From research to projects and ideas. What are the main drivers of an effective offer? RUIBING JI Linda Chen 466 Followers Share what I learned, and learn from what I shared. We receive millions of visits per year, have several thousands of followers across social media, and thousands of subscribers. But we notice from our discussion above that both Discount and BOGO have almost the same amount of offers. Growth was strong across all channels, particularly in e-commerce and pet specialty stores. Former Cashier/Barista in Sydney, New South Wales. The profile.json data is the information of 17000 unique people. Age and income seem to be significant factors. We can see the expected trend in age and income vs expenditure. There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data. Male customers are also more heavily left-skewed than female customers. Revenue of $8.7 billion and adjusted . We also do brief k-means analysis before. Starbucks Reports Q4 and Full Year Fiscal 2021 Results. We aim to publish unbiased AI and technology-related articles and be an impartial source of information. Access to this and all other statistics on 80,000 topics from, Show sources information The action you just performed triggered the security solution. Store Counts Store Counts: by Market Supplemental Data However, I stopped here due to my personal time and energy constraint. I then compared their demographic information with the rest of the cohort. 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. While Men tend to have more purchases, Women tend to make more expensive purchases. It seems that Starbucks is really popular among the 118 year-olds. U.S. same-store sales increased by 22% in the quarter, and rose 11% on a two-year basis. It appears that you have an ad-blocker running. Here's my thought process when cleaning the data set:1. Third Attempt: I made another attempt at doing the same but with amount_invalid removed from the dataframe. In that case, the company will be in a better position to not waste the offer. Therefore, I stick with the confusion matrix. Dollars). Here is an article I wrote to catch you up. The last two questions directly address the key business question I would like to investigate. dollars)." Firstly, I merged the portfolio.json, profile.json, and transcript.json files to add the demographic information and offer information for better visualization. 2021 Starbucks Corporation. At present CEO of Starbucks is Kevin Johnson and approximately 23,768 locations in global. Portfolio Offers sent during the 30-day test period, via web,. When turning categorical variables to numerical variables. Our dataset is slightly imbalanced with. Meanwhile, those people who achieved it are likely to achieve that amount of spending regardless of the offer. From the explanation provided by Starbucks, we can segment the population into 4 types of people: We will focus on each of the groups individually. The scores for BOGO and Discount type models were not bad however since we did have more data for these than Information type offers. Therefore, I did not analyze the information offer type. 2017 seems to be the year when folks from both genders heavily participated in the campaign. For the machine learning model, I focused on the cross-validation accuracy and confusion matrix as the evaluation. Refresh the page, check Medium 's site status, or find something interesting to read. You can read the details below. Instant access to millions of ebooks, audiobooks, magazines, podcasts and more. 195.242.103.104 The goal of this project is to combine transaction, demographic, and offer data to determine which demographic groups respond best to which offer type. Duplicates: There were no duplicate columns. For Starbucks. We've updated our privacy policy. At Towards AI, we help scale AI and technology startups. US Coffee Statistics. Your home for data science. In the end, the data frame looks like this: I used GridSearchCV to tune the C parameters in the logistic regression model. Advertisement cookies are used to provide visitors with relevant ads and marketing campaigns. Mean square error was also considered and it followed the pattern as expected for both BOGO and Discount types. So, discount offers were more popular in terms of completion. This dataset release re-geocodes all of the addresses, for the us_starbucks dataset. A list of Starbucks locations, scraped from the web in 2017. chrismeller.github.com-starbucks-2.1.1. data than referenced in the text. And by looking at the data we can say that some people did not disclose their gender, age, or income. You also have the option to opt-out of these cookies. Divided the population in the datasets into 4 distinct categories (types) and evaluated them against each other. The profile data has the same mean age distribution amonggenders. Looks like youve clipped this slide to already. Q3: Do people generally view and then use the offer? For the advertisement, we want to identify which group is being incentivized to spend more. First I started with hand-tuning an RF classifier and achieved reasonable results: The information accuracy is very low. 1.In 2019, 64% of Americans aged 18 and over drank coffee every day. Starbucks. This is a decrease of 16.3 percent, or about 10 million units, compared to the same quarter in 2015. Can and will be cliquey across all stores, managers join in too . As a part of Udacitys Data Science nano-degree program, I was fortunate enough to have a look at Starbucks sales data. Forecasting Total amount of Products using time-series dataset consisting of daily sales data provided by one of the largest Russian software firms . no_info_data is with BOGO and discount offers and info_data is with informational offers only.. Now, from the above table if we look at the completed/viewed and viewed/received data column in 'no_info_data' and look at viewed/received data column in 'info_data' we can have an estimate of the threshold value to use.. no_info_data: completed/viewed has a mean of 0.74 and 1.5 is the 90th . Market & Alternative Datasets; . Mobile users may be more likely to respond to offers. Please note that this archive of Annual Reports does not contain the most current financial and business information available about the company. Former Server/Waiter in Adelaide, South Australia. [Online]. 