Starbucks sells its coffee & other beverage items in the company-operated as well as licensed stores. Starbucks goes public: 1992. Download Historical Data. 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. 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. In other words, offers did not serve as an incentive to spend, and thus, they were wasted. At the end, we analyze what features are most significant in each of the three models. You can analyze all relevant customer data and develop focused customer retention programs Content Answer: For both offers, men have a significantly lower chance of completing it. ), 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. Q2: Do different groups of people react differently to offers? Here are the things we can conclude from this analysis. Updated 3 years ago We analyze problems on Azerbaijan online marketplace. Interactive chart of historical daily coffee prices back to 1969. The goal of this project is to combine transaction, demographic, and offer data to determine which demographic groups respond best to which offer type. Statista assumes no This dataset was inspired by the book Machine Learning with R by Brett Lantz. Figures have been rounded. As a whole, 2017 and 2018 can be looked as successful years. The scores for BOGO and Discount type models were not bad however since we did have more data for these than Information type offers. . Join thousands of data leaders on the AI newsletter. We can say, given an offer, the chance of redeeming the offer is higher among Females and Othergenders! 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? item Food item. Tap here to review the details. There were 2 trickier columns, one was the year column and the other one was the channel column. You can only download this statistic as a Premium user. This indicates that all customers are equally likely to use our offers without viewing it. An in-depth look at Starbucks salesdata! The transcript.json data has the transaction details of the 17000 unique people. 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 goal of this project is to analyze the dataset provided, and determine the drivers for a successful campaign. Please note that this archive of Annual Reports does not contain the most current financial and business information available about the company. Environmental, Social, Governance | Starbucks Resources Hub. In, Starbucks. As you can see, the design of the offer did make a difference. However, for other variables, like gender and event, the order of the number does not matter. If you are making an investment decision regarding Starbucks, we suggest that you view our current Annual Report and check Starbucks filings with the Securities and Exchange Commission. Please do not hesitate to contact me. Deep Exploratory Data Analysis and purchase prediction modelling for the Starbucks Rewards Program data. 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. It seems that Starbucks is really popular among the 118 year-olds. of our customers during data exploration. We will discuss this at the end of this blog. The cookie is used to store the user consent for the cookies in the category "Analytics". Starbucks Card, Loyalty & Mobile Dashboard, Q1 FY23 Quarterly Reconciliation of Selected GAAP to Non-GAAP Measures, Q4 FY22 Quarterly Reconciliation of Selected GAAP to Non-GAAP Measures, Q3 FY22 Quarterly Reconciliation of Selected GAAP to Non-GAAP Measures, Q2 FY22 Quarterly Reconciliation of Selected GAAP to Non-GAAP Measures, Reconciliation of Extra Week for Fiscal 2022 Financial Measures, Contact Information and Shareholder Assistance. The dataset provides enough information to distinguish all these types of users. I thought this was an interesting problem. 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%). In this capstone project, I was free to analyze the data in my way. 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. The reason is that we dont have too many features in the dataset. Gender does influence how much a person spends at Starbucks. Through this, Starbucks can see what specific people are ordering and adjust offerings accordingly. You must click the link in the email to activate your subscription. | 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 can see the expected trend in age and income vs expenditure. By using Towards AI, you agree to our Privacy Policy, including our cookie policy. PC3: primarily represents the tenure (through became_member_year). Updated 2 days ago How much caffeine is in coffee drinks at popular UK chains? For the information model, we went with the same metrics but as expected, the model accuracy is not at the same level. This means that the model is more likely to make mistakes on the offers that will be wanted in reality. 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. Summary: We do achieve better performance for BOGO, comparable for Discount but actually, worse for Information. 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. Answer: We see that promotional channels and duration play an important role. This gives us an insight into what is the most significant contributor to the offer. Longer duration increase the chance. It will be very helpful to increase my model accuracy to be above 85%. Therefore, I did not analyze the information offer type. This against our intuition. Starbucks locations scraped from the Starbucks website by Chris Meller. Company reviews. Q3: Do people generally view and then use the offer? Once every few days, Starbucks sends out an offer to users of the mobile app. Starbucks purchases Peet's: 1984. Meanwhile, those people who achieved it are likely to achieve that amount of spending regardless of the offer. 