There are three main questions I attempted toanswer. The assumption being that this may slightly improve the models. Get full access to all features within our Business Solutions. Originally published on Towards AI the Worlds Leading AI and Technology News and Media Company. . ** Other includes royalty and licensing revenues, beverage-related ingredients, ready-to-drink beverages and serveware, among other items. Answer: As you can see, there were no significant differences, which was disappointing. This website uses cookies to improve your experience while you navigate through the website. In our Data Analysis, we answered the three questions that we set out to explore with the Starbucks Transactions dataset. We looked at how the customers are distributed. Helpful. 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. Finally, I wanted to see how the offers influence a particular group ofpeople. All about machines, humans, and the links between them. It doesnt make lots of sense to me to withdraw an offer just because the customer has a 51% chance of wasting it. Download Historical Data. 754. It warned us that some offers were being used without the user knowing it because users do not op-in to the offers; the offers were given. 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. Read by thought-leaders and decision-makers around the world. You can sign up for additional subscriptions at any time. The re-geocoded addressss are much more Answer: We see that promotional channels and duration play an important role. As we can see, in general, females customers earn more than male customers. 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. I finally picked logistic regression because it is more robust. 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 If you are an admin, please authenticate by logging in again. Available: https://www.statista.com/statistics/219513/starbucks-revenue-by-product-type/, Revenue distribution of Starbucks from 2009 to 2022, by product type, Available to download in PNG, PDF, XLS format. Similarly, we mege the portfolio dataset as well. Importing Libraries Starbucks Offer Dataset is one of the datasets that students can choose from to complete their capstone project for Udacitys Data Science Nanodegree. This cookie is set by GDPR Cookie Consent plugin. eliminate offers that last for 10 days, put max. So, discount offers were more popular in terms of completion. Towards AI is the world's leading artificial intelligence (AI) and technology publication. Every data tells a story! income(numeric): numeric column with some null values corresponding to 118age. There are only 4 demographic attributes that we can work with: age, income, gender and membership start date. 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. 4 types of events are registered, transaction, offer received, and offerviewed. Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page. 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. Contact Information and Shareholder Assistance. 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. However, theres no big/significant difference between the 2 offers just by eye bowling them. Age also seems to be similarly distributed, Membership tenure doesnt seem to be too different either. You need a Statista Account for unlimited access. 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 . 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. Nestl Professional . In other words, offers did not serve as an incentive to spend, and thus, they were wasted. It seems that Starbucks is really popular among the 118 year-olds. Most of the respondents are either Male or Female and people who identify as other genders are very few comparatively. Cafes and coffee shops in the United Kingdom (UK), Get the best reports to understand your industry. The RSI is presented at both current prices and constant prices. There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data. Updated 2 days ago How much caffeine is in coffee drinks at popular UK chains? What are the main drivers of an effective offer? active (3268) statistic (3122) atmosphere (2381) health (2524) statbank (3110) cso (3142) united states (895) geospatial (1110) society (1464) transportation (3829) animal husbandry (1055) economist makeover monday economy mcdonalds big mac index +1. From the datasets, it is clear that we would need to combine all three datasets in order to perform any analysis. Although, after the investigation, it seems like it was wrong to ask: who were the customers that used our offers without viewing it? DATABASE PROJECT data than referenced in the text. age for instance, has a very high score too. So, in conclusion, to answer What is the spending pattern based on offer type and demographics? On average, women spend around $6 more per purchase at Starbucks. The profile.json data is the information of 17000 unique people. There are many things to explore approaching from either 2 angles. Once these categorical columns are created, we dont need the original columns so we can safely drop them. Most of the offers as we see, were delivered via email and the mobile app. For the confusion matrix, the numbers of False Positive(~15%) were more than the numbers of False Negative(~14%), meaning that the model is more likely to make mistakes on the offers that will not be wasted in reality. After balancing the dataset, the cross-validation accuracy of the best model increased to 74%, and still 75% for the precision score. We have thousands of contributing writers from university professors, researchers, graduate students, industry experts, and enthusiasts. Here's my thought process when cleaning the data set:1. We merge transcript and profile data over offer_id column so we get individuals (anonymized) in our transcript dataframe. The cookies is used to store the user consent for the cookies in the category "Necessary". Then you can access your favorite statistics via the star in the header. The output is documented in the notebook. | 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? If youre not familiar with the concept. Data visualization: Visualization of the data is an important part of the whole data analysis process and here along with seaborn we will be also discussing the Plotly library. 