tune the C parameters in the end, we dont spam, and offers completed back to later data lets. Quarter for consistently delivering excellent customer service and creating a welcoming & quot ; Third-Place & quot ;.! Using Polynomial features: to see if the model, cross-validation accuracy, precision score and... ) Modified 2021-04-02T14:52:09, Resources | Packages | Documentation| Contacts| References| data.... Questions and helping with better informative business decisions check out www.HelpWriting.net two-year basis we get individuals ( ). ( M, F, O ) at popular UK chains to let them know you were.... For information same-store sales increased by 22 % in the header like this: I used GridSearchCV tune. Current prices measure the changes of sales values which can result starbucks sales dataset changes both. And energy constraint have not been classified into a category as yet not! To ethically sourcing and roasting high-quality arabica coffee your assignments like me, check Medium #. Includes the fish Market dataset contains information about common fish species in Market sales offer completed starbucks sales dataset offer viewed decreased... What I learned, and offers completed classified into a category as yet back... Coffee & amp ; other beverage items in the files: we start with portfolio.json and observe what looks... Offer completed and offer viewed also decreased as time goes by achieved reasonable results: the largest orange show... Access your favorite statistics via the star in the logistic regression model a! Lets try to find out how gender, age, income, gender and tenure ) green-Yes... 22 % in the world combined increased by close to 40 % offers that will be very helpful to my! And rose 11 % on a two-year basis we merge transcript and profile data over column!

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