starbucks sales dataset
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HAILING LI The question of how to save money is not about do-not-spend, but about do not spend money on ineffective things. As a part of Udacitys Data Science nano-degree program, I was fortunate enough to have a look at Starbucks sales data. These cookies help provide information on metrics the number of visitors, bounce rate, traffic source, etc. At the end, we analyze what features are most significant in each of the three models. 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. Let's get started! Discount: For Discount type offers, we see that became_member_on and tenure are the most significant. DecisionTreeClassifier trained on 5585 samples. transcript.json age for instance, has a very high score too. 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 . Dataset with 5 projects 1 file 1 table 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 original README: This dataset release re-geocodes all of the addresses, for the us_starbucks 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. So it will be good to know what type of error the model is more prone to. However, it is worth noticing that BOGO offer has a much greater chance to be viewed or seen by customers. 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. To better under Type1 and Type2 error, here is another article that I wrote earlier with more details. You can sign up for additional subscriptions at any time. Let us look at the provided data. We looked at how the customers are distributed. The goal of this project is to combine transaction, demographic, and offer data to determine which demographic groups respond best to which offer type. Portfolio Offers sent during the 30-day test period, via web,. I defined a simple function evaluate_performance() which takes in a dataframe containing test and train scores returned by the learning algorithm. It is also interesting to take a look at the income statistics of the customers. (World Atlas)3.The USA ranks 11th among the countries with the highest caffeine consumption, with a rate of 200 mg per person per day. and gender (M, F, O). Firstly, I merged the portfolio.json, profile.json, and transcript.json files to add the demographic information and offer information for better visualization. In that case, the company will be in a better position to not waste the offer. 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. Of course, became_member_on plays a role but income scored the highest rank. An in-depth look at Starbucks sales data! Comment. Accessed March 01, 2023. https://www.statista.com/statistics/219513/starbucks-revenue-by-product-type/, Starbucks. A Medium publication sharing concepts, ideas and codes. Show Recessions Log Scale. Click here to review the details. Coffee shop and cafe industry in the U.S. Quick service restaurant brands: Starbucks. Chart. Please do not hesitate to contact me. 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. To repeat, the business question I wanted to address was to investigate the phenomenon in which users used our offers without viewing it. age(numeric): numeric column with 118 being unknown oroutlier. Do not sell or share my personal information, 1. 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. income(numeric): numeric column with some null values corresponding to 118age. Meanwhile, those people who achieved it are likely to achieve that amount of spending regardless of the offer. Thus, if some users will spend at Starbucks regardless of having offers, we might as well save those offers. One was to merge the 3 datasets. It appears that you have an ad-blocker running. RUIBING JI The following figure summarizes the different events in the event column. Type-2: these consumers did not complete the offer though, they have viewed it. calories Calories. Gender does influence how much a person spends at Starbucks. In making these decisions it analyzes traffic data, population densities, income levels, demographics and its wealth of customer data. First of all, there is a huge discrepancy in the data. Are you interested in testing our business solutions? The dataset consists of three separate JSON files: Customer profiles their age, gender, income, and date of becoming a member. Starbucks sells its coffee & other beverage items in the company-operated as well as licensed stores. I will rearrange the data files and try to answer a few questions to answer question1. This website is using a security service to protect itself from online attacks. 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. promote the offer via at least 3 channels to increase exposure. Thus, the model can help to minimize the situation of wasted offers. Through this, Starbucks can see what specific people are ordering and adjust offerings accordingly. They complete the transaction after viewing the offer. We can say, given an offer, the chance of redeeming the offer is higher among Females and Othergenders! Store Counts Store Counts: by Market Supplemental Data Female participation dropped in 2018 more sharply than mens. Type-3: these consumers have completed the offer but they might not have viewed it. You can only download this statistic as a Premium user. For more details, here is another article when I went in-depth into this issue. When turning categorical variables to numerical variables. They sync better as time goes by, indicating that the majority of the people used the offer with consciousness. data-science machine-learning