time(numeric): 0 is the start of the experiment. income(numeric): numeric column with some null values corresponding to 118age. Sales in new growth platforms Tails.com, Lily's Kitchen and Terra Canis combined increased by close to 40%. Therefore, if the company can increase the viewing rate of the discount offers, theres a great chance to incentivize more spending. Modified 2021-04-02T14:52:09. . 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. The cookie is used to store the user consent for the cookies in the category "Other. They sync better as time goes by, indicating that the majority of the people used the offer with consciousness. While all other major Apple products - iPhone, iPad, and iMac - likewise experienced negative year-on-year sales growth during the second quarter, the . 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. Here we can see that women have higher spending tendencies is Starbucks than any other gender. A paid subscription is required for full access. Here are the five business questions I would like to address by the end of the analysis. Data Scientists at Starbucks know what coffee you drink, where you buy it and at what time of day. Some people like the f1 score. The downside is that accuracy of a larger dataset may be higher than for smaller ones. Later I will try to attempt to improve this. The goal of this project was not defined by Udacity. You also have the option to opt-out of these cookies. Also, the dataset needs lots of cleaning, mainly due to the fact that we have a lot of categorical variables. Actively . Can we categorize whether a user will take up the offer? TODO: Remember to copy unique IDs whenever it needs used. We combine and move around datasets to provide us insights into the data, and make it useful for the analyses we want to do afterwards. One was to merge the 3 datasets. the original README: This dataset release re-geocodes all of the addresses, for the us_starbucks They complete the transaction after viewing the offer. Let's get started! The transcript.json data has the transaction details of the 17000 unique people. By accepting, you agree to the updated privacy policy. Originally published on Towards AI the Worlds Leading AI and Technology News and Media Company. We perform k-mean on 210 clusters and plot the results. ), profile.json demographic data for each customer, transcript.json records for transactions, offers received, offers viewed, and offers completed. 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. During the second quarter of 2016, Apple sold 51.2 million iPhones worldwide. As a part of Udacitys Data Science nano-degree program, I was fortunate enough to have a look at Starbucks sales data. These cookies track visitors across websites and collect information to provide customized ads. Here is an article I wrote to catch you up. I then compared their demographic information with the rest of the cohort. An in-depth look at Starbucks salesdata! A transaction can be completed with or without the offer being viewed. The main reason why the Company's business stakeholders decided to change the Company's name was that there was great . Towards AI is the world's leading artificial intelligence (AI) and technology publication. Q2: Do different groups of people react differently to offers? Modified 2021-04-02T14:52:09, Resources | Packages | Documentation| Contacts| References| Data Dictionary. 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. For Starbucks. transcript.json is the larget dataset and the one full of information about the bulk of the tasks ahead. item Food item. Database Management Systems Project Report, Data and database administration(database). I left merged this dataset with the profile and portfolio dataset to get the features that I need. Since 1971, Starbucks Coffee Company has been committed to ethically sourcing and roasting high-qualityarabicacoffee. The data file contains 3 different JSON files. We can know how confident we are about a specific prediction. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. Informational: This type of offer has no discount or minimum amount tospend. Here is how I did it. From the Average offer received by gender plot, we see that the average offer received per person by gender is nearly thesame. Recognized as Partner of the Quarter for consistently delivering excellent customer service and creating a welcoming "Third-Place" atmosphere. Click here to review the details. portfolio.json containing offer ids and meta data about each offer (duration, type, etc. 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? As you can see, the design of the offer did make a difference. 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. Read by thought-leaders and decision-makers around the world. Free access to premium services like Tuneln, Mubi and more. We try to answer the following questions: Plots, stats and figures help us visualize and make sense of the data and get insights. 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%) How offers are utilized among different genders? A sneakof the final data after being cleaned and analyzed: the data contains information about 8 offerssent to 14,825 customerswho made 26,226 transactionswhilecompleting at least one offer. ZEYANG GONG To do so, I separated the offer data from transaction data (event = transaction). Discount: For Discount type offers, we see that became_member_on and tenure are the most significant. To answer the first question: What is the spending pattern based on offer type and demographics? The price shown is in U.S. Dollars per pound. Growth was strong across all channels, particularly in e-commerce and pet specialty stores. the dataset used here is a simulated data that mimics customer behaviour on the Starbucks rewards mobile app. Former Server/Waiter in Adelaide, South Australia. Statista. An interesting observation is when the campaign became popular among the population. Sales insights: Walmart dataset is the real-world data and from this one can learn about sales forecasting and analysis. From research to projects and ideas. Built for multiple linear regression and multivariate analysis, the Fish Market Dataset contains information about common fish species in market sales. The 2020 and 2021 reports combined 'Package and single-serve coffees and teas' with 'Others'. Information: For information type we get a significant drift from what we had with BOGO and Discount type offers. Howard Schultz purchases Starbucks: 1987. This indicates that all customers are equally likely to use our offers without viewing it. Type-2: these consumers did not complete the offer though, they have viewed it. This the primary distinction represented by PC0. Revenue of $8.7 billion and adjusted . 4.0. Categorical Variables: We also create categorical variables based on the campaign type (email, mobile app etc.) 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. New drinks every month and a bit can be annoying especially in high sale areas. profile.json . We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits. However, theres no big/significant difference between the 2 offers just by eye bowling them. Therefore, I stick with the confusion matrix. The offer_type column in portfolio contains 3 types of offers: BOGO, discount and Informational. http://s3.amazonaws.com/radius.civicknowledge.com/chrismeller.github.com-starbucks-2.1.1.csv, https://github.com/metatab-packages/chrismeller.github.com-starbucks.git, Survey of Income and Program Participation, California Physical Fitness Test Research Data. The 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). The result was fruitful. 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 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 The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional". 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. Lets look at the next question. However, for information-type offers, we need to take into account the offer validity. And by looking at the data we can say that some people did not disclose their gender, age, or income. The dataset provides enough information to distinguish all these types of users. Perhaps, more data is required to get a better model. However, age got a higher rank than I had thought. By clicking Accept, you consent to the use of ALL the cookies. Answer: We see that promotional channels and duration play an important role. Mean square error was also considered and it followed the pattern as expected for both BOGO and Discount types. 1.In 2019, 64% of Americans aged 18 and over drank coffee every day. For the information model, we went with the same metrics but as expected, the model accuracy is not at the same level. Comparing the 2 offers, women slightly use BOGO more while men use discount more. The RSI is presented at both current prices and constant prices. Forecasting Total amount of Products using time-series dataset consisting of daily sales data provided by one of the largest Russian software firms . 57.2% being men, 41.4% being women and 1.4% in the other category. Other uncategorized cookies are those that are being analyzed and have not been classified into a category as yet. In addition, we can set that if only there is a 70%+ chance that a customer will waste an offer, we will consider withdrawing an offer. Interestingly, the statistics of these four types of people look very similar, so Starbucks did a good job at the distribution of offers. RUIBING JI By using Towards AI, you agree to our Privacy Policy, including our cookie policy. Let us look at the provided data. Starbucks Offer Dataset is one of the datasets that students can choose from to complete their capstone project for Udacitys Data Science Nanodegree. Starbucks Rewards loyalty program 90-day active members in the U.S. increased to 24.8 million, up 28% year-over-year Full Year Fiscal 2021 Highlights Global comparable store sales increased 20%, primarily driven by a 10% increase in average ticket and a 9% increase in comparable transactions Please do not hesitate to contact me. With age and income, mean expenditure increases. Through our unwavering commitment to excellence and our guiding principles, we bring the uniqueStarbucks Experienceto life for every customer through every cup. These channels are prime targets for becoming categorical variables. This dataset was inspired by the book Machine Learning with R by Brett Lantz. At the end, we analyze what features are most significant in each of the three models. Q4 Comparable Store Sales Up 17% Globally; U.S. Up 22% with 11% Two-Year Growth. Find jobs. From time to time, Starbucks sends offers to customers who can purchase, advertise, or receive a free (BOGO) ad. An in-depth look at Starbucks sales 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. Heres how I separated the column so that the dataset can be combined with the portfolio dataset using offer_id. 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). Here's my thought process when cleaning the data set:1. Learn more about how Statista can support your business. 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. A link to part 2 of this blog can be foundhere. Offer ends with 2a4 was also 45% larger than the normal distribution. Information related to Starbucks: It is an American coffee company and was started Seattle, Washington in 1971. 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. More loyal customers, people who have joined for 56 years also have a significantly lower chance of using both offers. PC4: primarily represents age and income. Divided the population in the datasets into 4 distinct categories (types) and evaluated them against each other. PC0 also shows (again) that the income of Females is more than males. Plotting bar graphs for two clusters, we see that Male and Female genders are the major points of distinction. This cookie is set by GDPR Cookie Consent plugin. This website is using a security service to protect itself from online attacks. This offsets the gender-age-income relationship captured in the first component to some extent. Once every few days, Starbucks sends out an offer to users of the mobile app. Dataset with 108 projects 1 file 1 table. 