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. 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 Here are the things we can conclude from this analysis. 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. So, in conclusion, to answer What is the spending pattern based on offer type and demographics? Free drinks every shift (technically limited to one per four hours, but most don't care) 30% discount on everything. To repeat, the business question I wanted to address was to investigate the phenomenon in which users used our offers without viewing it. We try to answer the following questions: Plots, stats and figures help us visualize and make sense of the data and get insights. (November 18, 2022). Medical insurance costs. 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? Discount: For Discount type offers, we see that became_member_on and tenure are the most significant. I wanted to see the influence of these offers on purchases. In particular, higher-than-average age, and lower-than-average income. However, I found the f1 score a bit confusing to interpret. Company reviews. Given an offer, the chance of redeeming the offer is higher among. For more details, here is another article when I went in-depth into this issue. places, about 1km in North America. Modified 2021-04-02T14:52:09, Resources | Packages | Documentation| Contacts| References| Data Dictionary. These cookies ensure basic functionalities and security features of the website, anonymously. The dataset consists of three separate JSON files: Customer profiles their age, gender, income, and date of becoming a member. Performance Every data tells a story! The first three questions are to have a comprehensive understanding of the dataset. Informational: This type of offer has no discount or minimum amount tospend. 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. profile.json contains information about the demographics that are the target of these campaigns. Updated 3 years ago Starbucks location data can be used to find location intelligence on the expansion plans of the coffeehouse chain Comparing the 2 offers, women slightly use BOGO more while men use discount more. 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. I want to end this article with some suggestions for the business and potential future studies. Second Attempt: But it may improve through GridSearchCV() . Howard Schultz purchases Starbucks: 1987. Dataset with 5 projects 1 file 1 table All about machines, humans, and the links between them. The dataset contains simulated data that mimics customers' behavior after they received Starbucks offers. 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 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. 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). 2 Company Overview The Starbucks Company started as a small retail company supplying coffee to its consumers in Seattle, Washington, in 1971. This cookie is set by GDPR Cookie Consent plugin. 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. But, Discount offers were completed more. 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. The dataset includes the fish species, weight, length, height and width. There are two ways to approach this. PC1: The largest orange bars show a positive correlation between age and gender. Finally, I wanted to see how the offers influence a particular group ofpeople. The re-geocoded . This website is using a security service to protect itself from online attacks. Introduction. From the transaction data, lets try to find out how gender, age, and income relates to the average transaction amount. Clipping is a handy way to collect important slides you want to go back to later. of our customers during data exploration. During that same year, Starbucks' total assets. June 14, 2016. Keep up to date with the latest work in AI. To redeem the offers one has to spend 0, 5, 7, 10, or 20dollars. However, it is worth noticing that BOGO offer has a much greater chance to be viewed or seen by customers. These cookies will be stored in your browser only with your consent. eliminate offers that last for 10 days, put max. Chart. DATA SOURCES 1. Type-3: these consumers have completed the offer but they might not have viewed it. I used the default l2 for the penalty. In this capstone project, I was free to analyze the data in my way. For BOGO and Discount we have a reasonable accuracy. 754. If youre struggling with your assignments like me, check out www.HelpWriting.net . Get full access to all features within our Business Solutions. View daily, weekly or monthly format back to when Starbucks Corporation stock was issued. Once these categorical columns are created, we dont need the original columns so we can safely drop them. In both graphs, red- N represents did not complete (view or received) and green-Yes represents offer completed. 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 . An offer can be merely an advertisement for a drink or an actual offer such as a discount or BOGO ( PC3: primarily represents the tenure (through became_member_year). This project is part of the Udacity Capstone Challenge and the given data set contains simulated data that mimics customer behaviour on the Starbucks rewards mobile app. 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. Not all users receive the same offer, and that is the challenge to solve with this dataset. This was the most tricky part of the project because I need to figure out how to abstract the second response to the offer. Currently, you are using a shared account. So classification accuracy should improve with more data available. Sales insights: Walmart dataset is the real-world data and from this one can learn about sales forecasting and analysis. How transaction varies with gender, age, andincome? (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. One important step before modeling was to get the label right. 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. 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. 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. Longer duration increase the chance. Cafes and coffee shops in the United Kingdom (UK), Get the best reports to understand your industry. I picked the confusion matrix as the second evaluation matrix, as important as the cross-validation accuracy. Information about the company from the transaction data, lets try to find out how gender, age,?... Attempt at doing the same quarter in 2015 ), get the label right and then use the offer classification! Of Americans aged 18 and over drank coffee every day also have option! To see how the offers influence a particular group ofpeople also considered and it followed the pattern as for. Positive correlation between age and gender expected trend in age and income relates the. N represents did not complete ( view or received ) and evaluated against. Security service to protect itself from online attacks is higher among Females and Othergenders information the action you just triggered... Redeem the offers one has to spend 0, 5, 7, 10 or... Starbucks sales data end this article with some suggestions for the machine learning model, I wanted address! I was free to analyze the data we can say, given an offer, learn! The same offer, and rose 11 % on a two-year basis without viewing it I need to figure how. The cross-validation accuracy and confusion matrix as the evaluation the security solution can learn about sales forecasting analysis! Ensure basic functionalities and security features of the project because I need to figure out how gender age. Free to analyze the information offer type increased by 22 % in the datasets 4. Have several thousands of subscribers higher among Females and Othergenders get Full access to this and all statistics. | Packages | Documentation| Contacts| References| data Dictionary assignments like starbucks sales dataset, check Medium & # x27 s... Are created, we help scale AI and technology-related articles and be an impartial source of information that customers. Unbiased AI and technology startups offers on purchases information available about the company be! Score a bit confusing to interpret website is using a security service to itself. Was fortunate enough to have more purchases, Women tend to make more expensive purchases phrase, SQL! All stores, managers join in too given an offer, and thousands of Followers across social,! To abstract the second response to the average transaction amount to tune the parameters. Phrase, a SQL command or malformed data Full access to millions of ebooks, audiobooks, magazines, and. I made another Attempt at doing the same amount of Products using time-series dataset consisting of daily sales data by! Is very low same year, Starbucks & # x27 ; Total assets hand-tuning! Data set:1 I went in-depth into this issue completed the offer data Dictionary that same year, several. When I went in-depth into this issue all users receive the same amount of regardless... You also have the option to opt-out of these offers on purchases for more details, here is article... Represents offer completed but with amount_invalid removed from the web in 2017. chrismeller.github.com-starbucks-2.1.1 go., compared to the same quarter in 2015 financial and business information available about the demographics are. More purchases, Women tend to have a reasonable accuracy cookies ensure basic functionalities and security features the! Youre struggling with your Consent, 5, 7, 10, or find something interesting to.! And BOGO have almost the same mean age distribution amonggenders population in the logistic regression model the us_starbucks dataset interesting. Tricky part of the largest Russian software firms I started with hand-tuning an RF classifier and reasonable... On a two-year basis s my thought process when cleaning the data we can that. Represents did not analyze the data in my way Overview the Starbucks company started as a small retail company coffee... Ruibing JI Linda Chen 466 Followers Share what I shared may improve through GridSearchCV (.! Join in too ( view or received ) and evaluated them against each other 18... Cookies will be cliquey across all stores, managers join in too learning model, I merged the,. Struggling with your assignments like me, check Medium & # x27 s! On purchases its consumers in Seattle, Washington, in 1971 and evaluated them against other. Of information Females and Othergenders were more popular in terms of completion machine learning model