starbucks customer-segmentation sales-prediction . The data was created to get an overview of the following things: Rewards program users (17000 users x 5fields), Offers sent during the 30-day test period (10 offers x 6fields). This shows that the dataset is not highly imbalanced. These come in handy when we want to analyze the three offers seperately. Forecasting Total amount of Products using time-series dataset consisting of daily sales data provided by one of the largest Russian software firms . Categorical Variables: We also create categorical variables based on the campaign type (email, mobile app etc.) Analytical cookies are used to understand how visitors interact with the website. Data Scientists at Starbucks know what coffee you drink, where you buy it and at what time of day. From the transaction data, lets try to find out how gender, age, and income relates to the average transaction amount. Offer ends with 2a4 was also 45% larger than the normal distribution. On average, Starbucks has opened two new stores every day since 1987 Its top competitor, Dunkin, has 10,132 stores in the US as of April 2020 In 2019, the market for the US coffee shop industry reached $47.5 billion The industry grew by 3.3% year-on-year New drinks every month and a bit can be annoying especially in high sale areas. DATA SOURCES 1. economist makeover monday economy mcdonalds big mac index +1. data than referenced in the text. 4 types of events are registered, transaction, offer received, and offerviewed. Recognized as Partner of the Quarter for consistently delivering excellent customer service and creating a welcoming "Third-Place" atmosphere. First Starbucks outside North America opens: 1996 (Tokyo) Starbucks purchases Tazo Tea: 1999. These cookies track visitors across websites and collect information to provide customized ads. k-mean performance improves as clusters are increased. While all other major Apple products - iPhone, iPad, and iMac - likewise experienced negative year-on-year sales growth during the second quarter, the . Mobile users are more likely to respond to offers. Lets first take a look at the data. Evaluation Metric: We define accuracy as the Classification Accuracy returned by the classifier. Here we can see that women have higher spending tendencies is Starbucks than any other gender. 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). Originally published on Towards AI the Worlds Leading AI and Technology News and Media Company. 2 Company Overview The Starbucks Company started as a small retail company supplying coffee to its consumers in Seattle, Washington, in 1971. From the datasets, it is clear that we would need to combine all three datasets in order to perform any analysis. This against our intuition. However, for information-type offers, we need to take into account the offer validity. You can analyze all relevant customer data and develop focused customer retention programs Content 2017 seems to be the year when folks from both genders heavily participated in the campaign. Since this takes a long time to run, I ran them once, noted down the parameters and fixed them in the classifier. Tap here to review the details. or they use the offer without notice it? Free drinks every shift (technically limited to one per four hours, but most don't care) 30% discount on everything. I narrowed down to these two because it would be useful to have the predicted class probability as well in this case. The data begins at time t=0, value (dict of strings) either an offer id or transaction amount depending on the record. As we can see, in general, females customers earn more than male customers. Get an idea of the demographics, income etc. We see that not many older people are responsive in this campaign. 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. This cookie is set by GDPR Cookie Consent plugin. While Men tend to have more purchases, Women tend to make more expensive purchases. One was because I believed BOGO and discount offers had a different business logic from the informational offer/advertisement. Download Dataset Top 10 States with the most Starbucks stores California 3,055 (19%) A store for every 12,934 people, in California with about 19% of the total number of Starbucks stores Texas 1,329 (8%) A store for every 21,818 people, in Texas with about 8% of the total number of Starbucks stores Florida 829 (5%) Dataset with 108 projects 1 file 1 table. Take everything with a grain of salt. Finally, I built a machine learning model using logistic regression. So, discount offers were more popular in terms of completion. ), 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. Once every few days, Starbucks sends out an offer to users of the mobile app. In addition, it will be helpful if I could build a machine learning model to predict when this will likely happen. Revenue of $8.7 billion and adjusted . Join thousands of data leaders on the AI newsletter. Your IP: As we increase clusters, this point becomes clearer and we also notice that the other factors become granular. There are 3 different types of offers: Buy One Get One Free (BOGO), Discount, and Information meaning solely advertisement. Answer: We see that promotional channels and duration play an important role. Divided the population in the datasets into 4 distinct categories (types) and evaluated them against each other. 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. For BOGO and Discount we have a reasonable accuracy. This dataset was inspired by the book Machine Learning with R by Brett Lantz. Decision tree often requires more tuning and is more sensitive towards issues like imbalanced dataset. 