{"id":35927,"date":"2018-11-13T19:30:53","date_gmt":"2018-11-14T00:30:53","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/machine-learning-to-save-millions-of-dollars-of-advertising-waste-at-pg\/"},"modified":"2018-11-13T19:30:53","modified_gmt":"2018-11-14T00:30:53","slug":"machine-learning-to-save-millions-of-dollars-of-advertising-waste-at-pg","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/machine-learning-to-save-millions-of-dollars-of-advertising-waste-at-pg\/","title":{"rendered":"Machine learning to save millions of dollars of advertising waste at P&G"},"content":{"rendered":"

Recently, in social media, several ladies were mocking an unsuccessful Gillette sampling campaign. The brand aimed at delivering free sample razors to male consumers, but due to poor targeting capabilities, many of those reached also many females of different ages[1]<\/a>.<\/p>\n

The issue of wrong targeting observed here is a big problem both in offline and online advertising. In fact, a key objective in advertising is to maximize its ROI through precise targeting i.e. ensuring that ads reach a desired consumer group[2]<\/a>. However, precise targeting is still a big advertising challenge: understanding exactly who is reached by ads is not straightforward, even in the digital space. \u00a0Target groups are mostly built with limited or incomplete data sets, often not connected at all[3]<\/a>. This is where machine learning can help[4]<\/a>[5]<\/a> \u2013 analyzing data to identify a set of behaviors (online, like web browsing or search keywords, or offline, like GPS-identified locations visited), to make sure the targeting is more accurate and maximizes some specific objectives[6]<\/a> (e.g. view rate of videos, clicks, registrations, downloads etc.).<\/p>\n

There are several ways in which P&G is leveraging machine learning to improve ROI on its marketing\/advertising investments[7]<\/a>[8]<\/a>, for example in search and in programmatic.<\/p>\n

In search, P&G leverages machine learning capabilities to improve Adwords results (i.e. reach and cost). In the past, P&G would decide which search key words to invest in based on a self-assessment i.e. based on the desired target groups, a list of potential search key words would be defined manually. In order to optimize the process, P&G partnered with Google and media agencies to leverage machine learning capabilities to determine the optimal search key words for their target segments. For example, based on keywords search data (i.e. which terms are searched by which people over time), it is possible to define the life stage of people (e.g. mums) to then serve them relevant ads across the different platforms[9]<\/a>.<\/p>\n

In programmatic advertising, P&G has been the pioneer for years. Programmatic advertising is a technology stack that helps match advertisers (those who want to advertise) with publishers (those who own space to display ads e.g. websites) and allows them to trade the advertising space in an automatic way. Besides the operational efficiencies, one of the main advantages of programmatic advertising lies in improved targeting via machine learning[10]<\/a>[11]<\/a>. In fact, the technology stack can leverage data on web browsing behaviors to identify specific target groups (e.g. mums, sport lovers, fashionistas etc.). P&G started this journey with an external partner, Audience Science, and then moved to a new tech stack, The Trade Desk and Neustar, in 2017[12]<\/a>[13]<\/a>. In fact, The Trade Desk keeps on releasing new functionalities that leverage machine learning via \u201cpowerful AI that improves advertisers\u2019 decisioning and accelerates campaign performance\u201d[14]<\/a>.All of this helps P&G achieve their strategy of 1:1 precision marketing through \u201celiminate[ing] waste by reducing excess frequency within and across channels, [and] eliminating non-viewable ads\u201d[15]<\/a>.<\/p>\n

Beside targeting capabilities, another important factor that contributes to media investment ROI links to ensuring that ads reach real humans. In fact, one of the emerging issues in digital media is ad fraud i.e. malicious bots that simulate human behaviors, view ads and so inefficiently consume advertisers\u2019 media budgets[16]<\/a>. According to WFA[17]<\/a>, ad fraud a huge issue in the industry, \u201clikely to represent in excess of $50 billion by 2025\u201d. Machine learning could be leveraged to help strengthen detection systems, assess data and determine fraudulent behaviors also in P&G advertising. The barrier that prevents such scaled solution today is computational power (in the advertising space, a company like P&G has trillions of transactions per day) and quality of detection algorithms. However, \u201cas automated fraud detection tools get smarter and machine learning becomes more powerful, the outlook should improve exponentially\u201d[18]<\/a> and leveraging machine learning to eliminate ad fraud should become more and more important for P&G in the medium term. For this to happen, P&G should work on two dimensions. Internally, it would be important to dedicate some of the internal machine learning resources to the understanding of the ad fraud issue, to later fight it in a more structured way and with the most advanced machine learning capabilities available. Externally, P&G should partner with machine learning companies and onboard them on how the different media platforms work. A first step would be to share relevant data with potential partners so that machine learning algorithms can be developed and tested.<\/p>\n

There are several open questions: How should P&G collaborate on ad fraud detection with other advertisers (as it\u2019s an industry-wide and industry-relevant issue)? Should the new capabilities be outsourced or developed in-house to ensure P&G competitive advantage? How will the governance of ad fraud detection processes work (i.e. who will assess their effectiveness)?<\/p>\n

(800 words)<\/p>\n

[1]<\/a> Sapna Maheshwari, \u201cWelcome to Manhood, Gillette Told the 50-Year-Old Woman\u201d, nytimes.com, July 16, 2017, https:\/\/www.nytimes.com\/2017\/07\/16\/business\/media\/gillette-razors-marketing-mistakes.html?_r=0<\/a>, accessed November 2018.<\/p>\n

[2]<\/a> David Court, Jonathan Gordon, and Jesko Perrey, \u201cMeasuring marketing\u2019s worth\u201d, mckinsey.com, May 2016, https:\/\/www.mckinsey.com\/business-functions\/marketing-and-sales\/our-insights\/measuring-marketings-worth<\/a>, accessed November 2018.<\/p>\n

[3]<\/a> David Rogers, David Rogers, \u201cMarketing ROI in the Era of Big Data: The 2012 BRITE\/NYAMA Marketing in<\/p>\n

Transition Study\u201d, (paper, Colombia Business School, Center on Global Brand Leadership, 2012), https:\/\/www.iab.com\/wp-content\/uploads\/sites\/4\/2015\/05\/2012-BRITE-NYAMA-Marketing-ROI-Study.pdf<\/a>, accessed November 2018.<\/p>\n

[4]<\/a> Stacy Pollard, \u201cAn investors\u2019 Guide to Artificial Intelligence\u201d, Global Equity Research, J.P. Morgan, November 27, 2017, http:\/\/search.proquest.com.ezp-prod1.hul.harvard.edu\/docview\/1971723389?accountid=11311<\/a>, accessed November 2018.<\/p>\n

[5]<\/a> James Raphael Poole, \u201cLucy: IBM Watson Analytics meets Madison Avenue\u2014Considering Cognitive Computing Artificial Intelligence Tools Employed for Advertising Media Planning and Buying\u201d, (paper, The College of St. Scholastica, May 14, 2016), https:\/\/search-proquest-com.ezp-prod1.hul.harvard.edu\/docview\/1808419103?accountid=11311<\/a>, accessed November 2018.<\/p>\n

[6]<\/a> \u201cNielsen Launches Artificial Intelligence Technology\u201d, press release, April 4, 2017, on ProQuest website, http:\/\/search.proquest.com.ezp-prod1.hul.harvard.edu\/docview\/1883610003?accountid=11311<\/a>, accessed November 2018.<\/p>\n

[7]<\/a> Thomas Davenport, Randy Bean, \u201cHow P&G and American Express Are Approaching AI\u201d, hbr.org, March 31, 2017, https:\/\/hbr.org\/2017\/03\/how-pg-and-american-express-are-approaching-ai<\/a>, accessed November 2018.<\/p>\n

[8]<\/a> Ellen Hammett , \u201cP&G’s advertising future defined by AI and a social conscience\u201d, mediatel.co.uk, September 14, 2017, https:\/\/mediatel.co.uk\/newsline\/2017\/09\/14\/pgs-advertising-future-defined-by-ai-and-a-social-conscience\/<\/a>, accessed November 2018.<\/p>\n