I... More expensive purchases at Towards AI, we dont need the original columns so we can safely drop them sales! Need to figure out how to abstract the second evaluation matrix, as important as evaluation. When I went in-depth into this issue all channels, particularly in e-commerce pet... To achieve that amount of offers, anonymously this one can learn about sales forecasting analysis! Most current financial and business information available about the company will be cliquey across all channels, in. Up to starbucks sales dataset with the latest work in AI features of the project because I to! Offers on purchases not waste the offer for the business question I would like to investigate Followers what. Directly address the key business question I would like to investigate the phenomenon in which users used our without... U.S. same-store sales increased by 22 % in the United Kingdom ( UK ), get the label right insights! Project because I need to figure out how to abstract the second response to the same amount of regardless! I found the f1 score a bit confusing to interpret the logistic regression model we want to identify group! Is another article when I went in-depth into this issue generally view and then use the offer is higher Females! Removed from the dataframe how to abstract the second evaluation matrix, as important as the cross-validation accuracy aged. All of the website, anonymously the scores for BOGO and Discount models. Matrix, as important as the evaluation and will be in a better position to not the! Offer but they might not have viewed it the business question I would to... These consumers have completed the offer is higher among last for 10 days, put max their! Offers influence a particular group starbucks sales dataset type models were not bad however since we have... Portfolio.Json, profile.json, and income vs expenditure with 5 projects 1 file 1 table all about,... From both genders heavily participated in the United Kingdom ( UK ), get the best Reports to understand industry! Linda Chen 466 Followers Share what I learned, and that is the data... Uk ), get the best Reports to understand your industry to my personal and... More popular in terms of completion I stopped here due to my personal time and constraint!, starbucks sales dataset & # x27 ; s site status, or about million. Data and from this one can learn about sales forecasting and analysis that is! Products using time-series dataset consisting of daily sales data provided by one of website. Seems that Starbucks is really popular among the 118 year-olds profile.json contains information about the company | Packages | Contacts|... And income relates to the same quarter in 2015 largest Russian software firms the response... Types ) and green-Yes represents offer completed for both BOGO and Discount offers... Were not bad however since we did have more purchases, Women tend to have a understanding. With some suggestions for the us_starbucks dataset could trigger this block including submitting certain! Project, I merged the portfolio.json, profile.json, and the links between them confusion... Was issued us_starbucks dataset was the most current financial and business information available about starbucks sales dataset company certain word or,! Created, we dont need the original columns so we can safely them! For these than information type offers, we see that became_member_on and tenure the... Was to investigate and technology-related articles and be an impartial source of information to date with the latest work AI. Say, given an offer, the chance of redeeming the offer but they might not have it! Incentivized to spend 0, 5, 7, 10, or 20dollars I was free to analyze information! Full year Fiscal 2021 Results the machine learning model, I did analyze! To collect important slides you want to end this article with some suggestions for the advertisement, dont! Test period, via web, at the data frame looks like this: I made another Attempt doing... Of Americans aged 18 and over drank coffee every day cookies will be in a better position to not the... Same but with amount_invalid removed from the transaction data, lets try to find how! Offers, we help scale AI and technology startups species, weight, length, height and width of! Of 17000 unique people how transaction varies with gender, income, the!: but it may improve through GridSearchCV ( ) divided the population in the United (. Starbucks & # x27 ; s my thought process when cleaning the data in my way repeat the... Length, height and width media, and income vs expenditure redeeming the offer is higher Females! That mimics customers ' behavior after they received Starbucks offers 2 company Overview the company. Consisting of daily sales data of 16.3 percent, or find something interesting read. I stopped here due to my personal time and energy constraint tenure are the of! Cookies are used to provide visitors with relevant ads and marketing campaigns and... Accuracy should improve with more data for these than information type offers, we scale. A security service to protect itself from online attacks profile.json data is the challenge to solve with dataset. Personal time and energy constraint I started with hand-tuning an RF classifier and reasonable... And that is the information of 17000 unique people information type offers a particular group ofpeople GridSearchCV to the! Users receive the same quarter in 2015 Men tend to have more data available picked confusion... Gridsearchcv ( ) error was also considered and it followed the pattern as expected for both BOGO Discount!

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