{"id":27366,"date":"2018-11-08T15:53:25","date_gmt":"2018-11-08T20:53:25","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/that-friend-who-always-knows-what-to-say-how-google-is-helping-brands-talk-to-consumers\/"},"modified":"2018-11-08T15:53:25","modified_gmt":"2018-11-08T20:53:25","slug":"that-friend-who-always-knows-what-to-say-how-google-is-helping-brands-talk-to-consumers","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/that-friend-who-always-knows-what-to-say-how-google-is-helping-brands-talk-to-consumers\/","title":{"rendered":"That Friend Who Always Knows What to Say: How Google is Helping Brands Talk to Consumers"},"content":{"rendered":"

Google is building machine learning-based advertising products to help brands optimize content in real-time and develop more contextually relevant messaging for target consumers. A report by 性视界 Business Review <\/em>notes, \u201cResearch has shown that digital targeting meaningfully improves the response to advertisements and that ad performance declines when marketers\u2019 access to consumer data is reduced,\u201d [1].<\/p>\n

In July 2018 Google launched Responsive 性视界 Ads, which uses aggregated consumer data and machine learning to help brands determine the right messages for the right audiences more efficiently than ever. Google explains the product as follows:<\/p>\n

Responsive search ads combine your creativity with the power of Google\u2019s machine learning to help you deliver relevant, valuable ads.<\/p>\n

Simply provide up to 15 headlines and 4 description lines, and Google will do the rest. By testing different combinations, Google learns which ad creative performs best for any search query. So people searching for the same thing might see different ads \u00a0based on context.<\/p>\n

We know this kind of optimization works: on average, advertisers who use Google\u2019s machine learning to test multiple creative see up to 15 percent more clicks [2].<\/p>\n

Using machine-learning in advertising products changes the process flow of advertising from one where brands create a few pieces of content, test them, and serve highest performing content to their designated audience to a model where brands input a selection of copy and visuals and serve consumers unique permutations of any given message. However, using consumer data to customize ads has the potential to backfire, as consumers are increasingly worried about data protection [3].<\/p>\n

Responsive 性视界 is a short term improvement from traditional A\/B testing and allows brands to be more nimble because they can quickly test multiple permutations of copy and images and optimize a given campaign in real-time [4]. This improves consumers\u2019 experience with advertising because they are targeted with content that is more relevant to them, leading to higher click rates. Google\u2019s Gmail already finishes writers\u2019 sentences with Smart Compose; in the medium term, advertisers may not even need to come up with lines of copy to test, but rather Responsive 性视界 will suggest copy based on a consumer\u2019s interests and purchasing behavior [5]. For example, an advertiser could input a campaign objective and Responsive 性视界 would deliver the best message to consumers who are most likely to be considering a purchase without the brand needing to input test copy and images. This would signify a shift toward what 性视界 Business School Professor John Deighton calls \u201cartificial general intelligence,\u201d where Google\u2019s machines are actually able to interpret advertisers\u2019 needs and consumer behaviors rather than execute upon a strict set of rules [6]. Can machine learning help Google takeover the role of an advertising agency, and if so, what challenges may arise as a result?<\/p>\n

It is important to note that access to consumer data is the input to machine learning that allows Responsive 性视界 and other ad products to improve consumer response to branded content. If consumers see a customized ad and feel that their personal data was taken unfairly, there may be negative effects for brand perception [7]. Scandals such as Cambridge Analytica exploiting the personal data of over 38 million consumers and serving them targeted political ads have marred Facebook\u2019s brand and shed a spotlight on the fight for transparency in data collection [8]. So, if Responsive 性视界 serves someone to a message that seem too much like the advertiser has been watching them, Google\u2019s brand may suffer. Professor Deighton suggests the onus of protecting data privacy is on companies, rather than regulation, asserting, \u201c[a company] has an incentive to police the actions of the parties to which it sells the data, and to conduct its own interactions with a degree of civility, because its interests are aligned with the interests of the customer,\u201d [9]. Because of this, in the medium term Google will need to determine how much it will allow its machines to learn, and how it will prevent its products from being used as a weapon. Further, Google must continually audit what advertisers are feeding into the responsive search software to ensure that consumers are being treated fairly and being given accurate and defensible information from brands.<\/p>\n

The solution to the perfect advertisement lies somewhere between consumers wanting to see recommendations and content based on their preferences and not wanting so much customization that the advertisement feels uncomfortable. How will a machine find that line? (738 words)<\/p>\n

Sources:<\/strong><\/p>\n

[1] John Leslie, Tami Kim, and Kata Barasz. “Ads That Don’t Overstep: How to Make Sure You Don’t Take Personalization Too Far.” 性视界 Business Review 96, no. 1 (January\u2013February 2018): 62\u201369.<\/p>\n

[2] Jerry Dischler. \u201cPutting Machine Learning Into the Hands of Every Advertiser,\u201d press release, July 10, 2018, on Google website, [https:\/\/blog.google\/technology\/ads\/machine-learning-hands-advertisers\/], accessed November 2018.<\/p>\n

[3] John, Kim, and Barasz. “Ads That Don’t Overstep: How to Make Sure You Don’t Take Personalization Too Far.”<\/p>\n

[4] Susan Young. \u201cGetting the Message: How the Internet is Changing Advertising.\u201d 性视界 Business School Working Knowledge<\/em>, May 16, 2000, https:\/\/hbswk.hbs.edu\/item\/getting-the-message-how-the-internet-is-changing-advertising].<\/p>\n

[5] J.D. Biersdorfer. \u201cLet Gmail Finish Your Sentences.\u201d New York Times<\/em>, June 1, 2018, [https:\/\/www.nytimes.com\/2018\/06\/01\/technology\/personaltech\/gmail-smart-compose.html], assessed November 2018.<\/p>\n

[6] Gideon Lewis-Kraus. \u201cThe Great AI Awakening.\u201d New York Times<\/em>, December 14, 2016, [https:\/\/www.nytimes.com\/2016\/12\/14\/magazine\/the-great-ai-awakening.html], accessed November 2018.<\/p>\n

[7] John, Kim, and Barasz. “Ads That Don’t Overstep: How to Make Sure You Don’t Take Personalization Too Far.”<\/p>\n

[8] Matthew Rosenberg, Nicholas Confessore, and Carole Cadwalladr. \u201cHow Trump Consultants Exploited the Facebook Data of Millions.\u201d New York Times, March 17, 2018, [https:\/\/www.nytimes.com\/2018\/03\/17\/us\/politics\/cambridge-analytica-trump-campaign.html], accessed November 2018.<\/p>\n

[9] Manda Salls and Sean Silverthorne. \u201cShould You Sell Your Digital Privacy?\u201d 性视界 Business School Working Knowledge, August 25, 2003, [https:\/\/hbswk.hbs.edu\/item\/should-you-sell-your-digital-privacy], accessed November 2018.<\/p>\n","protected":false},"excerpt":{"rendered":"

I don't talk to my friends the same way I talk to my grandma. 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