{"id":34486,"date":"2018-11-13T18:33:08","date_gmt":"2018-11-13T23:33:08","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/pinterest-needs-to-improve-revenue-per-user-ahead-of-ipo\/"},"modified":"2018-11-13T23:51:30","modified_gmt":"2018-11-14T04:51:30","slug":"pinterest-needs-to-improve-revenue-per-user-ahead-of-ipo","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/pinterest-needs-to-improve-revenue-per-user-ahead-of-ipo\/","title":{"rendered":"Pinterest Needs to Improve Revenue Per User Ahead of IPO"},"content":{"rendered":"

Machine learning is imperative to Pinterest\u2019s business model and is an important area of focus as the company prepares to go public next year at a valuation of up to $15 billion1<\/sup>. This valuation reflects strong user growth of almost 15 percent last year, which is significantly higher than that of Twitter and Facebook who each had 9% over the same period2,3<\/sup>. While the Pinterest\u2019s user growth is impressive, their revenue per user lags that of their peers at just $5, compared to $8 for Twitter and $25 for Facebook4,5,6<\/sup>.<\/p>\n

It is imperative for Pinterest to grow revenue per user given their relatively narrow target user base of predominately women aged 25 \u2013 54, 60% of whom are mothers7<\/sup>. Further, revenue per user growth is one of the main valuation and performance metrics used by technology investors8<\/sup>, making it even more important as Pinterest looks to attract public shareholders. In this essay, I will propose new ways to use Pinterest\u2019s existing machine learning infrastructure to increase revenue per user.<\/p>\n

 <\/p>\n

How Pinterest Works<\/u><\/strong><\/p>\n

Pinterest has a vast userbase of 200+ million unique users9<\/sup> and processes 150 million image searches per month10<\/sup>. Pinterest users curate \u201cpins\u201d by selecting images from across the web and saving them into different collections, called \u201cboards\u201d. These pins maintain their original website links when they are \u201crepined\u201d by different users, effectively allowing users to share images and idea sources with one another11<\/sup>. Pinterest earns revenue from retailers who pay to have their pins prioritized on a user\u2019s stream through \u201cpromoted pins\u201d12<\/sup>.<\/p>\n

Exhibit 1: How A Link is Saved Through Pinterest13<\/sup><\/p>\n


\n\"\"<\/a><\/p>\n

 <\/p>\n

How Pinterest Uses Machine Learning<\/u><\/strong><\/p>\n

Pinterest uses GPUs (Graphic Processing Units) to analyze and draw inferences from information within pinned images, including image characteristics (color, quality, etc.) as well as metrics about the source website and the profiles of the Pinterest users interacting with the pin. These relationships are used to segment users by interests, frequency of actions and sequence of actions, and ultimately to predict user behavior. Given the amount of data that Pinterest has, they can now predict if a user can will interact with a new pin with 77% – 85% accuracy14<\/sup>, giving them a differentiated advantage in recommending new content for their userbase.<\/p>\n

Currently, Pinterest excels at recommending pins similar <\/em>to ones previously saved by each user but hopes to improve the ability to identify complementary<\/em> information related to user searches15<\/sup>. Recently, Pinterest developed Pinterest Lens, which allows users to take a photo with their smartphone and then uses machine learning to attempt to source related items16<\/sup>. This is how Pinterest uses machine learning to differentiate themselves and improve their product: by learning about a user\u2019s interests and then predicting helpful and relevant information before the user even knows what they are looking for.<\/p>\n

 <\/p>\n

How Pinterest Can Use Machine Learning to Improve Revenue Per User <\/u><\/strong><\/p>\n

As described above, Pinterest uses machine learning to both segment users into target groups and predict their behavior with a high level of accuracy. However, the company is not using this information to optimize their clients\u2019 promoted pins and resulting revenue. Currently, the majority of Pinterest purchases are originally discovered through the boards of strangers or friends, versus just 7% sourced through retailer\u2019s promoted pins. Further, only 19% of Pinterest-driven purchases involved a sale or promotion sourced through Pinterest, compared to 37% of Facebook and 32% of Twitter-driven purchases17<\/sup>.<\/p>\n

These shortcomings highlight a significant revenue growth opportunity if Pinterest can use their existing machine learning infrastructure to better match promoted pins to target markets. Specifically, I believe that Pinterest should segment users into two main categories: frugal, price-conscious purchasers and trend-conscious buyers who are willing to pay full price. Below is a table describing these two representative buyers, with suggested promoted pin strategies for each:<\/p>\n