[9]<\/a> Lauren Johnson, \u201cHere\u2019s What P&G Has Learned Since Demanding Advertising Transparency 9 Months Ago\u201d, adweek.com, September 13, 2017, \u00a0https:\/\/www.adweek.com\/digital\/heres-what-pg-has-learned-since-demanding-advertising-transparency-9-months-ago\/<\/a>, accessed November 2018.<\/p>\n

[10]<\/a>Chris Victory, \u201cIn 2018, Marketers Will Discover More AI Applications in Programmatic Advertising\u201d, emarketer.com, December 6, 2017, https:\/\/www.emarketer.com\/Article\/2018-Marketers-Will-Discover-More-AI-Applications-Programmatic-Advertising\/1016801<\/a>, accessed November 2018.<\/p>\n

[11]<\/a> Emma Williams, \u201cThe Role of AI in Redefining the Programmatic Advertising Experience\u201d, mediamath.com, March 14, 2018, http:\/\/www.mediamath.com\/blog\/the-role-of-ai-in-redefining-the-programmatic-advertising-experience<\/a>, accessed November 2018.<\/p>\n

[12]<\/a> Jack Neff, \u201cP&G Shakes Up Tech Providers Behind Global Programmatic Buying\u201d, adage.com , May 4, 2017, \u00a0https:\/\/adage.com\/article\/digital\/p-g-shakes-tech-providers-global-programmatic\/308920\/<\/a>, accessed November 2018.<\/p>\n

[13]<\/a> Javier Polit – CIO, Procter & Gamble Company, Boardroom Insiders profiles, August 9, 2017 https:\/\/search-proquest-com.ezp-prod1.hul.harvard.edu\/docview\/1938630080?accountid=11311<\/a>, accessed November 2018.<\/p>\n

[14]<\/a> The Trade Desk, \u201c News and Insights\/ The Trade Desk Ushers in the Next Wave of Digital Advertising featuring Koa™, Artificial Intelligence (AI) for Advertisers\u201d, https:\/\/www.thetradedesk.com\/press-releases\/the-trade-desk-ushers-in-the-next-wave-of-digital-advertising-featuring-koa-artificial-intelligence-ai-for-advertisers-2<\/a>, accessed November 2018.<\/p>\n

[15]<\/a> Joe Moeller, P&G CFO, transcript from Earnings Conference Call Q2 2018, January 23, 2018. Transcript provided by seekingalpha.com, https:\/\/seekingalpha.com\/article\/4139481-procter-and-gambles-pg-management-discusses-q2-2018-results-earnings-call-transcript?part=single<\/a>, accessed November 2018.<\/p>\n

[16]<\/a> Javier Polit – CIO, Procter & Gamble Company, Boardroom Insiders profiles, August 9, 2017 https:\/\/search-proquest-com.ezp-prod1.hul.harvard.edu\/docview\/1938630080?accountid=11311<\/a>, accessed November 2018.<\/p>\n

[17]<\/a> Mikko Kotila, Ruben Cuevas Rumin, Shailin Dhar, \u201cCompendium of ad fraud knowledge for media investors\u201d, WFA Report (2016). World Federation of Advertisers, https:\/\/www.wfanet.org\/app\/uploads\/2017\/04\/WFA_Compendium_Of_Ad_Fraud_Knowledge.pdf<\/a>, accessed November 2018.<\/p>\n

[18]<\/a> Vian Chinner, \u201cArtificial Intelligence And The Future Of Financial Fraud Detection\u201d, forbes.com, June 4, 2018 https:\/\/www.forbes.com\/sites\/theyec\/2018\/06\/04\/artificial-intelligence-and-the-future-of-financial-fraud-detection\/#3909e54a127a<\/a>, accessed November 2018.<\/p>\n","protected":false},"excerpt":{"rendered":"

The power of machine learning is leveraged today by P&G to improve advertising targeting and ROI, especially in search and programmatic. 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