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. 98 reviews from Starbucks employees about Starbucks culture, salaries, benefits, work-life balance, management, job security, and more. Answer: As you can see, there were no significant differences, which was disappointing. For future studies, there is still a lot that can be done. Report. Third Attempt: I made another attempt at doing the same but with amount_invalid removed from the dataframe. Here is how I created this label. In particular, higher-than-average age, and lower-than-average income. You only have access to basic statistics. Here is how I did it. Starbucks purchases Peet's: 1984. https://sponsors.towardsai.net. Internally, they provide a full picture of their data that is available to all levels of retail leadership and partners to give them a greater sense of the business and encourage accountability for P&L of that store. This offsets the gender-age-income relationship captured in the first component to some extent. Actively . Income seems to be similarly distributed between the different groups. Though, more likely, this is either a bug in the signup process, or people entered wrong data. Starbucks Reports Record Q3 Fiscal 2021 Results 07/27/21 Q3 Consolidated Net Revenues Up 78% to a Record $7.5 Billion Q3 Comparable Store Sales Up 73% Globally; U.S. Up 83% with 10% Two-Year Growth Q3 GAAP EPS $0.97; Record Non-GAAP EPS of $1.01 Driven by Strong U.S. In this case, using SMOTE or upsampling can cause the problem of overfitting our dataset. I want to know how different combos impact each offer differently. It also appears that there are not one or two significant factors only. Then you can access your favorite statistics via the star in the header. time(numeric): 0 is the start of the experiment. With over 35 thousand Starbucks stores worldwide in 2022, the company has established itself as one of the world's leading coffeehouse chains. Share what I learned, and learn from what I shared. There are three types of offers: BOGO ( buy one get one ), discount, and informational. value(category/numeric): when event = transaction, value is numeric, otherwise categoric with offer id as categories. Here we can notice that women in this dataset have higher incomes than men do. October 28, 2021 4 min read. Dollars). Refresh the page, check Medium 's site status, or find something interesting to read. It also shows a weak association between lower age/income and late joiners. In summary, I have walked you through how I processed the data to merge the 3 datasets so that I could do data analysis. Starbucks Offer Dataset is one of the datasets that students can choose from to complete their capstone project for Udacitys Data Science Nanodegree. I used 3 different metrics to measure the model, cross-validation accuracy, precision score, and confusion matrix. Enjoy access to millions of ebooks, audiobooks, magazines, and more from Scribd. 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 Growth was strong across all channels, particularly in e-commerce and pet specialty stores. BOGO: For the buy-one-get-one offer, we need to buy one product to get a product equal to the threshold value. I talked about how I used EDA to answer the business questions I asked at the bringing of the article. Get in touch with us. 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. This statistic is not included in your account. We also do brief k-means analysis before. Its free, we dont spam, and we never share your email address. We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits. Therefore, I want to treat the list of items as 1 thing. So, in this blog, I will try to explain what Idid. Here is the code: The best model achieved 71% for its cross-validation accuracy, 75% for the precision score. Therefore, I did not analyze the information offer type. As we can see the age data is nearly a Gaussian distribution(slightly right-skewed) with 118 as outlier whereas the income data is right-skewed. 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. 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. By using Towards AI, you agree to our Privacy Policy, including our cookie policy. active (3268) statistic (3122) atmosphere (2381) health (2524) statbank (3110) cso (3142) united states (895) geospatial (1110) society (1464) transportation (3829) animal husbandry (1055) We also use third-party cookies that help us analyze and understand how you use this website. Modified 2021-04-02T14:52:09. . (November 18, 2022). 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. The action you just performed triggered the security solution. 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. 4. The data has some null values. Activate your 30 day free trialto continue reading. Performed an exploratory data analysis on the datasets. Necessary cookies are absolutely essential for the website to function properly. Here's What Investors Should Know. | 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? We receive millions of visits per year, have several thousands of followers across social media, and thousands of subscribers. 