\"\"<\/a>\u00a0
\n<\/a><\/p>\n

The key to this strategy in predicting the buying behavior of each user, profiling them based on how<\/em> they make purchases, rather than what <\/em>they purchase. This allows retailers to get the most revenue from each segment. They will close more deals with price-conscious buyers by alerting them to sales, without unnecessarily giving up revenue by showing discounts to consumers who are willing to buy premium or newer items at higher prices. If Pinterest can successfully segment and target these two groups, they will achieve stronger revenue per user growth to amplify their already impressive userbase statistics and business model.<\/p>\n

Key Questions:<\/strong><\/span><\/p>\n

How specific should Pinterest be with segmenting? Is there a threat to retailers by offering a price comparison tool within the app as suggested above?<\/p>\n

 <\/p>\n

Word Count: 711<\/p>\n

 <\/p>\n

References<\/p>\n

    \n
  1. Castillo, M. (2018). Pinterest nears $1 billion in ad sales and valuation rises as it looks to go public in mid-2019. CNBC. <\/em>Retrieved from http:\/\/www.cnbc.com<\/a><\/u>.<\/li>\n
  2. Twitter, Inc. (NYSE: TWTR) (2018). Twitter Third Quarter 2018 Results. Twitter, Inc. <\/em>Retrieved from EDGAR.com<\/u><\/li>\n
  3. Facebook, Inc. (NASDAQ: FB) (2018). Facebook Third Quarter 2018 Results. Facebook, Inc. <\/em>Retrieved from EDGAR.com<\/u><\/li>\n
  4. Laughlin, L. (2018). Breakingviews – Pinterest shapes up for anti-millennial IPO. Reuters<\/em>. August 8, 2018<\/li>\n
  5. Twitter, Inc. (NYSE: TWTR) (2018). Twitter Third Quarter 2018 Results. Twitter, Inc. <\/em>Retrieved from EDGAR.com<\/u><\/li>\n
  6. Facebook, Inc. (NASDAQ: FB) (2018). Facebook Third Quarter 2018 Results. Facebook, Inc. <\/em>Retrieved from EDGAR.com<\/u><\/li>\n
  7. Laughlin, L. (2018). Breakingviews – Pinterest shapes up for anti-millennial IPO. Reuters<\/em>. August 8, 2018<\/li>\n
  8. Louis, T. (2013). How Much Is A User Worth?. Forbes<\/em>. Retrieved from http:\/\/www.Forbes.com<\/a>.<\/li>\n
  9. Castillo, M. (2018). Pinterest nears $1 billion in ad sales and valuation rises as it looks to go public in mid-2019. CNBC. <\/em>Retrieved from http:\/\/www.cnbc.com<\/a><\/u>.<\/li>\n
  10. Melendez, S. (2016). How Pinterest Uses Machine Learning To Keep Its Users Pinned. Fast Company. <\/em>December 2016 \/ January 2017, p.p. 40 \u2013 41.<\/li>\n
  11. Zhong, C., Karamshuk, D., & Sastry, N. (2015). Predicting Pinterest: Automating a Distributed Human Computation.\u00a0Proceedings of the 24th International Conference on World Wide Web,<\/em>1417-1426.<\/li>\n
  12. Wagner, K. (2017). Pinterest expects to make more than $500 million in revenue this year.\u00a0Recode.<\/em>Retrieved from http:\/\/www.recode.com<\/a><\/li>\n
  13. Text and graphics modified. Original source: shopify.com. How Pinterest Drives Online Commerce.<\/em>Retrieved from http:\/\/www.shopify.com<\/a><\/li>\n
  14. Martin, J. (2014). Behavioral Segmentation of Pinterest Users. Proceedings of the 3rd Workshop on Data-driven User Behavioral Modeling and Mining from Social Media<\/em>, 2014 (November), 13-14.<\/li>\n
  15. Lee, N. (2018). Pinterest rolls out product recommendations for easier shopping. Engadget. <\/em>Retrieved from http:\/\/www.engadget.com<\/a>.<\/li>\n
  16. Wagner, K. (2017). Pinterest expects to make more than $500 million in revenue this year.\u00a0Recode.<\/em>Retrieved from http:\/\/www.recode.com<\/a><\/li>\n
  17. Sevitt, D., & Samuel, A. (2013). How Pinterest Puts People in Stores. 性视界 Business Review, 91<\/em>(7\/8), p.p. 26\u201327.<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

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