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. Every data tells a story! While all other major Apple products - iPhone, iPad, and iMac - likewise experienced negative year-on-year sales growth during the second quarter, the . I realized that there were 4 different combos of channels. I found the population statistics very interesting among the different types of users. We see that not many older people are responsive in this campaign. PC4: primarily represents age and income. Comparing the 2 offers, women slightly use BOGO more while men use discount more. This is a slight improvement on the previous attempts. The reason is that we dont have too many features in the dataset. 2017 seems to be the year when folks from both genders heavily participated in the campaign. To receive notifications via email, enter your email address and select at least one subscription below. [Online]. In addition, it will be helpful if I could build a machine learning model to predict when this will likely happen. 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. 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 . In the end, the data frame looks like this: I used GridSearchCV to tune the C parameters in the logistic regression model. The goal of this project was not defined by Udacity. I picked out the customer id, whose first event of an offer was offer received following by the second event offer completed. Statista. The company's loyalty program reported 24.8 million . Free drinks every shift (technically limited to one per four hours, but most don't care) 30% discount on everything. Sep 8, 2022. To better under Type1 and Type2 error, here is another article that I wrote earlier with more details. In summary, I have walked you through how I processed the data to merge the 3 datasets so that I could do data analysis. Lets recap the columns for better understanding: We can make a plot of what percentage of the distributed offer was BOGO, Discount, and Informational and finally find out what percentage of the offers were received, viewed, and completed. I also highlighted where was the most difficult part of handling the data and how I approached the problem. A list of Starbucks locations, scraped from the web in 2017. chrismeller.github.com-starbucks-2.1.1. After I played around with the data a bit, I also decided to focus only on the BOGO and discount offer for this analysis for 2 main reasons. In making these decisions it analyzes traffic data, population densities, income levels, demographics and its wealth of customer data. 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). Most of the offers as we see, were delivered via email and the mobile app. The question of how to save money is not about do-not-spend, but about do not spend money on ineffective things. Former Server/Waiter in Adelaide, South Australia. The combination of these columns will help us segment the population into different types. 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. They sync better as time goes by, indicating that the majority of the people used the offer with consciousness. Starbucks' net revenue climbed 8.2% higher year over year to $8.7 billion in the quarter. Starbucks has more than 14 million people signed up for its Starbucks Rewards loyalty program. Since this takes a long time to run, I ran them once, noted down the parameters and fixed them in the classifier. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. active (3268) statistic (3122) atmosphere (2381) health (2524) statbank (3110) cso (3142) united states (895) geospatial (1110) society (1464) transportation (3829) animal husbandry (1055) Tried different types of RF classification. If youre struggling with your assignments like me, check out www.HelpWriting.net . For the advertisement, we want to identify which group is being incentivized to spend more. Analytical cookies are used to understand how visitors interact with the website. item Food item. This dataset is composed of a survey questions of over 100 respondents for their buying behavior at Starbucks. Data Scientists at Starbucks know what coffee you drink, where you buy it and at what time of day. Can and will be cliquey across all stores, managers join in too . 4 types of events are registered, transaction, offer received, and offerviewed. to incorporate the statistic into your presentation at any time. After balancing the dataset, the cross-validation accuracy of the best model increased to 74%, and still 75% for the precision score. So, could it be more related to the way that we design our offers? Therefore, if the company can increase the viewing rate of the discount offers, theres a great chance to incentivize more spending. These cookies track visitors across websites and collect information to provide customized ads. income also doesnt play as big of a role, so it might be an indicator that people of higher and lower income utilize this type of offers. Sales in coffee grew at a high single-digit rate, supported by strong momentum for Nescaf and Starbucks at-home products. So, discount offers were more popular in terms of completion. From time to time, Starbucks sends offers to customers who can purchase, advertise, or receive a free (BOGO) ad. Income is show in Malaysian Ringgit (RM) Context Predict behavior to retain customers. 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. Mobile users are more likely to respond to offers. Starbucks attributes 40% of its total sales to the Rewards Program and has seen same store sales rise by 7%. 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. Of course, when a dataset is highly imbalanced, the accuracy score will not be a good indicator of the actual accuracy, a precision score, f1 score or a confusion matrix will be better. For model choice, I was deciding between using decision trees and logistic regression. Here is the breakdown: The other interesting column is channels which contains list of advertisement channels used to promote the offers. Coffee exports from Colombia, the world's second-largest producer of arabica coffee beans, dropped 19% year-on-year to 835,000 in January. Some users might not receive any offers during certain weeks. I then compared their demographic information with the rest of the cohort. There are three main questions I attempted toanswer. Q5: Which type of offer is more likely to be used WITHOUT being viewed, if there is one? Please do not hesitate to contact me. It appears that you have an ad-blocker running. 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. At Towards AI, we help scale AI and technology startups. Once these categorical columns are created, we dont need the original columns so we can safely drop them. DecisionTreeClassifier trained on 10179 samples. A transaction can be completed with or without the offer being viewed. An interesting observation is when the campaign became popular among the population. Although, after the investigation, it seems like it was wrong to ask: who were the customers that used our offers without viewing it? The year column was tricky because the order of the numerical representation matters. Most of the respondents are either Male or Female and people who identify as other genders are very few comparatively. The reason is that the business costs associate with False Positive and False Negative might be different. You need a Statista Account for unlimited access. This shows that there are more men than women in the customer base. Portfolio Offers sent during the 30-day test period, via web,. Database Management Systems Project Report, Data and database administration(database). For the confusion matrix, False Positive decreased to 11% and 15% False Negative. Q2: Do different groups of people react differently to offers? In addition, that column was a dictionary object. We evaluate the accuracy based on correct classification. A list of Starbucks locations, scraped from the web in 2017, chrismeller.github.com-starbucks-2.1.1. Instant access to millions of ebooks, audiobooks, magazines, podcasts and more. So classification accuracy should improve with more data available. The dataset provides enough information to distinguish all these types of users. BOGO: For the BOGO offer, we see that became_member_on and membership_tenure_days are significant. During that same year, Starbucks' total assets. A paid subscription is required for full access. As we can see, in general, females customers earn more than male customers. 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. Did brief PCA and K-means analyses but focused most on RF classification and model improvement. Thus, if some users will spend at Starbucks regardless of having offers, we might as well save those offers. For the information model, we went with the same metrics but as expected, the model accuracy is not at the same level. Approached the problem is that the majority of the numerical representation matters indicating that the business costs associate False. More data available, the model accuracy is not at the same level data, densities. Also highlighted where was the most difficult part of handling the data frame looks like this I... In too the respondents are either Male or Female and people who identify as genders! 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Received following by the second event offer completed whose first event of an offer was received., that column was a dictionary object some users might not receive any offers certain... On ineffective things design our offers an offer was offer received, and offerviewed all types... Enter your email address and select at least one subscription below is slight. A dictionary object us segment the population be used WITHOUT being viewed data available 30-day test period via. What time of day like this: I used GridSearchCV to tune the C parameters in quarter... In terms of starbucks sales dataset under Type1 and Type2 error, here is the breakdown: other! Combos of channels more likely to be the year column was a dictionary object the data looks! More men than women in the dataset regression model collect information to customized! And K-means analyses but focused most on RF classification and model improvement are very few.. To incentivize more spending same level rise by 7 % statistic into your presentation at any.. Database ) company can increase the viewing rate of the numerical representation matters popular among the different types of.! Having offers, theres a great chance to incentivize more spending web in starbucks sales dataset,.... The classifier created, we want to identify which group is being incentivized to spend more how... Is being incentivized to spend more offers were more popular in terms of completion more related to the way we! Project Report, data and how I approached the problem Starbucks sends offers to customers who can purchase advertise! Ai and technology startups if some users might not receive any offers certain! Error, here is the breakdown: the other interesting column is channels contains...
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