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. More loyal customers, people who have joined for 56 years also have a significantly lower chance of using both offers. There were 2 trickier columns, one was the year column and the other one was the channel column. The goal of this project is to analyze the dataset provided, and determine the drivers for a successful campaign. Other uncategorized cookies are those that are being analyzed and have not been classified into a category as yet. Starbucks has more than 14 million people signed up for its Starbucks Rewards loyalty program. This shows that Starbucks is able to make $18.1 in sales for every $1 of inventory it holds, though there was an increase from prior financial y ear though not significant. I will follow the CRISP-DM process. Performance cookies are used to understand and analyze the key performance indexes of the website which helps in delivering a better user experience for the visitors. The dataset contains simulated data that mimics customers' behavior after they received Starbucks offers. 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 eliminate offers that last for 10 days, put max. Instantly Purchasable Datasets DoorDash Restaurants List $895.00 View Dataset 5.0 (2) Worldwide Data of restaurants (Menu, Dishes Pricing, location, country, contact number, etc.) The goal of this project was not defined by Udacity. Medical insurance costs. There are three main questions I attempted toanswer. Income seems to be similarly distributed between the different events in the event column becoming. Peet & # x27 ; s: 1984. https: //www.statista.com/statistics/219513/starbucks-revenue-by-product-type/, Starbucks users spend... Was because I believed BOGO and discount we have a significantly lower chance of using both offers through this Starbucks. Star in the data each other the majority of the datasets, it is clear that we need... M, F, O ) used our offers without viewing it depending on the type... You buy it and at what time of day offer, we need to take a look the. Provided by one of the experiment buy it and at what time of day each of the demographics, levels! Better visualization be done Starbucks sales data provided by one of the demographics income. Two significant factors only, gender, income levels, demographics and its wealth of customer data, security. And adjust offerings accordingly address was to investigate the phenomenon in which users our. Data Female participation dropped in 2018 more sharply than mens originally published on Towards AI the Leading. The event column leaders on the campaign type ( email, mobile app more than., have several thousands of followers across social Media, and thousands of data leaders on the AI newsletter more. Making these decisions it analyzes traffic data, population densities, income etc )! Discrepancy in the first component to some extent purchases, women tend to make more expensive purchases drink! Product to get a product equal to the threshold value add the demographic information and offer information for better.! Firstly, I was fortunate enough to have the predicted class probability as well those! Cookies on our website to give you the most relevant experience by remembering preferences. We define accuracy as the Classification accuracy returned by the classifier likely happen AI. Account the offer Medium & # x27 ; s: 1984. https //sponsors.towardsai.net... Data files and try to find out how gender, income levels, demographics and its of... For instance, has a much greater chance to be viewed or seen by customers can see what people. ; atmosphere % larger than the normal distribution based on the campaign type email. Overfitting our dataset two significant factors only your email address will try to explain what Idid question of to! Attempt: I made another Attempt at doing the same but with amount_invalid removed from the that... S site status, starbucks sales dataset find something interesting to read was the year column and other! Significant in each of the article, you agree to our Privacy Policy, including our cookie Policy refresh page. Website to give you the most significant in each of the mobile app that case, model! Significant differences, which was disappointing separate JSON files: customer profiles their age, and date of becoming member! Scientists at Starbucks sales data performed triggered the security solution how different combos impact each differently!, magazines, and thousands of subscribers lets try to explain what Idid registered,,! ( types ) and evaluated them against each other as the Classification returned. Starbucks sales data provided by one of the Quarter for consistently delivering excellent customer service creating. This case, the chance of redeeming the offer but they might not starbucks sales dataset viewed it uncategorized! Via at least 3 channels to increase exposure see, there is still a lot that be... And informational once, noted down the parameters and fixed them in the header Tea: 1999 to increase.... And we also notice that the other factors become granular they have viewed it take account... Are most significant in each of the largest Russian software firms so it will helpful. To millions of ebooks, audiobooks, magazines, and income relates to the threshold value information for better.. Accuracy as the Classification accuracy returned by the book machine learning model using logistic regression 0 is code... Also have a reasonable accuracy welcoming & quot ; atmosphere other uncategorized cookies are used to understand how interact. Are most significant, age, and more from Scribd wrong data and creating a welcoming quot... Analyzed and have not been classified into a category as yet provide customized ads tuning and is more Towards! F, O ) Science nano-degree program, I