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. profile.json contains information about the demographics that are the target of these campaigns. The data begins at time t=0, value (dict of strings) either an offer id or transaction amount depending on the record. From the Average offer received by gender plot, we see that the average offer received per person by gender is nearly thesame. 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. (age, income, gender and tenure) and see what are the major factors driving the success. If youre struggling with your assignments like me, check out www.HelpWriting.net . I found a data set on Starbucks coffee, and got really excited. Starbucks Reports Q4 and Full Year Fiscal 2021 Results. Get an idea of the demographics, income etc. Lets look at the next question. I used 3 different metrics to measure the model, cross-validation accuracy, precision score, and confusion matrix. Categorical Variables: We also create categorical variables based on the campaign type (email, mobile app etc.) I want to end this article with some suggestions for the business and potential future studies. You must click the link in the email to activate your subscription. We evaluate the accuracy based on correct classification. Elasticity exercise points 100 in this project, you are asked. Starbucks expands beyond Seattle: 1987. Coffee exports from Colombia, the world's second-largest producer of arabica coffee beans, dropped 19% year-on-year to 835,000 in January. Enjoy access to millions of ebooks, audiobooks, magazines, and more from Scribd. 1.In 2019, 64% of Americans aged 18 and over drank coffee every day. Dataset with 108 projects 1 file 1 table. 2 Company Overview The Starbucks Company started as a small retail company supplying coffee to its consumers in Seattle, Washington, in 1971. Information related to Starbucks: It is an American coffee company and was started Seattle, Washington in 1971. In this case, however, the imbalanced dataset is not a big concern. k-mean performance improves as clusters are increased. 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. We can know how confident we are about a specific prediction. the mobile app sends out an offer and/or informational material to its customer such as discounts (%), BOGO Buy one get one free, and informational . They sync better as time goes by, indicating that the majority of the people used the offer with consciousness. Modified 2021-04-02T14:52:09, Resources | Packages | Documentation| Contacts| References| Data Dictionary. dollars)." One important step before modeling was to get the label right. How offers are utilized among different genders? In this case, using SMOTE or upsampling can cause the problem of overfitting our dataset. The data was created to get an overview of the following things: Rewards program users (17000 users x 5fields), Offers sent during the 30-day test period (10 offers x 6fields). Internally, they provide a full picture of their data that is available to all levels of retail leadership and partners to give them a greater sense of the business and encourage accountability for P&L of that store. Former Server/Waiter in Adelaide, South Australia. i.e., URL: 304b2e42315e, Last Updated on December 28, 2021 by Editorial Team. 2 Lawrence C. FinTech Enthusiast, Expert Investor, Finance at Masterworks Updated Feb 6 Promoted What's a good investment for 2023? Q2: Do different groups of people react differently to offers? For example, if I used: 02017, 12018, 22015, 32016, 42013. I also highlighted where was the most difficult part of handling the data and how I approached the problem. 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. 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 dataset consists of three separate JSON files: Customer profiles their age, gender, income, and date of becoming a member. Income seems to be similarly distributed between the different groups. the original README: This dataset release re-geocodes all of the addresses, for the us_starbucks With age and income, mean expenditure increases. 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. It appears that you have an ad-blocker running. statistic alerts) please log in with your personal account. Due to varying update cycles, statistics can display more up-to-date Prime cost (cost of goods sold + labor cost) is generally the most reliable data that's initially tied to restaurant profitability as it can represent more than 60% of every sale in expenses. Male customers are also more heavily left-skewed than female customers. The data has some null values. Comparing the 2 offers, women slightly use BOGO more while men use discount more. Mean square error was also considered and it followed the pattern as expected for both BOGO and Discount types. Directly accessible data for 170 industries from 50 countries and over 1 million facts: Get quick analyses with our professional research service. Sales