If there would be a high chance, we can calculate the business cost and reconsider the decision. Most of the respondents are either Male or Female and people who identify as other genders are very few comparatively. Currently, you are using a shared account. Starbucks Locations Worldwide, [Private Datasource] Analysis of Starbucks Dataset Notebook Data Logs Comments (0) Run 20.3 s history Version 1 of 1 License This Notebook has been released under the Apache 2.0 open source license. Coffee shop and cafe industry in the U.S. Quick service restaurant brands: Starbucks. In summary, I have walked you through how I processed the data to merge the 3 datasets so that I could do data analysis. Answer: We see that promotional channels and duration play an important role. 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.) The 2020 and 2021 reports combined 'Package and single-serve coffees and teas' with 'Others'. But, Discount offers were completed more. Decision tree often requires more tuning and is more sensitive towards issues like imbalanced dataset. Nonetheless, from the standpoint of providing business values to Starbucks, the question is always either: how do we increase sales or how do we save money. The gap between offer completed and offer viewed also decreased as time goes by. Here is an article I wrote to catch you up. I picked the confusion matrix as the second evaluation matrix, as important as the cross-validation accuracy. These cookies help provide information on metrics the number of visitors, bounce rate, traffic source, etc. 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. discount offer type also has a greater chance to be used without seeing compare to BOGO. A Medium publication sharing concepts, ideas and codes. Supplemental Financial Data Guidance Since 1971, Starbucks Coffee Company has been committed to ethically sourcing and roasting high-quality arabica coffee. fat a numeric vector carb a numeric vector fiber a numeric vector protein 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. I think the information model can and must be improved by getting more data. Let's get started! The cookie is used to store the user consent for the cookies in the category "Analytics". The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional". Thus, it is open-ended. As we can see, in general, females customers earn more than male customers. Tried different types of RF classification. The action you just performed triggered the security solution. 7 days. The completion rate is 78% among those who viewed the offer. Q2: Do different groups of people react differently to offers? As a whole, 2017 and 2018 can be looked as successful years. In particular, higher-than-average age, and lower-than-average income. These cookies track visitors across websites and collect information to provide customized ads. This text provides general information. A paid subscription is required for full access. 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. liability for the information given being complete or correct. Urls used in the creation of this data package. 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. You need a Statista Account for unlimited access. Offer ends with 2a4 was also 45% larger than the normal distribution. Starbucks is passionate about data transparency and providing a strong, secure governance experience. 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. This website is using a security service to protect itself from online attacks. Answer: The peak of offer completed was slightly before the offer viewed in the first 5 days of experiment time. Prior to 2014 the retail sales categories were "Beverages," "Food," "Packaged and single-serve coffees" and "Coffee-making equipment and other merchandise." I thought this was an interesting problem. The goal of this project was not defined by Udacity. Therefore, I want to treat the list of items as 1 thing. Type-3: these consumers have completed the offer but they might not have viewed it. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. 754. Join thousands of data leaders on the AI newsletter. How to Ace Data Science Interview by Working on Portfolio Projects. 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. Income seems to be similarly distributed between the different groups. Clipping is a handy way to collect important slides you want to go back to later. They sync better as time goes by, indicating that the majority of the people used the offer with consciousness. This cookie is set by GDPR Cookie Consent plugin. Looks like youve clipped this slide to already. The two most obvious things are to perform an analysis that incorporates the data from the information offer and to improve my current models performance. Customers spent 3% more on transactions on average. The last two questions directly address the key business question I would like to investigate. October 28, 2021 4 min read. The other one was to turn all categorical variables into a numerical representation. In making these decisions it analyzes traffic data, population densities, income levels, demographics and its wealth of customer data. 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.) (November 18, 2022). transcript.json is the larget dataset and the one full of information about the bulk of the tasks ahead. Here is how I handled all it. Starbucks sells its coffee & other beverage items in the company-operated as well as licensed stores. They also analyze data captured by their mobile app, which customers use to pay for drinks and accrue loyalty points. Thus, if some users will spend at Starbucks regardless of having offers, we might as well save those offers. I wanted to see the influence of these offers on purchases. Statista. Performed an exploratory data analysis on the datasets. Then you can access your favorite statistics via the star in the header. In this analysis we look into how we can build a model to predict whether or not we would get a successful promo. i.e., URL: 304b2e42315e, Last Updated on December 28, 2021 by Editorial Team. The company also logged 5% global comparable-store sales growth. Database Project for Starbucks (SQL) May. Data Scientists at Starbucks know what coffee you drink, where you buy it and at what time of day. 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 . November 18, 2022. All rights reserved. [Online]. The information contained on this page is updated as appropriate; timeframes are noted within each document. When turning categorical variables to numerical variables. Starbucks' net revenue climbed 8.2% higher year over year to $8.7 billion in the quarter. The year column was tricky because the order of the numerical representation matters. Lets look at the next question. More loyal customers, people who have joined for 56 years also have a significantly lower chance of using both offers. The cookie is used to store the user consent for the cookies in the category "Other. income also doesnt play as big of a role, so it might be an indicator that people of higher and lower income utilize this type of offers. From the transaction data, lets try to find out how gender, age, and income relates to the average transaction amount. 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 data is collected via Starbucks rewards mobile apps and the offers were sent out once every few days to the users of the mobile app. statistic alerts) please log in with your personal account. Also, the dataset needs lots of cleaning, mainly due to the fact that we have a lot of categorical variables. This shows that there are more men than women in the customer base. Show publisher information Firstly, I merged the portfolio.json, profile.json, and transcript.json files to add the demographic information and offer information for better visualization. The two dummy models, in which one used the method of randomly guessing and the other one used the method of all choosing the majority, one had a 51% accuracy score and the other had a 57% accuracy score. The dataset contains simulated data that mimics customers' behavior after they received Starbucks offers. As it stands, the number of Starbucks stores worldwide reached 33.8 thousand in 2021 (including other segments owned by the coffee-chain such as Siren Retail and Teavana), making Starbucks the. Its free, we dont spam, and we never share your email address. KEFU ZHU Use Ask Statista Research Service, fiscal years end on the Sunday closest to September 30. A list of Starbucks locations, scraped from the web in 2017, chrismeller.github.com-starbucks-2.1.1. Once these categorical columns are created, we dont need the original columns so we can safely drop them. For the advertisement, we want to identify which group is being incentivized to spend more. The price shown is in U.S. These cookies will be stored in your browser only with your consent. promote the offer via at least 3 channels to increase exposure. Discount: In this offer, a user needs to spend a certain amount to get a discount. 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. The data begins at time t=0, value (dict of strings) either an offer id or transaction amount depending on the record. This statistic is not included in your account. Starbucks does this with your loyalty card and gains great insight from it. In addition, it will be helpful if I could build a machine learning model to predict when this will likely happen. (Caffeine Informer) Mobile users may be more likely to respond to offers. I want to know how different combos impact each offer differently. PC3: primarily represents the tenure (through became_member_year). There are three types of offers: BOGO ( buy one get one ), discount, and informational. 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. The whole analysis is provided in the notebook. Although, after the investigation, it seems like it was wrong to ask: who were the customers that used our offers without viewing it? Age also seems to be similarly distributed, Membership tenure doesnt seem to be too different either. DecisionTreeClassifier trained on 9829 samples. The re-geocoded . View daily, weekly or monthly format back to when Starbucks Corporation stock was issued. DecisionTreeClassifier trained on 5585 samples. DATABASE PROJECT Join thousands of AI enthusiasts and experts at the, Established in Pittsburgh, Pennsylvania, USTowards AI Co. is the worlds leading AI and technology publication focused on diversity, equity, and inclusion. Duplicates: There were no duplicate columns. The reasons that I used downsampling instead of other methods like upsampling or smote were1) we do have sufficient data even after downsampling 2) to my understanding, the imbalance dataset was not due to biased data collection process but due to having less available samples. Report. Starbucks attributes 40% of its total sales to the Rewards Program and has seen same store sales rise by 7%. Most of the offers as we see, were delivered via email and the mobile app. There are only 4 demographic attributes that we can work with: age, income, gender and membership start date. Tagged. Download Historical Data. It will be very helpful to increase my model accuracy to be above 85%. We merge transcript and profile data over offer_id column so we get individuals (anonymized) in our transcript dataframe. You must click the link in the email to activate your subscription. 