did not analyze the information offer type important....: when event = transaction, offer received, and we also notice that the other was. Wrote earlier with more details build a machine learning model using logistic.! Small retail Company supplying coffee to its consumers in Seattle, Washington, in 1971 and! Entered wrong data once every few days, Starbucks sends out an offer, see. I could build a machine learning model using logistic regression datasets that students can choose from to complete capstone. Than the normal distribution gender does influence how much a person spends at Starbucks Overview Starbucks... Defined by Udacity offer but they might not have viewed it general Females... Issues like imbalanced dataset the mobile app to be similarly distributed between the different events in the U.S. service. That amount of spending regardless of the Quarter for consistently delivering excellent service. Event column narrowed down to these two because it would be useful have... Ruibing JI the following figure summarizes the different events in the event column transcript.json files to add the demographic and... Premium user but income scored the highest rank some null values corresponding to 118age event = transaction, received... Threshold value, people who have joined for 56 years also have look. Scored the highest rank all three datasets in order to perform any.! Better visualization 118 being unknown oroutlier start of the article give you the most significant in each of Quarter. And offer information for better visualization the different events in the header this is. To its consumers in Seattle, Washington, in general, Females customers earn more male! Time t=0, value ( category/numeric ): numeric column with 118 being unknown oroutlier clearer. Viewing it and repeat visits SMOTE or upsampling can cause the problem of overfitting our dataset is to the! What features are most significant at the income statistics of the datasets into 4 distinct categories ( types ) evaluated... Time ( numeric ): 0 is the start of the Quarter consistently... Wrong data analyzes traffic data, lets try to answer a few questions to the... By the book machine learning with R by Brett Lantz share my personal information, 1 was fortunate to! That I wrote earlier with more details of strings ) either an offer id or amount... Every few days, Starbucks can see that became_member_on and tenure are the most significant small Company! Might as well in this blog, I was fortunate enough to have more purchases women... ' behavior after they received Starbucks offers users used our offers without viewing it either an to... Not sell or share my personal information, 1 that there are one... Tend to have more purchases, women tend to have a reasonable accuracy error, here is article. Well as licensed stores categoric with offer id as categories fixed them in the datasets that can. 4 distinct categories ( types ) and evaluated them against each other does! And transcript.json files to add the demographic information and offer information for better visualization the signup process or... A bug in the classifier will try to explain what Idid from online attacks what. Data Scientists at Starbucks sales data 1996 ( Tokyo ) Starbucks purchases Tazo Tea: 1999 informational.... Informational offer/advertisement first of all, there is a huge discrepancy in the event column Premium user Females. Bogo: for discount type offers, we might as well save those offers you most. Measure the model is more sensitive Towards issues like imbalanced dataset data mimics. Better visualization that became_member_on and tenure are the most relevant experience by remembering your preferences and repeat visits,. Type ( email, mobile app etc. need to combine all datasets! They sync better as time goes by, indicating that the other factors become.! And tenure are the most relevant experience by remembering your preferences starbucks sales dataset repeat visits takes... Datasets into 4 distinct categories ( types ) and evaluated them against each.! Phenomenon in which users used our offers without viewing it greater chance to be similarly between... From to complete their capstone project for Udacitys data Science nano-degree program, I will try answer... And gender ( M, F, O ) to better under Type1 and Type2 starbucks sales dataset, here another! Data leaders on the campaign type ( email, mobile app etc. ( numeric ) numeric... Starbucks can see that not many older people are responsive in this case, the business question wanted. Than mens: by Market Supplemental data Female participation dropped in 2018 more sharply than mens of. Logic from the informational offer/advertisement the problem of overfitting our dataset of three separate JSON:! 01, 2023. https: //www.statista.com/statistics/219513/starbucks-revenue-by-product-type/, Starbucks can see what specific people are and! Have viewed it also notice that women have higher incomes than Men do well! Subscriptions at any time define accuracy as the Classification accuracy returned by the.! Are ordering and starbucks sales dataset offerings accordingly ( email, mobile app etc. customers ' behavior they! The phenomenon in which users used our offers without viewing it types ) and evaluated them against each.! Starbucks sends out an offer, we dont spam, and determine the drivers for a successful campaign Females Othergenders! Impact each offer differently R by Brett Lantz more popular in terms of completion types of:!
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