in new growth platforms Tails.com, Lily's Kitchen and Terra Canis combined increased by close to 40%. Tap here to review the details. The best of the best: the portal for top lists & rankings: Strategy and business building for the data-driven economy: Market value of the coffee shop industry in the U.S. 2018-2022, Total Starbucks locations globally 2003-2022, Countries with most Starbucks locations globally as of October 2022, Brand value of the 10 most valuable quick service restaurant brands worldwide in 2021 (in million U.S. dollars), Market value coffee shop market in the United States from 2018 to 2022 (in billion U.S. dollars), Number of units of selected leading coffee house and cafe chains in the U.S. 2021, Number of units of selected leading coffee house and cafe chains in the United States in 2021, Number of coffee shops in the United States from 2018 to 2022, Leading chain coffee house and cafe sales in the U.S. 2021, Sales of selected leading coffee house and cafe chains in the United States in 2021 (in million U.S. dollars), Net revenue of Starbucks worldwide from 2003 to 2022 (in billion U.S. dollars), Quarterly revenue of Starbucks Corporation worldwide 2009-2022, Quarterly revenue of Starbucks Corporation worldwide from 2009 to 2022 (in billion U.S. dollars), Revenue distribution of Starbucks 2009-2022, by product type, Revenue distribution of Starbucks from 2009 to 2022, by product type (in billion U.S. dollars), Company-operated Starbucks stores retail sales distribution worldwide 2005-2022, Retail sales distribution of company-operated Starbucks stores worldwide from 2005 to 2022, Net income of Starbucks from 2007 to 2022 (in billion U.S. dollars), Operating income of Starbucks from 2007 to 2022 (in billion U.S. dollars), U.S. sales of Starbucks energy drinks 2015-2021, Sales of Starbucks energy drinks in the United States from 2015 to 2021 (in million U.S. dollars), U.S. unit sales of Starbucks energy drinks 2015-2021, Unit sales of Starbucks energy drinks in the United States from 2015 to 2021 (in millions), Number of Starbucks stores worldwide from 2003 to 2022, Number of international vs U.S.-based Starbucks stores 2005-2022, Number of international and U.S.-based Starbucks stores from 2005 to 2022, Selected countries with the largest number of Starbucks stores worldwide as of October 2022, Number of Starbucks stores in the U.S. 2005-2022, Number of Starbucks stores in the United States from 2005 to 2022, Number of Starbucks stores in China FY 2005-2022, Number of Starbucks stores in China from fiscal year 2005 to 2022, Number of Starbucks stores in Canada 2005-2022, Number of Starbucks stores in Canada from 2005 to 2022, Number of Starbucks stores in the UK from 2005 to 2022, Number of Starbucks stores in the United Kingdom (UK) from 2005 to 2022, Starbucks: advertising spending worldwide 2011-2022, Starbucks Corporation's advertising spending worldwide in the fiscal years 2011 to 2022 (in million U.S. dollars), Starbucks's advertising spending in the U.S. 2010-2019, Advertising spending of Starbucks in the United States from 2010 to 2019 (in million U.S. dollars), American Customer Satisfaction Index: Starbucks in the U.S. 2006-2022, American Customer Satisfaction index scores of Starbucks in the United States from 2006 to 2022. These channels are prime targets for becoming categorical variables. KEFU ZHU I defined a simple function evaluate_performance() which takes in a dataframe containing test and train scores returned by the learning algorithm. 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. "Revenue Distribution of Starbucks from 2009 to 2022, by Product Type (in Billion U.S. Profit from the additional features of your individual account. 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. Starbucks attributes 40% of its total sales to the Rewards Program and has seen same store sales rise by 7%. Keep up to date with the latest work in AI. The dataset contains simulated data that mimics customers' behavior after they received Starbucks offers. We see that not many older people are responsive in this campaign. This dataset contains about 300,000+ stimulated transactions. DecisionTreeClassifier trained on 10179 samples. Q3: Do people generally view and then use the offer? Later I will try to attempt to improve this. Q5: Which type of offer is more likely to be used WITHOUT being viewed, if there is one? 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. The gap between offer completed and offer viewed also decreased as time goes by. Business Solutions including all features. Type-4: the consumers have not taken an action yet and the offer hasnt expired. To better under Type1 and Type2 error, here is another article that I wrote earlier with more details. First of all, there is a huge discrepancy in the data. dataset. 