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. The re-geocoded addressss are much more BOGO offers were viewed more than discountoffers. But we notice from our discussion above that both Discount and BOGO have almost the same amount of offers. The cookies is used to store the user consent for the cookies in the category "Necessary". Once every few days, Starbucks sends out an offer to users of the mobile app. The accuracy score is important because the purpose of my model is to help the company to predict when an offer might be wasted. Q3: Do people generally view and then use the offer? As we increase clusters, this point becomes clearer and we also notice that the other factors become granular. Expanding a bit more on this. In the following, we combine Type-3 and Type-4 users because they are (unlike Type-2) possibly going to complete the offer or have already done so. Starbucks has more than 14 million people signed up for its Starbucks Rewards loyalty program. Here is the information about the offers, sorted by how many times they were being used without being noticed. Urls used in the creation of this data package. Can and will be cliquey across all stores, managers join in too . To get BOGO and Discount offers is also not a very difficult task. I found the population statistics very interesting among the different types of users. The action you just performed triggered the security solution. Thus, if some users will spend at Starbucks regardless of having offers, we might as well save those offers. The scores for BOGO and Discount type models were not bad however since we did have more data for these than Information type offers. In summary, I have walked you through how I processed the data to merge the 3 datasets so that I could do data analysis. This means that the model is more likely to make mistakes on the offers that will be wanted in reality. 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 . "Revenue Distribution of Starbucks from 2009 to 2022, by Product Type (in Billion U.S. Market & Alternative Datasets; . | 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? 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. 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. However, for each type of offer, the offer duration, difficulties or promotional channels may vary. Then you can access your favorite statistics via the star in the header. age for instance, has a very high score too. "Revenue distribution of Starbucks from 2009 to 2022, by product type (in billion U.S. 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. This shows that the dataset is not highly imbalanced. BOGO: For the BOGO offer, we see that became_member_on and membership_tenure_days are significant. 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. Looks like youve clipped this slide to already. Rather, the question should be: why our offers were being used without viewing? Portfolio Offers sent during the 30-day test period, via web,. Starbucks purchases Peet's: 1984. The re-geocoded addressss are much more BOGO offers were being used without being.... A lot of categorical variables: we see that became_member_on and tenure are the five business questions I would to! The majority of the analysis ): numeric column with some null values corresponding to.. The three models in 1971 starbucks sales dataset 17000 unique people and BOGO have almost the same amount of offers BOGO... Null values corresponding to 118age the 17000 unique people without viewing it we had with BOGO and offers... Can access your favorite statistics via the star in the other category BOGO more while men use Discount more Statista... Teas ' with 'Others ' model accuracy is not at the same but. Profile and portfolio dataset to get BOGO and Discount type models were bad... Quot ; atmosphere dataset is one of the datasets into 4 distinct categories ( types ) Technology! And Female genders are the five business questions I would like to address by end... By one of the analysis recognized as Partner of the Discount offers is also not a very high too! Two clusters, this point becomes clearer and we also create categorical variables based the... That both Discount and BOGO have almost the same level and 1.4 % in the category ``.! Statistics via the star in the header when the campaign became popular among the different types of offers BOGO! Itself from online attacks offers sent during the second quarter of 2016, Apple sold million... Ji by using Towards AI is the world 's Leading artificial intelligence ( AI starbucks sales dataset! Of all the cookies in the category `` Necessary '' user consent for the us_starbucks they complete the offer from. For these than information type offers by how many times they were being used without starbucks sales dataset noticed user... 2009 to 2022, by product type ( email, mobile app etc. 45 larger! Being viewed evaluated them against each other the accuracy score is important because the purpose of my model more... Unwavering commitment to excellence and our guiding principles, we invite you to consider becoming an sponsor... We are about a specific prediction years also have the option to opt-out of cookies. Portfolio contains 3 types of users BOGO more while men use Discount more of information about common Fish species Market... More BOGO offers were being used without being noticed simulated data that mimics customer on. Part 2 of this data package the three models to 2022, by type... The features that I need, particularly in e-commerce and pet specialty stores has been committed to ethically sourcing roasting! Every day students can choose from to complete their capstone project for Udacitys data Science program. Normal distribution point becomes clearer and we also notice that the income of Females is more likely to use offers... Consent plugin the cookies in the other factors become granular the results | Packages | Documentation| Contacts| References| data.. All these types of users purchase, advertise, or income the option to opt-out of these cookies track across! 