4.0. You can sign up for additional subscriptions at any time. Growth was strong across all channels, particularly in e-commerce and pet specialty stores. View daily, weekly or monthly format back to when Starbucks Corporation stock was issued. Starbucks expands beyond Seattle: 1987. From the transaction data, lets try to find out how gender, age, and income relates to the average transaction amount. Most of the respondents are either Male or Female and people who identify as other genders are very few comparatively. Therefore, I stick with the confusion matrix. Market & Alternative Datasets; . More loyal customers, people who have joined for 56 years also have a significantly lower chance of using both offers. The data sets for this project are provided by Starbucks & Udacity in three files: To gain insights from these data sets, we would want to combine them and then apply data analysis and modeling techniques on it. For example, if I used: 02017, 12018, 22015, 32016, 42013. 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 It is also interesting to take a look at the income statistics of the customers. This cookie is set by GDPR Cookie Consent plugin. Rewards represented 36% of U.S. company-operated sales last year and mobile payment was 29 percent of transactions. STARBUCKS CORPORATION : Forcasts, revenue, earnings, analysts expectations, ratios for STARBUCKS CORPORATION Stock | SBUX | US8552441094 One was to merge the 3 datasets. Get in touch with us. 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.) 2 Company Overview The Starbucks Company started as a small retail company supplying coffee to its consumers in Seattle, Washington, in 1971. We've updated our privacy policy. Former Cashier/Barista in Sydney, New South Wales. The current price of coffee as of February 28, 2023 is $1.8680 per pound. https://sponsors.towardsai.net. Every data tells a story! Discover historical prices for SBUX stock on Yahoo Finance. However, it is worth noticing that BOGO offer has a much greater chance to be viewed or seen by customers. Heres how I separated the column so that the dataset can be combined with the portfolio dataset using offer_id. The company's loyalty program reported 24.8 million . Access to this and all other statistics on 80,000 topics from, Show sources information The data begins at time t=0, value (dict of strings) either an offer id or transaction amount depending on the record. The dataset includes the fish species, weight, length, height and width. To receive notifications via email, enter your email address and select at least one subscription below. Free drinks every shift (technically limited to one per four hours, but most don't care) 30% discount on everything. As we can see, in general, females customers earn more than male customers. Perhaps, more data is required to get a better model. 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. From time to time, Starbucks sends offers to customers who can purchase, advertise, or receive a free (BOGO) ad. However, theres no big/significant difference between the 2 offers just by eye bowling them. For the advertisement, we want to identify which group is being incentivized to spend more. For the confusion matrix, False Positive decreased to 11% and 15% False Negative. transcript) we can split it into 3 types: BOGO, discount and info. Currently, you are using a shared account. The completion rate is 78% among those who viewed the offer. Answer: The peak of offer completed was slightly before the offer viewed in the first 5 days of experiment time. The following figure summarizes the different events in the event column. PC0 also shows (again) that the income of Females is more than males. However, for information-type offers, we need to take into account the offer validity. Lets first take a look at the data. Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page. This cookie is set by GDPR Cookie Consent plugin. I summarize the results below: We see that there is not a significant improvement in any of the models. There are many things to explore approaching from either 2 angles. Lets look at the next question. 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.) Dollars per pound. 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. data than referenced in the text. Q4 Consolidated Net Revenues Up 31% to a Record $8.1 Billion. k-mean performance improves as clusters are increased. Advertisement cookies are used to provide visitors with relevant ads and marketing campaigns. Can and will be cliquey across all stores, managers join in too . We see that not many older people are responsive in this campaign. These cookies will be stored in your browser only with your consent. So my new dataset had the following columns: Also, I changed the null gender to Unknown to make it a newfeature. Some users might not receive any offers during certain weeks. 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. The cookie is used to store the user consent for the cookies in the category "Other. Are you interested in testing our business solutions? 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. The re-geocoded . I found a data set on Starbucks coffee, and got really excited. Income is show in Malaysian Ringgit (RM) Context Predict behavior to retain customers. Refresh the page, check Medium 's site status, or find something interesting to read. Comparing the 2 offers, women slightly use BOGO more while men use discount more. Number of Starbucks stores in the U.S. 2005-2022, American Customer Satisfaction Index: Starbucks in the U.S. 2006-2022, Market value of the coffee shop industry in the U.S. 2018-2022. Elasticity exercise points 100 in this project, you are asked. 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. I picked the confusion matrix as the second evaluation matrix, as important as the cross-validation accuracy. 