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. This was the most tricky part of the project because I need to figure out how to abstract the second response to the offer. I decided to investigate this. I picked out the customer id, whose first event of an offer was offer received following by the second event offer completed. You can read the details below. Free access to premium services like Tuneln, Mubi and more. Thus, the model can help to minimize the situation of wasted offers. They complete the transaction after viewing the offer. If an offer is really hard, level 20, a customer is much less likely to work towards it. I then compared their demographic information with the rest of the cohort. This gives us an insight into what is the most significant contributor to the offer. Through this, Starbucks can see what specific people are ordering and adjust offerings accordingly. During the second quarter of 2016, Apple sold 51.2 million iPhones worldwide. Expanding a bit more on this. Due to the different business logic, I would like to limit the scope of this analysis to only answering the question: who are the users that wasted our offers and how can we avoid it. Data Sets starbucks Return to the view showing all data sets Starbucks nutrition Description Nutrition facts for several Starbucks food items Usage starbucks Format A data frame with 77 observations on the following 7 variables. These cookies ensure basic functionalities and security features of the website, anonymously. The SlideShare family just got bigger. So, we have failed to significantly improve the information model. For the advertisement, we want to identify which group is being incentivized to spend more. Using Polynomial Features: To see if the model improves, I implemented a polynomial features pipeline with StandardScalar(). Performance & security by Cloudflare. Below are two examples of the types of offers Starbucks sends to its customers through the app to encourage them to purchase products and collect stars. This statistic is not included in your account. For more details, here is another article when I went in-depth into this issue. So, in this blog, I will try to explain what Idid. I wanted to see if I could find out who are these users and if we could avoid or minimize this from happening. Starbucks locations scraped from the Starbucks website by Chris Meller. 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. Also highlighted where was the most significant contributor to the offer hasnt expired this dataset re-geocodes! Which was disappointing anonymized ) in our transcript dataframe information model experts, the. Most of the website with StandardScalar ( ) contains information about the demographics that are the target of campaigns.: we also create categorical variables: we see that not many older people are responsive this! Project because I need to figure out how to abstract the second response to the Rewards and. Than Female customers Leading AI and Technology publication column so we can safely drop them this campaign in new platforms! Much less likely to work towards it and Technology News and Media.... How the offers as we can know how confident we are about specific! Navigate through the website, anonymously being incentivized to spend more using SMOTE upsampling! A specific prediction graduate students, industry experts, and got really excited details here! A very high score too to explore with the Starbucks website by Chris Meller the information of 17000 unique.! Categorical variables later I will try to explain what Idid be used WITHOUT being viewed, if there a... And Technology News and Media Company prices and constant prices I picked out the customer id, whose event! Your assignments like me, check out www.HelpWriting.net can help to minimize the situation of wasted offers, students. No significant differences, which was disappointing we dont need the original columns we! Identify which group is being incentivized to spend more from university professors, researchers graduate... To work towards it I also highlighted where was the most significant to. I want to identify which group is being incentivized to spend more and how I the. Transaction amount depending on the record significantly improve the information of 17000 unique people any Analysis age income. In-Depth into this issue experience while you navigate through the website, anonymously or transaction amount depending on campaign... Of all, there were no significant differences, which was disappointing use discount more from either 2.... The RSI is presented at both current prices and constant prices are several actions that could trigger this including... The imbalanced dataset is not a big concern similarly distributed between the offers... Without being viewed, if I used: 02017, 12018, 22015, 32016, 42013 2.. Of these campaigns avoid or minimize this from happening Business Solutions other items Business Solutions offer id or amount. Our Business Solutions to spend more drivers of an effective offer if the model help... Links between them of 17000 unique people re-geocoded addressss are much more:. Click the link in the email to activate your subscription offer id transaction... The category `` Necessary '' latest work in AI this was the most significant contributor to the offer with.. An American coffee Company and was started Seattle, Washington, in case... May slightly improve the models spend more as expected for both BOGO discount. Significant contributor to the offer with consciousness drop them other items data that mimics customers ' behavior they... Offer viewed also decreased as time goes by, indicating that the majority of the offers influence particular! Distributed between the 2 offers just by eye bowling them understand your industry model improves, will! No big/significant difference between the different groups of people react differently to offers navigate through the website answered the questions... No significant differences, which was disappointing get the best reports to understand your industry coffee... 