Whats All the Buzz?

Blog Post Number 3, Big Data Case Study - Buzzfeed

"Whats all the Buzz?"

Popular news and entertainment social sharing site BuzzFeed earns its large traffic by publishing viral content. BuzzFeed’s team identifies trending stories and their unique characteristics in order to duplicate success in the future. For example, photos of food are popular, as are photos with guns, and the color red and women in bikinis tend to rank higher with traffic than others.
“We use two methodologies to power our analysis, identify characteristics with predictive relationship to virality: quantitative and descriptive,” Harlin said. “We have to understand how the spread of content differs by social network.” “Machine learning predicts social hits: we know what’s viral before it takes off. Regression analysis and machine learning are approaches for data analysis,” he said. However, BuzzFeed's innovation doesn't have anything to do with the content is posted, it's about how that content is tracked across the multiple sites, platforms, and services through which it is distributed. More than anything else, BuzzFeed is the prime example for the multi-platform approach to media, as much as 75% of the site's output never appears on its website at all.

How is data actually used through BuzzFeed?

Data drives BuzzFeed’s content strategy. BuzzFeed has mastered being able to predict what articles will go viral on the internet. BuzzFeed now covers news, politics, business, tech, entertainment, food, international coverage, and much more, reaching over 150 million unique visitors a month.

The key takeaways are that data can be used to optimize content for sharing through the life of every article.

Before publishing, data can help determine what to write about and how to present it.

Before an article is posted, data can helps to answer questions like what to write about. There are all these APIs out there on sites such as Facebook, Twitter, or Google. So BuzzFeed can identify things people care about and figure out how these topics are already being covered. Looking at historical data  BuzzFeed can determine characteristics of a good headline, a good thumbnail, good length for a post.

After publishing optimizing how to promote the content is also important

For content, BuzzFeed can tell within an hour or so of publishing what type of stuff we should put prominently on the home page, promote on Twitter, etc. That’s where data science can be really useful.

They also use it to train their models. The way something goes viral on Facebook now versus how it went viral before tells us what to change. We’ve gotten really good at predicting what the 10 biggest posts will be on a given day. The one thing that is hard to predict is the magnitutde.

One of other things we do is try to make data accessible to our writers—give them feedback on how content is doing in a consistent and regular way. That way, we see the same things, and can try to figure out why does well on certain platforms and things like that.

What is the problem, if any is plenty of news. We believe in mobile. People don’t engage on mobile news as much as on desktops.In order to bridge the barrier of time, Circa technologists use Big Data to serve bite-sized chunks of only new news that readers are following, instead of expecting story followers to re-read content they have already consumed. Each atomized chunk is called a card.

“We strive to present each article as atomic content. It’s also context. Here is how we construct mobile news on Circa. We don’t use auto summarization; we have actual journalists.” Metrics are an important element for journalists. “You have to decide how much to give to journalists. You want to give them a nice balance of analytics that matter most to you: users, pages, sources, editorial statistics, story sharing. The metrics that mater include explicit and implicit user behavior,” he said. “We tend to look at follows and shares more than anything else.” BuzzFeed employees urge publishers to re-think their content, change the way to re-formulate it, and make it more efficient and dynamic. Big data can be used to assist in streamlining news feeds, as well as creating actionable audience metrics for your team. In a nutshell, BuzzFeed has been building a giant engine for understanding content flows and sharing behavior online, thanks to tools like Pound and The Hive—a  huge, real-time database that react-text can ingest and track BuzzFeed content wherever it is shared across multiple platforms. In summary we see how data helps determine what to write and how to promote the content. However data has yet to tell us why people share some articles versus others. That is where quantitative shifts to qualitative  Through reading comments and reading articles about articles BuzzFeed can learn where they went wrong and right.  

http://reutersinstitute.politics.ox.ac.uk/sites/default/files/Big%20Data%20For%20Media_0.pdf

Thats a Wrap


Data is strongly correlated to fashion and brings multiple components of a complex industry together. It is really cool to see the big picture through data and learn how and why consumers prefer the clothing they do. Through big data, there is no guessing, merchandisers know exactly what will sell and where without producing more products than are necessary for a certain target market. Big Data is becoming an important part of one of the most intuition-based and unpredictable industry. In a world where clothes become outdated with the release of a new movie or the latest fashion week, even companies like Burberry and Ralph Lauren have resorted to Big Data analysis. The runway at the fashion week, the latest edition of Cosmopolitan — are all losing their charm; designers these days release photos of their exclusive collections on Social Media (Facebook, Twitter, Instagram, Pinterest etc) which helps them know the trends and people’s response before a show. Likes shares and comments are also a form of data that companies use to help understand which clothing is most popular at the time. Not only sales, marketing and advertising become effective too. As of now, we have little idea of how well our billboards are working – but come in action Google Glass, we will be able to collect data on human gaze and hence the popularity of the brand in that area. Also, brands like Burberry are using Big Data to improve its Social Media presence and Customer Engagement.

Data also helps companies to optimize their supply chains so they can now decide what to produce more and should be stocked in inventory while what can be kept for made-to-order or Just-in-Time. Big Data cannot ever entirely redefine fashion industry as it is more of art, innovation and creativity than science and numbers, it definitely is modernizing the way industrialists and brands produce apparels and accessories.


http://bigdata-madesimple.com/forecasting-the-fashion-future-big-data-comes-to-rescue-fashion-designers/

Fashion & Big Data

Big data can explain multiple components in the fashion industry. The article states how “Big data explains when customers make their purchases, how large their purchases are and how many items are sold.” This data can help companies and designers decide which items are absolutely necessary to sell in their line and which ones will do well. Another component big data aids in is pricing within items. Clothing needs to be priced accordingly so that customers will actually buy the items. Items cannot be priced too high because then customers will not make any purchases. Big data is easy to average, and the average price can be placed on garments to make sales easier to complete. Items that are priced for sale to the general population must be reduced greatly after their conversion, and designers may use big data to price their products, and each product may take on a price that is derived from the previous year's data. Big data can also uncover new product categories. Big data shows which products will be successful and which such be avoided. There are several different ways a fashion house can expand its offerings, and the data you collect will help you remain competitive in the fashion industry. Your business must release products that are most likely to sell, but you cannot create any product without first consulting the data you have collected. 
The fashion industry is changing as creative designers use analytics to operate their businesses, and each step in this article will help guide your business. Selectively use your data to create and convert product lines your customers are sure to buy in the future. Data can also help predict the future, Analysis of data over time can help designers and retailers predict upcoming trends. It is important for designers to have reasonably accurate forecasts of pending trends so that they do not design for a trend that does not come to fruition. Production paired with insightful business analytics, enables successful designers to appear prescient with their new lines. They can even foresee how desirable certain pieces will be. Gaining data helps to define customer interaction with various different brands and helps retailers create better pricing models, markdown schedules, inventory planning, and seasonal availability. Data and the fashion industry are strongly correlated and data contributes to consumers buying behavior and patterns. Through the analyzation of this data designers and retailers can make more sales and create better revenue. 

   

https://channels.theinnovationenterprise.com/articles/8230-big-data-hits-the-runway-how-big-data-is-changing-the-fashion-industry.