Data Study on Majestic Wine

Posted on Dec 4, 2019
The skills the author demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

Introduction

Earlier this year, the UK wine company Majestic agreed to sell off its retail stores to Fortress Investment Group for £95 million. The company is shifting its focus to its online subscription service Naked Wines, which it bought for £70 million in 2015. This sale, which is to be completed after Christmas 2019, will free up capital to be directed at Naked Wines and promote the evolution of Majestic towards online retail. Josh Lincoln, the managing director of Majestic, calls it a growing sector where data shows wine sales in the £8 - £15 range are growing faster than cheaper wines.

In light of Majestic's increasing focus on their online presence, I decided to scrape their website for data on pricing and customer satisfaction to see if I could glean anything from their business model.

Data

Data Study on Majestic Wine

Having scraped their website using Selenium, I found initial exploratory data analysis added weight to the managing director's statement about sales in the £8 - £15 range. This density plot shows that Majestic is clearly targeting that market. If we look at the average price of bottles grouped by wine type, rosé is cheaper than red and white wine and, unsurprisingly, dessert and fortified wine is the most expensive of them all.

Price Distribution

It is quite misleading to look at average pricing, though, as it's heavily skewed by a minority of very expensive wines; density plots are more reliable. The following density plot illustrates the price distribution of red and white wines. There is a sharper peak in the proportion of white wines around the £11 or £12 mark than red wines which are slightly more expensive.

Majestic offers its own descriptions of wine styles. As a result I was able to segment them into groups and compare prices to ratings. For example, red wines are split into three groups: Fruity Reds, Smooth Reds and Big Reds. If one judges on price, one would expect the most expensive wines to be reviewed best. Big Reds would then be top-ranked as the bottles are on average £5 more expensive than Smooth Reds. In reality, though, Smooth Reds are the most popular. 

Data Study on Majestic Wine

Data Study on Majestic Wine

Likewise, in the case of white wines, the most expensive Rich White is out-reviewed by the much cheaper Fruity White, suggesting that customers may not possess such sophisticated palettes as those at Majestic in charge of pricing!

One may have noticed, however, that the difference in the measure of customer approval is almost too small to see with the naked eye. This sheds light on Majestic's approach to reviewing which I believe to be a key aspect of their marketing. Instead of using traditional reviewing systems where one might give a rating out of five stars, they merely ask their customers whether or not they would buy the bottle again.

Personally, I am not one to review products, but I'd consider it here because I wouldn't be thinking of it as reviewing for other people but rather leaving a note to myself for my next order.

Cork 

For your average customer, this is the only question they are interested in, and it encourages less fussy types to leave positive reviews rather than a higher proportion of more critical wine drinkers leaving 2 or 3 star reviews. A customer is far more likely to buy a bottle with over 80% of customers saying they would buy it again as compared to one with a mediocre 3 out of 5 stars.  As the star system encourages people to be more critical, Majestic's simpler review system is much better for sales.

Finally, perhaps the most interesting discovery was how much a cork increases the price. Majestic's target market isn't a customer looking for fine wines to store in a cellar for sixty years but looking for wines that are ready to be drunk. So there's no real need for a cork except to pander to those who expect one. But if it gives Majestic extra leeway to hike up prices, then it's a worthwhile pricing strategy.

If you'd like to follow me on LinkedIn or visit my GitHub, I have placed links below:

LinkedIn

GitHub

 

About Author

William Ponsonby

William Ponsonby is a data scientist currently studying at the NYC Data Science Academy. Prior to that he studied Russian, Czech and Slovak at Oxford University and did internships in Investment Analysis, Accounting, Advertising and Self-Storage in London,...
View all posts by William Ponsonby >

Leave a Comment

Seeing stars: Acquiring More Reviews and Sales with Simpler Ratings - TOP WEBINARS January 1, 2020
[…] The solution to overthinking what’s good or not good enough is to adapt the same simple rule of thumb system to wine customers. That’s what the UK-based Majestic Wines did. Instead of assessing gradations of quality, customers merely have to indicate if they would or would not buy that particular variety again. That results in many more bottles receiving a large number of positive reviews. As William Ponsonby wrote in his blog on the site’s data: […]
Seeing stars: Acquiring More Reviews and Sales with Simpler Ratings - eCommerceFastlane.com December 31, 2019
[…] The solution to overthinking what’s good or not good enough is to adapt the same simple rule of thumb system to wine customers. That’s what the UK-based Majestic Wines did. Instead of assessing gradations of quality, customers merely have to indicate if they would or would not buy that particular variety again. That results in many more bottles receiving a large number of positive reviews. As William Ponsonby wrote in his blog on the site’s data: […]
Seeing stars: Acquiring More Reviews and Sales with Simpler Ratings | Post Funnel December 31, 2019
[…] The solution to overthinking what’s good or not good enough is to adapt the same simple rule of thumb system to wine customers. That’s what the UK-based Majestic Wines did. Instead of assessing gradations of quality, customers merely have to indicate if they would or would not buy that particular variety again. That results in many more bottles receiving a large number of positive reviews. As William Ponsonby wrote in his blog on the site’s data: […]

View Posts by Categories


Our Recent Popular Posts


View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI