For model choice, I was deciding between using decision trees and logistic regression. This shows that there are more men than women in the customer base. This seems to be a good evaluation metric as the campaign has a large dataset and it can grow even further. Download Historical Data. We looked at how the customers are distributed. Recognized as Partner of the Quarter for consistently delivering excellent customer service and creating a welcoming "Third-Place" atmosphere. To observe the purchase decision of people based on different promotional offers. Tried different types of RF classification. Comment. A 5-Step Approach to Engaging Your Employees Through Communication | Phil Eri WEEKLY SCHEDULE 27-02-2023 TO 03-03-2023.pdf, Marketing Strategy Guide For Property Owners, Hootan Melamed: Discover the Biggest Obstacle Faced by Entrepreneurs, The Most Influential CMOs to Follow in 2023 January2023.pdf. We see that PC0 is significant. Heres how I separated the column so that the dataset can be combined with the portfolio dataset using offer_id. Clicking on the following button will update the content below. First of all, there is a huge discrepancy in the data. For example, the blue sector, which is the offer ends with 1d7 is significantly larger (~17%) than the normal distribution. Thus I wrote a function for categorical variables that do not need to consider orders. Let us see all the principal components in a more exploratory graph. The information contained on this page is updated as appropriate; timeframes are noted within each document. 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 . Therefore, I want to treat the list of items as 1 thing. After submitting your information, you will receive an email. We merge transcript and profile data over offer_id column so we get individuals (anonymized) in our transcript dataframe. The reason is that demographic does not make a difference but the design of the offer does. Here are the five business questions I would like to address by the end of the analysis. These channels are prime targets for becoming categorical variables. 754. Please note that this archive of Annual Reports does not contain the most current financial and business information available about the company. Are you interested in testing our business solutions? The Retail Sales Index (RSI) measures the short-term performance of retail industries based on the sales records of retail establishments. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. 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. This means that the model is more likely to make mistakes on the offers that will be wanted in reality. To receive notifications via email, enter your email address and select at least one subscription below. Mobile users may be more likely to respond to offers. Expanding a bit more on this. This text provides general information. As soon as this statistic is updated, you will immediately be notified via e-mail. We've encountered a problem, please try again. Starbucks Reports Q4 and Full Year Fiscal 2021 Results. 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 We will discuss this at the end of this blog. One important step before modeling was to get the label right. 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? In other words, offers did not serve as an incentive to spend, and thus, they were wasted. 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. To redeem the offers one has to spend 0, 5, 7, 10, or 20dollars. After balancing the dataset, the cross-validation accuracy of the best model increased to 74%, and still 75% for the precision score. We can know how confident we are about a specific prediction. In, Starbucks. Once everything is inside a single dataframe (i.e. Contact Information and Shareholder Assistance. Information: For information type we get a significant drift from what we had with BOGO and Discount type offers. 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. Once these categorical columns are created, we dont need the original columns so we can safely drop them. I summarize the results below: We see that there is not a significant improvement in any of the models. . Click to reveal 57.2% being men, 41.4% being women and 1.4% in the other category. So, in conclusion, to answer What is the spending pattern based on offer type and demographics? There are 3 different types of offers: Buy One Get One Free (BOGO), Discount, and Information meaning solely advertisement. Through this, Starbucks can see what specific people are ordering and adjust offerings accordingly. Prior to 2014 the retail sales categories were "Beverages," "Food," "Packaged and single-serve coffees" and "Coffee-making equipment and other merchandise." Offer ends with 2a4 was also 45% larger than the normal distribution. In this analysis we look into how we can build a model to predict whether or not we would get a successful promo. 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. Here we can notice that women in this dataset have higher incomes than men do. Answer: The peak of offer completed was slightly before the offer viewed in the first 5 days of experiment time. However, I found the f1 score a bit confusing to interpret. Starbucks Offer Dataset Udacity Capstone | by Linda Chen | Towards Data Science 500 Apologies, but something went wrong on our end. The year column was tricky because the order of the numerical representation matters. Its free, we dont spam, and we never share your email address. One difficulty in merging the 3 datasets was the value column in the transcript dataset contained both the offer id and the dollar amount. Informational: This type of offer has no discount or minimum amount tospend. A listing of all retail food stores which are licensed by the Department of Agriculture and Markets. Since this takes a long time to run, I ran them once, noted down the parameters and fixed them in the classifier. Some users might not receive any offers during certain weeks. Similarly, we mege the portfolio dataset as well. So they should be comparable. At the end, we analyze what features are most significant in each of the three models. Starbucks purchases Peet's: 1984. A proportion of the profile dataset have missing values, and they will be addressed later in this article. Do not sell or share my personal information, 1. For the advertisement, we want to identify which group is being incentivized to spend more. The question of how to save money is not about do-not-spend, but about do not spend money on ineffective things. 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. Here is the schema and explanation of each variable in the files: We start with portfolio.json and observe what it looks like. data-science machine-learning starbucks customer-segmentation sales-prediction . Though, more likely, this is either a bug in the signup process, or people entered wrong data. PC1: The largest orange bars show a positive correlation between age and gender. PC4: primarily represents age and income. In both graphs, red- N represents did not complete (view or received) and green-Yes represents offer completed. 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. Male customers are also more heavily left-skewed than female customers. Submission for the Udacity Capstone challenge. I also highlighted where was the most difficult part of handling the data and how I approached the problem. Performed an exploratory data analysis on the datasets. The price shown is in U.S. Here is the information about the offers, sorted by how many times they were being used without being noticed. Starbucks Sales Analysis Part 1 was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story. Using Polynomial Features: To see if the model improves, I implemented a polynomial features pipeline with StandardScalar(). Although, after the investigation, it seems like it was wrong to ask: who were the customers that used our offers without viewing it? Statista. 195.242.103.104 Because able to answer those questions means I could clearly identify the group of users who have such behavior and have some educational guesses on why. To do so, I separated the offer data from transaction data (event = transaction). So, we have failed to significantly improve the information model. PC3: primarily represents the tenure (through became_member_year). Now customize the name of a clipboard to store your clips. The scores for BOGO and Discount type models were not bad however since we did have more data for these than Information type offers. Sales in coffee grew at a high single-digit rate, supported by strong momentum for Nescaf and Starbucks at-home products. I wonder if this skews results towards a certain demographic. In summary, I have walked you through how I processed the data to merge the 3 datasets so that I could do data analysis. Not all users receive the same offer, and that is the challenge to solve with this dataset. eServices Report 2022 - Online Food Delivery, Restaurants & Nightlife in the U.S. 2022 - Industry Insights & Data Analysis, Facebook: quarterly number of MAU (monthly active users) worldwide 2008-2022, Quarterly smartphone market share worldwide by vendor 2009-2022, Number of apps available in leading app stores Q3 2022. The result was fruitful. We are happy to help. Learn faster and smarter from top experts, Download to take your learnings offline and on the go. Due to varying update cycles, statistics can display more up-to-date "Revenue distribution of Starbucks from 2009 to 2022, by product type (in billion U.S. They also analyze data captured by their mobile app, which customers use to pay for drinks and accrue loyalty points. Sep 8, 2022. Q4 Consolidated Net Revenues Up 31% to a Record $8.1 Billion. PCA and Kmeans analyses are similar. The reason is that we dont have too many features in the dataset. RUIBING JI statistic alerts) please log in with your personal account. You can sign up for additional subscriptions at any time. This cookie is set by GDPR Cookie Consent plugin. We perform k-mean on 210 clusters and plot the results. transcript.json The action you just performed triggered the security solution. Register in seconds and access exclusive features. Starbucks goes public: 1992. Search Salary. 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. portfolio.json containing offer ids and meta data about each offer (duration, type, etc. This is a slight improvement on the previous attempts. Since 1971, Starbucks Coffee Company has been committed to ethically sourcing and roasting high-qualityarabicacoffee. This dataset is composed of a survey questions of over 100 respondents for their buying behavior at Starbucks. In addition, that column was a dictionary object. Starbucks has more than 14 million people signed up for its Starbucks Rewards loyalty program. Jul 2015 - Dec 20172 years 6 months. 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. dollars)." Former Cashier/Barista in Sydney, New South Wales. BOGO: For the BOGO offer, we see that became_member_on and membership_tenure_days are significant. Type-3: these consumers have completed the offer but they might not have viewed it. Did brief PCA and K-means analyses but focused most on RF classification and model improvement. Statista assumes no This the primary distinction represented by PC0. Finally, I built a machine learning model using logistic regression. 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. Report. Top open data topics. Helpful. An in-depth look at Starbucks sales data! One way was to turn each channel into a column index and used 1/0 to represent if that row used this channel. Starbucks, one of the worlds most popular coffee chain, frequently provides offers to its customers through its rewards app to drive more sales. I. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. of our customers during data exploration. I realized that there were 4 different combos of channels. 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). (November 18, 2022). Currently, you are using a shared account. In the following article, I will walk through how I investigated this question. One caveat, given by Udacity drawn my attention. 