{"id":31632,"date":"2018-11-13T14:32:19","date_gmt":"2018-11-13T19:32:19","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/pinterests-uses-machine-learning-to-expand-their-efficacy-for-consumers-and-marketers\/"},"modified":"2018-11-13T14:32:19","modified_gmt":"2018-11-13T19:32:19","slug":"pinterest-uses-machine-learning-to-expand-their-offerings-for-consumers-and-marketers","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/pinterest-uses-machine-learning-to-expand-their-offerings-for-consumers-and-marketers\/","title":{"rendered":"Pinterest uses machine learning to expand their offerings for consumers and marketers"},"content":{"rendered":"

Pinterest is a digital platform that is designed to imitate pin boards; a board where someone might put up a picture of a bathroom they want to replicate in their house, a recipe they want to try, a vacation destination they want to go to or an inspirational quote. A simple example of this is if someone goes on Pinterest looking for slow cooker recipes and \u2018pins\u2019 them to their board, Pinterest utilizes machine learning to figure out what are good recommendations for that specific person based on what other people who have had similar pins have done.[1]<\/a> With over 250 million monthly users, Pinterest has a massive amount of data at their fingertips to achieve this.[2]<\/a><\/p>\n

The biggest push for Pinterest, like many of the other large social technology companies, is to justify their current market valuation by showing that they can monetize their users\u2019 data. The long-term play is to become a super-efficient recommendation platform that has highly targeted ads for consumers who have a high level of purchase intent. They believe their users have higher purchase intent because they are using Pinterest to find things they might want to buy or try. However, they are still very much in the process of using machine learning to improve on how effective the ads and recommendations are. One of the big things they\u2019re doing is image recognition, where someone can click on an item in an image and Pinterest will then be able to bring up the exact item or similar ones, hoping the user will click through and buy it. They are trying to really refine this tool by using machine learning to see how many users that click one image then look at similar images, this allows the program to continue to improve its image recognition.[3]<\/a> This is a significant component of how targeted their advertising can get and thus allow them to be a better ad dollar spend for retail and service companies. Being able to place ads next to things that are similar not just based on keywords or other tagging but the imagery of the product can be a differentiating characteristic for Pinterest.[4]<\/a> They also have extended these capabilities outside of their website with their Pinterest chrome extension that will allow users to be on other websites and click on pictures to see what the products are.[5]<\/a> Pinterest uses machine learning to enhance their services to compete against the likes of Amazon, Google and Facebook for their users\u2019 eyeballs and who will be the go to source for product discovery and recommendations.<\/p>\n

In the interim, while this technology continues to advance, Pinterest is using machine learning to try to enhance user experience through the more \u201ctraditional\u201d recommendations based on keywords and the content of posts. Also, to build out their revenue base they have expanded into different types of ads and the ability to more seamlessly purchase relevant goods through their platform. In the immediate future they should continue to pursue this path of revenue generation and gather the data to show that their more targeted ads generate a better return on investment for marketers. So far they have done a great job of many of these items; 56% of style shoppers open the app while shopping in store, 40% of pinners bought a fashion or style item in the last 6 months, 42% of Pinterest women say it helps them discover new brands, etc\u2026[6]<\/a> but it seems there is still room for growth with all the data they are collecting from their users.<\/p>\n

Something that seems like it could be the future is how image recognition will be utilized in many aspects of an individual\u2019s life. Granted I am not sure how feasible this is, but it seems to make logical sense that if you have a high definition photo you took on your phone the same technology that is running image recognition currently on pinned images could do the same for those photos. Imagine if you are walking down the street, see someone wearing cool shoes and take a picture and then be told not only what they are but be given the option to buy it on the spot. That could be an incredible tool for Pinterest.<\/p>\n

I would love to hear from people with more experience in the marketing sphere, whether Pinterest\u2019s users actually present a better advertising opportunity for companies? (731 words).<\/p>\n

[1]<\/a> Pinterest, \u201cHow Pinterest works\u201d, https:\/\/business.pinterest.com\/en\/how-pinterest-works, accessed November 2018.<\/p>\n

[2]<\/a> Pinterest, \u201cPinterest audience demographics\u201d, https:\/\/business.pinterest.com\/en\/audience-demographics-user-stats, accessed November 2018.<\/p>\n

[3]<\/a> Andrew Zhai, \u201cVisual Discovery at Pinterest\u201d, University of California, Berkeley (2017): accessed November 2018.<\/p>\n

[4]<\/a> TechCrunch, \u201cPinterest\u2019s visual search technology is coming to its ads\u201d, https:\/\/techcrunch.com\/2017\/05\/16\/pinterests-visual-search-technology-is-coming-to-its-ads\/, accessed on November 2018.<\/p>\n

[5]<\/a> TechCrunch, \u201cPinterest brings its visual search technology to the web\u201d, https:\/\/techcrunch.com\/2017\/03\/07\/pinterest-brings-its-visual-search-technology-to-the-web\/, accessed on November 2018.<\/p>\n

[6]<\/a> Pinterest, \u201cStyle on Pinterest\u201d, https:\/\/business.pinterest.com\/sub\/business\/insights\/pinterest-style-report-2017-08.pdf, accessed November 2018.<\/p>\n","protected":false},"excerpt":{"rendered":"

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