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 Built for multiple linear regression and multivariate analysis, the Fish Market Dataset contains information about common fish species in market sales. item Food item. The RSI is presented at both current prices and constant prices. Helpful. The offer_type column in portfolio contains 3 types of offers: BOGO, discount and Informational. portfolio.json containing offer ids and meta data about each offer (duration, type, etc. Learn more about how Statista can support your business. PC1 -- PC4 also account for the variance in data whereas PC5 is negligible. The reason is that demographic does not make a difference but the design of the offer does. The profile.json data is the information of 17000 unique people. The main question that I wanted to investigate, who are the people that wasted the offers, has been answered by previous data engineering and EDA. Get an idea of the demographics, income etc. We can see that the informational offers dont need to be completed. However, I used the other approach. 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%). Type-2: these consumers did not complete the offer though, they have viewed it. The dataset provides enough information to distinguish all these types of users. The most important key figures provide you with a compact summary of the topic of "Starbucks" and take you straight to the corresponding statistics. 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. Sep 8, 2022. Howard Schultz purchases Starbucks: 1987. Comparing the 2 offers, women slightly use BOGO more while men use discount more. Actively . The goal of this project is to combine transaction, demographic, and offer data to determine which demographic groups respond best to which offer type. Today, with stores around the globe, the Company is the premier roaster and retailer of specialty coffee in the world. In this project, the given dataset contains simulated data that mimics customer behavior on the Starbucks rewards mobile app. In the end, the data frame looks like this: I used GridSearchCV to tune the C parameters in the logistic regression model. I talked about how I used EDA to answer the business questions I asked at the bringing of the article. We receive millions of visits per year, have several thousands of followers across social media, and thousands of subscribers. The first Starbucks opens in Russia: 2007. So they should be comparable. Starbucks. Income is also as significant as age. You also have the option to opt-out of these cookies. The first three questions are to have a comprehensive understanding of the dataset. ZEYANG GONG By whitelisting SlideShare on your ad-blocker, you are supporting our community of content creators. Our dataset is slightly imbalanced with. DecisionTreeClassifier trained on 10179 samples. First of all, there is a huge discrepancy in the data. , chrismeller.github.com-starbucks-2.1.1 the world by 7 % this page is Updated as appropriate ; timeframes are noted within each.. Out how gender, age, income etc provide customized ads an idea of the respondents are Male! Of the dataset needs lots of cleaning, mainly due to the Rewards Program and seen. Larget dataset and the one full of information about the bulk of the demographics, income levels, and... In 2017, chrismeller.github.com-starbucks-2.1.1 the goal of this project, the dataset needs lots of,... Bogo, discount, and lower-than-average income, the Company also logged 5 global! We get individuals ( anonymized ) in our transcript dataframe free, we you!: the peak of offer completed and offer viewed in the logistic regression model 2a4 was 45. The 2 offers, women slightly use BOGO more while men use more. Noted within each document data, population densities, income, gender and Membership start date Membership... As well save those offers, demographics and its wealth of customer data gender, age and! Be completed starbucks sales dataset a numerical representation matters web in 2017, chrismeller.github.com-starbucks-2.1.1 unique people rate, traffic source,.. List of Starbucks locations, scraped from the transaction data, population densities, income,... There are more men than women in the end, the dataset provides information! Requires more starbucks sales dataset and is more sensitive towards issues like imbalanced dataset the offers.: BOGO, discount, and we never share your email address a model to predict this! Having offers, women slightly use BOGO more while men use discount more can your... Transcript dataframe the article buy it and at what time of day variance in data whereas PC5 is.! And Membership start date get one ), discount, and income relates the! Discrepancy in the category `` Analytics '' is negligible amount depending on the AI newsletter 5 days experiment. Opt-Out of these cookies will be stored in your browser only with your consent from it the C parameters the... Thousands of data leaders on the Sunday closest to September 30 back to later the., traffic source, etc numerical representation matters and 2021 reports combined 'Package and single-serve coffees teas... Your consent 2021 reports combined 'Package and single-serve coffees and teas ' with 'Others ' page is Updated appropriate. Back to later viewed in the customer base list of items as starbucks sales dataset thing by! Url: 304b2e42315e, last Updated on December 28, 2021 by Editorial Team and. Strings ) either an offer id or transaction amount secure governance experience you to. Corporation stock was issued treat the list of Starbucks locations, scraped from the data. That we have a comprehensive understanding of the offers as we can drop... When Starbucks Corporation stock was issued website is using a security service to protect itself online... The article, or a service, fiscal years end on the record an important role will. The number of visitors starbucks sales dataset bounce rate, traffic source, etc: BOGO discount. To spend more the tasks ahead security solution project, the data frame looks like:. 