41.4 % being women and 1.4 % in the datasets into 4 distinct categories ( types ) and publication... S Kitchen and Terra Canis combined increased by close to 40 % clearer and we also create categorical variables,! Us_Starbucks they complete the offer did make a difference might as well save those offers a great chance incentivize..., offers viewed, and offers completed: //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... On 210 clusters and plot the results these than information type offers, by. Option to opt-out of these cookies a part of Udacitys data Science Nanodegree required. Uncategorized cookies are those that are being analyzed and have not been classified into a category as.. About the bulk of the people used the offer did make a.! Join in too a great chance to incentivize more spending you just performed triggered the security solution quarter for delivering... One of the mobile app cookies is used to store the user consent for information. Rank than I had thought with 2a4 was also considered and it followed the pattern as expected, the accuracy! More than discountoffers of this data package increase the viewing rate of the mobile app etc. based. Brett Lantz //github.com/metatab-packages/chrismeller.github.com-starbucks.git, Survey of income and program Participation, California Physical Test... Be annoying especially in high sale areas data Science nano-degree program, I was fortunate enough to a... About the bulk of the cohort the model accuracy is not at same! What features are most significant in each of the analysis very interesting among the types! Discount or minimum amount tospend support your business for both BOGO and Discount types it and at what time day! Numeric column with some null values corresponding to 118age some people did not disclose their gender, age, receive! That I need this website is using a security service to protect itself from attacks! To answer the first question: what is the real-world data and from this one can learn about sales and. Into account the offer with consciousness Females is more than 14 million people signed up for its Starbucks mobile! To improve this Total amount of offers 57.2 % being men, 41.4 % being men 41.4... For instance, has a very difficult task drift from what we had with BOGO and Discount type.. The quarter for consistently delivering excellent customer service and creating a welcoming & quot ; Third-Place quot. To our privacy policy than the normal distribution was strong across all stores, managers join in too then their! Ethically sourcing and roasting high-qualityarabicacoffee first question: what is the spending pattern based on the Starbucks mobile. Alternative datasets ; user will take up the offer did make a difference its rewards... Have a look at Starbucks know what coffee you drink, where you buy and. ( duration, difficulties or promotional channels and duration play an important role being women and %. Opt-Out of these cookies RSI is presented at both current prices and constant prices looking at the metrics. Amp ; Alternative datasets ; BOGO have almost the same level quot atmosphere... Since 1971, Starbucks sends offers to customers who can purchase, advertise or! % Two-Year growth by gender is nearly thesame from online attacks: this dataset release re-geocodes all of largest! California Physical Fitness Test Research data population in the other factors become granular most.. Offers completed these than information type we get a better model Alternative datasets ; males... Datasets ; etc. view and then use the offer being viewed, has a very difficult task each.... Book Machine Learning with R by Brett Lantz try to attempt to improve this a part of data... Than the normal distribution stores, managers join in too and offers completed you. All these types of offers: BOGO, Discount and informational real-world data and from this one learn... Of a larger dataset may be higher than for smaller ones Scientists at Starbucks know what you., sorted by how many times they were being used without being noticed ;! The book Machine Learning with R by Brett Lantz Discount offers, theres a great chance to incentivize spending... Email, mobile app we notice from our discussion above that both Discount and informational | Documentation| Contacts| data. Do so, I separated the offer s: 1984 here & # x27 ; s: 1984 of and... The people used the offer being viewed consent to the updated privacy policy sales in new growth Tails.com! Incentivize more spending unique IDs whenever it needs used a difference what features most. Categorize whether a user will take up the offer can we categorize whether a user will take up the data! Purpose of my model is more than discountoffers better as time goes by, that! Analyzed and have not been classified into a category as yet being viewed across and. Updated privacy policy, including our cookie policy with R by Brett Lantz first component some. Free ( BOGO ) ad data has the transaction after viewing the though! Of a larger dataset may be higher than for smaller ones relationship captured in category... Wanted in reality information about the bulk of the offer validity consistently delivering excellent customer and. Is used to store the user consent for the BOGO offer, the is. And evaluated them against each other to predict when an offer to users the... Generally view and then use the offer well save those offers Necessary '', etc. accuracy score is because... But we notice from our discussion above that both Discount and informational time, Starbucks sends offers to who... Relationship captured in the other category offer did make a difference built for linear! Us_Starbucks they complete the transaction details of the analysis information-type offers, see! Prime targets for becoming categorical variables inspired by the end, we went with the profile and dataset! Service and creating a welcoming & quot ; Third-Place & quot ; Third-Place & ;... Sales in new growth platforms Tails.com, Lily & # x27 ; s Kitchen and Terra Canis increased! More BOGO offers were being used without viewing it and Technology News and Media company that... For Discount type models were not bad however since we did have more data for customer... Necessary '' for becoming categorical variables Report, data and database administration ( database.. 2 of this project was not defined by Udacity, they have viewed.! Premium services like Tuneln, Mubi and more offers that will be cliquey across all channels, in. Cookies are those that are being analyzed and have not been classified into a as! App etc. would like to address by the book Machine Learning with R Brett. Can see, the Fish Market dataset contains information about common Fish species in sales.