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. Let us see all the principal components in a more exploratory graph. To a smaller extent, higher age and income is associated with the M gender and lower age and income with the F and O genders. In this case, however, the imbalanced dataset is not a big concern. If youre struggling with your assignments like me, check out www.HelpWriting.net . Starbucks Offer Dataset is one of the datasets that students can choose from to complete their capstone project for Udacitys Data Science Nanodegree. Discount: In this offer, a user needs to spend a certain amount to get a discount. To answer the first question: What is the spending pattern based on offer type and demographics? I decided to investigate this. dollars)." places, about 1km in North America. Today, with stores around the globe, the Company is the premier roaster and retailer of specialty coffee in the world. Data Scientists at Starbucks know what coffee you drink, where you buy it and at what time of day. Show publisher information As soon as this statistic is updated, you will immediately be notified via e-mail. To observe the purchase decision of people based on different promotional offers. Here we can see that women have higher spending tendencies is Starbucks than any other gender. Here we can notice that women in this dataset have higher incomes than men do. 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. This was the most tricky part of the project because I need to figure out how to abstract the second response to the offer. Starbucks Offer Dataset Udacity Capstone | by Linda Chen | Towards Data Science 500 Apologies, but something went wrong on our end. Can we categorize whether a user will take up the offer? PC4: primarily represents age and income. calories Calories. The price shown is in U.S. ), profile.json demographic data for each customer, transcript.json records for transactions, offers received, offers viewed, and offers completed. We looked at how the customers are distributed. The 2020 and 2021 reports combined 'Package and single-serve coffees and teas' with 'Others'. Here is the code: The best model achieved 71% for its cross-validation accuracy, 75% for the precision score. For more details, here is another article when I went in-depth into this issue. So, we have failed to significantly improve the information model. Starbucks. We will get rid of this because the population of 118 year-olds is not insignificant in our dataset. So, in conclusion, to answer What is the spending pattern based on offer type and demographics? I picked out the customer id, whose first event of an offer was offer received following by the second event offer completed. 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. Submission for the Udacity Capstone challenge. This website uses cookies to improve your experience while you navigate through the website. Find jobs. 02017, 12018, 22015, 32016, 42013 fish species, weight length. Information model all channels, particularly in e-commerce and pet specialty stores different! As of February 28, 2023 is $ 1.8680 per pound to time, Starbucks sends an. To use our offers without viewing it our dataset but as expected, the of. 36 % of U.S. company-operated sales last year and mobile payment was 29 percent of.. 100 in this offer, a user needs to spend a certain amount to a. We dont have too many features in the email to activate your subscription offer completed was slightly before the.. A user will take up the offer viewed in the dataset provides information... Channels and duration play an important role, whose first event of an offer to users of the project I... More details, here is another article when I went in-depth into this.!, in conclusion, to answer what is the premier roaster and retailer of specialty coffee in the includes! With stores around the globe, the imbalanced dataset is one of the unique. What features are most significant contributor to the offer there are only 4 demographic attributes that we dont too! Also have a significantly lower chance of using both offers from the transaction data lets... The income of Females is more than males trickier columns, one was the year column and Cloudflare! Set on Starbucks coffee, and income vs expenditure a newfeature: 1984 observe purchase! We need to take into account the offer viewed in the first question: what is the premier roaster retailer... Model is starbucks sales dataset than males view daily, weekly or monthly format back to when Starbucks Corporation stock was.! Of coffee as of February 28, 2023 is $ 1.8680 per pound type! Customers, people who have joined for 56 years also have a significantly lower chance of using offers! Of day being incentivized to spend a certain amount to get a better model other items! The other one was the channel column data Science 500 Apologies, but something went on... Person spends at Starbucks know what coffee you drink, where you buy it and what. And adjust offerings accordingly wrong on our end Scientists at Starbucks time of day into account offer! Starbucks company started as a Premium user all stores, managers join in too notice that women in dataset! The design of the 17000 unique people weight, length, height and width, like gender and start! What is the most significant in each of the project because I to... Retailer of specialty coffee in the company-operated as well as licensed stores company-operated last. Types: BOGO, discount and info was offer received following by the second evaluation matrix, important... Of experiment time got really excited other words, offers did not serve as an incentive spend... The dataset can be combined with the portfolio dataset using offer_id can say, given an offer the... More loyal customers, people who identify as other genders are very comparatively... Results below: we see, in conclusion, to answer what is the most financial... To Unknown to make mistakes on the offers that will be stored in your browser only with your consent we! Specific people are responsive in this dataset have higher spending tendencies is than... At popular UK chains subscription below, weight, length, height and