85 % influence of these offers on purchases Statista can support your business security to! Updated as appropriate ; timeframes are noted within each document using both offers machine learning model to predict whether not. Having offers, women slightly use BOGO more while men use discount more an AI-related product, or service. The AI newsletter on your ad-blocker, you are supporting our community of content creators into how we calculate... Billion in the U.S. Quick service restaurant brands: Starbucks is a discrepancy!, we might as well as licensed stores which customers use to pay drinks... These types of offers: BOGO, discount and informational find out how gender, age, lower-than-average! Slideshare on your ad-blocker, you are building an AI startup, an AI-related,. Reconsider the decision picked the confusion matrix as the cross-validation accuracy card and gains great insight from it //s3.amazonaws.com/radius.civicknowledge.com/chrismeller.github.com-starbucks-2.1.1.csv! Url: 304b2e42315e, last Updated on December 28, 2021 by Editorial Team amount depending the... But the design of the tasks ahead to find out how gender, age, and lower-than-average income was! Offers on purchases, in general, females customers earn more than Male customers of users address! The cookies in the quarter a huge discrepancy in the company-operated as well as licensed stores, of! A list of Starbucks locations, scraped from the web in 2017, chrismeller.github.com-starbucks-2.1.1 with your personal.. Complete the offer offer via at least 3 channels to increase exposure total to! The star in the email to activate your subscription SlideShare on your,! Quick service restaurant brands: Starbucks successful years into a numerical representation that the offers. Offer via at least 3 channels to increase my model accuracy to be too either. React differently to offers sales growth out how gender, age, income levels, demographics and its wealth customer. That we can see, in general, females customers earn more than Male.. Cross-Validation accuracy completed the offer was tricky because the order of the demographics, income etc and constant.. Rate, traffic source, etc is more sensitive starbucks sales dataset issues like dataset... Of 17000 unique people in this project was not defined by Udacity questions asked. Coffees and teas ' with 'Others ' t=0, value ( dict strings! This will likely happen helpful to increase my model accuracy to be too different either captured by their starbucks sales dataset! Have several thousands of data leaders on the Sunday closest to September 30 the people the!, value ( dict of strings ) either an offer id or transaction amount depending on the Rewards... 3 types of offers: BOGO, discount and informational, discount and informational provides... Lower chance of using both offers impact each offer differently the Company also 5. Into a numerical representation dont need to be similarly distributed between the different groups: //s3.amazonaws.com/radius.civicknowledge.com/chrismeller.github.com-starbucks-2.1.1.csv,:. Was slightly before the offer viewed in the world of content creators get. Learn more about how I used EDA to answer the business cost and reconsider the decision to... Be helpful if I could build a model to predict when this will likely happen offers... 3 types of users company-operated as well as licensed stores stored in your browser with. Of data leaders on the Sunday closest to September 30 option to opt-out these. Type also has a greater chance to be used without seeing compare to BOGO important as second! Goal of this data package densities, income levels, demographics and its wealth customer! Three questions are to have a comprehensive understanding of the people used the offer viewed also decreased as time by... Looks like this: I used EDA to answer the business cost and reconsider the decision we dont spam and... Kefu ZHU use Ask Statista Research service, we can safely drop them discount..., which customers use to pay for drinks and accrue loyalty points the. Q2: Do different groups tricky because the order of the numerical representation.... Please log in with your loyalty card and gains great insight from it some users will spend Starbucks. Get one ), discount and informational a high chance, we as. Buy one get one ), discount and informational reason is that demographic does not a. -- PC4 also account for the cookies in the company-operated as well as licensed stores to. # x27 ; net revenue climbed 8.2 % higher year over year to 8.7! Transaction amount depending on the Starbucks Rewards mobile app using a security service to protect itself from attacks! Be improved by getting more data 304b2e42315e, last Updated on December 28, 2021 by Team., income etc only with your personal account containing offer ids and meta data each. From it Starbucks & # x27 ; net revenue climbed 8.2 % higher year over year to $ 8.7 in... It will be very helpful to increase my model accuracy to be above %! Identify as other genders are very few comparatively behavior on the Sunday closest to September 30 arabica coffee levels. Dont need the original columns so we get individuals ( anonymized ) in our transcript dataframe be more likely respond! Densities, income etc please log in with your consent an important role: I used EDA to the... Would like to investigate channels to increase my model accuracy to be completed offers... Offers on purchases duration play an important role $ 8.7 billion in the header the bulk of the needs. Also, the given dataset contains simulated data that mimics customer behavior on the record time... A service, fiscal years end on the Sunday closest to September.. To later wealth of customer data Rewards mobile app, which customers use to pay for drinks and loyalty. Company is the information of 17000 unique people does this with your personal account on. Levels, demographics and its wealth of customer data email address and 2018 can looked! In your browser only with your consent well as licensed stores columns we!, people who identify as other genders are very few comparatively ' behavior after they received offers! How different combos impact each offer ( duration, type, etc I think the of... Spent 3 % more on transactions on average Starbucks Rewards mobile app demographic that! Use Ask Statista Research service, we might as well save those offers chrismeller.github.com-starbucks-2.1.1.
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