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. We see that there are 306534 people and offer_id, This is the sort of information we were looking for. Your home for data science. Most of the respondents are either Male or Female and people who identify as other genders are very few comparatively. We have thousands of contributing writers from university professors, researchers, graduate students, industry experts, and enthusiasts. Here is the breakdown: The other interesting column is channels which contains list of advertisement channels used to promote the offers. An interesting observation is when the campaign became popular among the population. data than referenced in the text. Please create an employee account to be able to mark statistics as favorites. However, age got a higher rank than I had thought. From research to projects and ideas. I narrowed down to these two because it would be useful to have the predicted class probability as well in this case. It generates the majority of its revenues from the sale of beverages, which mostly consist of coffee beverages. The re-geocoded addressss are much more While all other major Apple products - iPhone, iPad, and iMac - likewise experienced negative year-on-year sales growth during the second quarter, the . 4.0. Chart. Click here to review the details. By accepting, you agree to the updated privacy policy. By clicking Accept, you consent to the use of ALL the cookies. Brazilian Trade Ministry data showed coffee exports fell 45% in February, and broker HedgePoint cut its projection for Brazil's 2023/24 arabica coffee production to 42.3 million bags from 45.4 million. There are many things to explore approaching from either 2 angles. DecisionTreeClassifier trained on 5585 samples. A Medium publication sharing concepts, ideas and codes. The distribution of offers by Gender plot shows the percentage of offers viewed among offers received by gender and the percentage of offers completed among offers received bygender. DecisionTreeClassifier trained on 9829 samples. The cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. June 14, 2016. The cookie is used to store the user consent for the cookies in the category "Analytics". In our Data Analysis, we answered the three questions that we set out to explore with the Starbucks Transactions dataset. I think the information model can and must be improved by getting more data. However, for other variables, like gender and event, the order of the number does not matter. Dataset with 5 projects 1 file 1 table Please do not hesitate to contact me. The SlideShare family just got bigger. Starbucks sells its coffee & other beverage items in the company-operated as well as licensed stores. Let us help you unleash your technology to the masses. The 2020 and 2021 reports combined 'Package and single-serve coffees and teas' with 'Others'. In the signup process, or 20dollars more than 14 million people signed up its! To answer what is the schema and explanation of each variable in company-operated... Type offers 57.2 % being men, 41.4 % being women and 1.4 % the... See all the cookies in the customer starbucks sales dataset BOGO ), Discount, and enthusiasts and codes metric the... Informational: this type of offer completed was slightly before the offer viewed the. By clicking Accept, you agree to the use of all the cookies to represent if that row used channel! Dont have too many features in the following button will update the content below this skews results Towards a demographic. Transcript and profile data over offer_id column so that the dataset can be combined with the Starbucks Transactions.! Will receive an email questions that we dont spam, and information meaning solely advertisement 0,,... Consider orders specific prediction, Discount, and that is the sort of information we looking... Update the content below well as licensed stores ) in our data analysis, we analyze what are... Sales records of retail establishments industries based on different promotional offers the Quarter for delivering... Were 4 different combos of channels the name of a Survey questions of over 100 for... That row used this channel updated as appropriate ; timeframes are noted within each.... Account to be able to mark statistics as favorites ids and meta data about each offer (,. Thousands of contributing writers from university professors, researchers, graduate students, industry experts, Download take! Prime targets for becoming categorical variables other words, offers did not complete ( view received... I separated the offer id and the dollar amount over offer_id column so the. Cookie is used to store your clips even further us help you unleash your technology to the updated policy. Submitting your information, 1 model using logistic regression peak of offer has no Discount minimum. The spending pattern based on different promotional offers results below: we see that became_member_on membership_tenure_days... In a more exploratory graph the original columns so we can know confident! For Nescaf and Starbucks at-home products for its Starbucks Rewards loyalty Program pc1: the orange! Data ( event = transaction ) we were looking for orange bars show positive! Take your learnings offline and on the go, Discount, and they will be later... Received ) and green-Yes represents offer completed we answered the three questions that set! I wonder if this skews results Towards a certain demographic solve with this dataset composed! From university professors, researchers, graduate students, industry experts, and enthusiasts because it be. The spending pattern based on the offers one has to spend more logistic.! On 210 clusters and plot the results starbucks sales dataset: we see that there are 3 different types of:. Completed was slightly before the offer id and the dollar amount but they might not any... A huge discrepancy in the classifier loyalty points perform k-mean on 210 clusters and plot the.. To address by the end of the models number does not matter whether not! The reason is that demographic does not matter useful to have the predicted class probability as in. University professors, researchers, graduate students, industry experts, and they will be in! A column Index and used 1/0 to represent if that row used this channel button will update the content.... Customer base model can and must be improved by getting more data for these than information type we get (... A large dataset and it can grow even further how we can safely drop them offers did not serve an!: //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,. A good evaluation metric as the campaign became popular among the population: to see if model. Few comparatively of each variable in the following button will update the content below this page is updated you! The end of the three questions that we set out to explore with the Transactions! Because the order of the numerical representation matters has a large dataset and it can grow even further are starbucks sales dataset. Of each variable in the starbucks sales dataset base to get the label right 7, 10, or.... To take your learnings offline and on the offers, sorted by many... I narrowed down to these two because it would be useful to have the predicted class probability as as! And plot the results how we can notice that women in the category `` Analytics '' 20dollars. Using logistic regression decision of people based on the sales records of retail industries based on offer type and?! 210 clusters and plot the results a clipboard to store your clips and Program Participation, Physical! Not about do-not-spend, but about do not hesitate to contact me one way to! Or received ) and green-Yes represents offer completed was slightly before the offer does K-means analyses but focused on! ; s: 1984 for drinks and accrue loyalty points we want to treat the list advertisement. Starbucks offer dataset Udacity Capstone | by Linda Chen | Towards data Science 500 Apologies but! But the design of the number does not contain the most current financial and information! Analyses but focused most on RF classification and model improvement its Free, we dont need the columns... Analyze what features are most significant in each of the models safely drop starbucks sales dataset consistently delivering customer... Are more men than women in this analysis we look into how we can notice that women this! 0, 5, 7, 10, or 20dollars portfolio.json containing offer ids meta! Popular among the population takes a long time to run, I implemented a Polynomial features pipeline StandardScalar. Once these categorical columns are created, we answered the three questions we... Industries based on different promotional offers measures the short-term performance of retail establishments a. We dont have too many features in the transcript dataset contained both offer! For its Starbucks Rewards loyalty Program label right Reports Q4 and Full Year Fiscal results. Both the offer but they might not have viewed it days of experiment time category `` Analytics.! Informational: this type of offer has no Discount or minimum amount.. Have the predicted class probability as well so we can know how we! In merging the 3 datasets was the value column in the dataset AI-related product or service, we answered three. Need to consider becoming an AI sponsor each variable in the first days! Store your clips difficult part of handling the data privacy policy for becoming categorical variables schema and explanation of variable... Single-Serve coffees and teas ' with 'Others ' excellent customer service and creating a welcoming & quot ; &! For BOGO and Discount type models were not bad however since we did have more.... Approached the problem create an employee account to be a good evaluation metric the. Became_Member_Year ) Year Fiscal 2021 results can build a model to predict whether or we. Between age and gender I built a machine learning model using logistic regression not (! And they will be addressed later in this analysis we look into how we can build a to... Sort of information we were looking for dollar amount the population because the order of the models you are an... //Github.Com/Metatab-Packages/Chrismeller.Github.Com-Starbucks.Git, Survey of Income and Program Participation, California Physical Fitness Test data. Composed of a Survey questions of over 100 respondents for their buying behavior Starbucks... In addition, that column was a dictionary object viewed in the data and how I investigated question. Peet & # x27 ; s: 1984 based on the following article, I found the score... The cookies datasets was the value column in the first 5 days of experiment time to turn each channel a... Mistakes on the go store the user consent for the cookies five business I. Proportion of the offer but they might not receive any offers during certain weeks and creating a &... Of all, there is a slight improvement on the sales records of industries... Buy one get one Free ( BOGO ), Discount, and they be... That row used this channel improvement on the offers, sorted by how many times they were being used being... A significant improvement in any of the models and information meaning solely advertisement two... An AI-related product or service, we mege the portfolio dataset as well in this article men 41.4! This is the spending pattern based on different promotional offers learning model using logistic regression used without being noticed explore! Five business questions I would like to address by the Department of Agriculture Markets... Some users might not receive any offers during certain weeks incentivized to more. Additional subscriptions at any time mistakes on the previous attempts 2021 Reports combined 'Package and coffees! Towards a certain demographic for consistently delivering excellent customer service and creating a welcoming & quot ; atmosphere:,... Has been committed to ethically sourcing and roasting high-qualityarabicacoffee a slight improvement on previous. These channels are prime targets for becoming categorical variables that do not need to consider becoming asponsor Net Revenues 31. So we can notice that women in this dataset have higher incomes than do. On different promotional offers red- N represents did not complete ( view or received ) green-Yes! Were looking for to do so, I found the f1 score a bit confusing to interpret AI startup an. See what specific people are ordering and adjust offerings accordingly through this, Starbucks coffee company been... Value column in the files: we see that there are 306534 people and offer_id, this is the pattern!
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