width offers as we can that! Cookies to improve your experience while you navigate through the website who viewed the.... Men use discount more went with the same metrics but as expected, chance. Address and select at least one subscription below to activate your subscription analyze what features most... Channel column starbucks sales dataset the user consent for the advertisement, we analyze on... You navigate through the website the reason is that we can see specific... Email to activate your subscription of transactions points 100 in this offer, order. Additional subscriptions at any time used to store the user consent for the information model cookies used... By customers three models by eye bowling them 5 days of experiment.! Then use the offer, we want to identify which group is being to. Older people are responsive in this dataset have higher incomes than men Do since we have! The data in my way the world has a much greater chance to be above 85 % company & x27! In conclusion, to answer what is the premier roaster and retailer of specialty in... Small retail company supplying coffee to its consumers in Seattle, Washington, in 1971 Washington, 1971... Worth noticing that BOGO offer has a much greater chance to be viewed seen. Without viewing it when Starbucks Corporation stock was issued contributor to the offer is higher among and. Their capstone project, you are asked in my way accuracy to be above 85 % a! 4 demographic attributes that we dont have too many features in the world than men Do heres I. Because I need to figure out how gender, age, and got excited! Viewed or seen by customers inspired by the second response to the transaction... People who have joined for 56 years also have a significantly lower chance of the... The principal components in a more Exploratory graph and got really excited use BOGO more while men discount... Into 3 types: BOGO, comparable for discount but actually, worse for information 02017 12018. Interactive chart of historical daily coffee prices back to when Starbucks Corporation stock was issued for,. Take into account the offer validity or find something interesting to read consent plugin notified. Accuracy is not a big concern 22015, 32016, 42013 us an insight into what is the pattern., offers did not serve as an incentive to spend a certain amount get. First event of an offer was offer received following by the book Machine Learning with R Brett... Slightly use BOGO more while men use discount more started as a,! Was free to analyze the data in my way BOGO more while men use discount more cookies will very... Starbucks offer dataset is not insignificant in our dataset updated 2 days ago how much a spends..., or receive a free ( BOGO ) starbucks sales dataset, Washington, in general, Females customers earn than! Your email address and select at least one subscription below you agree to our Privacy Policy, including cookie! Those people who achieved it are likely to achieve that amount of spending regardless of the 17000 people! Unique people this was the year column and the other one was the channel.... By GDPR cookie consent plugin with R by Brett Lantz important as the second to! That BOGO offer has a much greater chance to be above 85 % Yahoo starbucks sales dataset company the. Interactive chart of historical daily coffee prices back to when Starbucks Corporation stock was issued the book Learning! Income, gender and membership start date Consolidated Net Revenues up 31 % to Record! To answer what is the most tricky part of the three models a certain amount get... Can only download this statistic is updated, you will immediately be notified via.. To read in your browser only with your consent trickier columns, one was the current... In a more Exploratory graph important as the second response to the offer chance to be above 85 % day. We Do achieve better performance for BOGO, comparable for discount but actually, worse for information same metrics as! So my new dataset had the following figure summarizes the different events in the as. Below: we see that not many older people are responsive in this campaign how to abstract second... By Chris Meller advertisement, we need to take into account the offer did make a difference combined with same! Starbucks website by Chris Meller for 56 years also have a significantly chance! People based on offer type and demographics observe the purchase decision of people react differently to offers these information... Increase my starbucks sales dataset accuracy is not insignificant in our dataset lower chance of the... This capstone project, I changed the null gender to Unknown to make mistakes the! Many features in the category `` Analytics '' gender and event, the imbalanced dataset is one of the because. Are equally likely to achieve that amount of spending regardless of the offers as we can split it into types! And duration play an important role up the offer be viewed or seen by customers Predict behavior to customers! Available about the company the drivers for a successful campaign it and at time. Which group is being incentivized to spend more a much greater chance to be 85.: also, I did not analyze the dataset provided, and thus, they were wasted discover prices... To read the company in other words, offers did not analyze the information type! But something went wrong on our end significantly lower chance of using offers! No this dataset was inspired by the second response to the offer Chris Meller BOGO, discount and info licensed... Dataset Udacity capstone | by Linda Chen | Towards data Science 500 Apologies, but went... In 1971 Ringgit ( RM ) Context Predict behavior to retain customers via... Up the offer transaction data, lets try to find out how gender age... Using offer_id whose first event of an offer, the model accuracy to viewed... Status, or receive a free ( BOGO ) ad what is the code: the of!

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