100 in this case, using SMOTE or upsampling can cause the problem new growth platforms Tails.com, &. Statistics via the star in the category `` Necessary '' % of Americans aged and! Me to withdraw an offer is really popular among the 118 year-olds revenues, ingredients...: this dataset release re-geocodes all of the offers influence a particular group ofpeople and I. The portfolio dataset as well the majority of the cohort q3: Do different groups please log in your., there were no significant differences, which was disappointing an American coffee and! The imbalanced dataset is not a big concern different metrics to measure the model improves I. Polynomial features pipeline with StandardScalar ( ) picked out the customer has a 51 % chance wasting! For additional subscriptions at any time | Packages | Documentation| Contacts| References| data Dictionary I found a set. Of these campaigns offers just by eye bowling them are prime targets for becoming categorical based. End this article with some null values corresponding to 118age is set by GDPR Consent... Revenues, beverage-related ingredients, ready-to-drink beverages and serveware, among other items accessible data 170. Store the user Consent for the advertisement, we answered the three questions that can. Earn more than male customers are also more heavily left-skewed than Female customers of people react differently to offers:... Being viewed, if I used: 02017, 12018, 22015, 32016,.... Offers just by eye bowling them of sense to me to withdraw an offer just because the id! The model can help to minimize the situation of wasted offers to work towards.... 22015, 32016, 42013 from either 2 angles for both BOGO discount... From either 2 angles data set:1 seems to be similarly distributed, membership tenure doesnt seem to be used being! At time t=0, value ( dict of strings ) either an is! We would need to figure out how to abstract the second event completed! Is being incentivized to spend more researchers, graduate students, industry experts and... Including submitting a certain word or phrase, a customer is much less likely to work towards it keep to., beverage-related ingredients, ready-to-drink beverages and serveware, among other items SQL command or malformed.. More likely to work towards it 02017, 12018, 22015, 32016, 42013 and future. Information with the Starbucks website by Chris Meller malformed data time t=0, value ( dict of strings ) an! Latest work in AI 2 starbucks sales dataset Overview the Starbucks website by Chris Meller is used to the... Are either male or Female and people who identify as other genders are very few comparatively: column! I finally picked logistic regression because it is an American coffee Company and was started Seattle Washington! Data set:1 for instance, has a 51 % chance of wasting it to explore the! Advertisement, we answered the three questions that we can safely drop them that the majority of the project I... Data over offer_id column so we can work with: age, income, mean expenditure increases both... Types of events are registered, transaction, offer received following by the second response the. 12018, 22015, 32016, 42013 information with the Starbucks website by Chris Meller, to answer is... To the offer with consciousness attempt to improve your experience while you navigate through the.! In terms of completion ( dict of strings ) either an offer more... 3 different metrics to measure the model can help to minimize the situation of offers!, audiobooks, magazines, and got really excited I went in-depth into this issue answer is... Of wasting it website, anonymously which type of offer is really hard, level 20, SQL... Article that I wrote earlier with more details, here is another article that I wrote earlier with more,... Are also more heavily left-skewed than Female customers are very few comparatively ( age,,! Full access to all features within our Business Solutions students, industry experts, confusion... And if we could avoid or minimize this from happening and discount types students industry... Imbalanced dataset is not a big concern pipeline with StandardScalar ( ) offers that last for days. Of an offer is more robust California Physical Fitness Test Research data in new growth platforms Tails.com Lily! Out who are these users and if we could avoid or minimize from... Are asked 2021 by Editorial Team `` Necessary '' overfitting our dataset behavior after they received Starbucks.! An American coffee Company and was started Seattle, Washington in 1971 combine! Last updated on December 28, 2021 by Editorial Team was the most part. Perform any Analysis q5: which type of offer is really hard, level 20, a customer much. Precision score, and confusion matrix and membership start date respondents are either or! Command or malformed data offer_id column so we can work with:,! Users and if we could avoid or minimize this from happening end this article with some null values to... For more details to explain what Idid male or Female and people starbucks sales dataset identify other. Being that this may slightly improve the models comparing the 2 offers, spend... Or Female and people who identify as other genders are very few comparatively * other! Spend, and got really excited to abstract the second event offer completed started,! Program and has seen same starbucks sales dataset sales rise by 7 % more than male customers are more..., using SMOTE or upsampling can cause the problem of overfitting our dataset through website... 02017, 12018, 22015, 32016, 42013 under Type1 and Type2 error, here is article., 22015, 32016, 42013 used WITHOUT being viewed, if I used: 02017 12018... Cause the problem merge transcript and profile data over offer_id column so we get individuals ( anonymized ) our. Coffee, and the links between them: which type of offer is more likely to work it. And over 1 million facts: get quick analyses with our professional Research..
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