{"id":29746,"date":"2018-11-12T21:03:19","date_gmt":"2018-11-13T02:03:19","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/predicting-customer-preferences-at-starbucks-using-ai-and-the-challenges-for-the-marketer-of-the-future\/"},"modified":"2018-11-12T21:04:39","modified_gmt":"2018-11-13T02:04:39","slug":"predicting-customer-preferences-at-starbucks-and-the-challenges-for-the-marketer-of-the-future","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/predicting-customer-preferences-at-starbucks-and-the-challenges-for-the-marketer-of-the-future\/","title":{"rendered":"Predicting customer preferences at Starbucks and the challenges for the marketer of the future"},"content":{"rendered":"

Marketers are using AI to help them connect with their clients.\u00a0 <\/strong>The marketing department is usually an early adopter of new analytic technologies such as machine learning and AI. Business in general use these tools to understand patterns in data and derive insights from it. As companies seek to understand better the drivers of their customer behavior, more marketers are now leaning on AI (Artificial Intelligence) to process data on their operations more efficiently. Essentially, marketing departments are using computers to scale up their analytical capacity and support the decision-making process with less human resources. Currently, it is estimated that 51% of the marketers use some kind of AI, while 71% high performers say they do [1], suggesting a correlation -but not necessarily a causation – \u00a0between analytical capabilities and performance. Some of the benefits that such technology presents to marketing departments could be generating new recommendations based on previous interactions, segmenting customers based on their shared preferences, hyper personalization of content to match their interests or predictive modeling of future behavior [2]. The increasing focus on customer centric marketing strategies, social media advertisement and virtual assistants is expected to bring this industry up to $40 billion by 2025 from $6.5 billion in 2018. This increase is majorly attributed to the use of Machine Learning to analyze big data to generate insights in a way not possible before [3].<\/p>\n

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Source: Salesforce.com [1]<\/figcaption><\/figure>\n

Starbucks and the Digital Flywheel Program.\u00a0 <\/strong>Based on more than 90 million transaction a week, Starbucks gather an insane amount of data and it is eager to extract value from it. In their minds, that data can provide insights on how the best tailor the user experience, independently from where they are [4]. The famous coffee brewing powerhouse Starbucks announced in 2016 that they were going to implement a new AI feature to their already consolidated Starbucks Rewards loyalty program. The technology aims to cross-analyze an enormous amount of data points such as weather conditions, time of the day, past purchases among others to start real-time interactions in the most convenient moment [5]. For instance, this cloud-based engine is able to recommend different combinations from their menu to customers that may want to try a coffee whenever they are close a Starbucks location [6]. As other digital features \u2013 such as Mobile Order and Pay checkout – of the Rewards program evolve, new opportunities to integrate the predictive technology emerges. So far, the success of the program is remarkable, with the company commenting in a report that it has \u201cmore than doubled customer response rates over previous segmented email campaigns, translating into increased customer engagement and, importantly, accelerated spend.\u201d [7].<\/p>\n

Generating impact from computer-based decision-making.\u00a0 <\/strong>As the company explores ways to leverage their artificial intelligence and machine learning capabilities, the applications to generate impact are numerous. In the short term AI provides an endless source of opportunities to tailor the user experience for each client. From product development to promotions, some of the decisions usually assigned to marketing analysts can be in better hands if delegated to a well-trained machine. The computer can be more precise, agile and unbiased than a human if fed with the right data.<\/p>\n

Predicting consumer preferences should serve as an input for the definition of an overarching strategy by the marketer, not the end strategy itself. The marketing department must see the technology as an ally to take over some of the marketing processes to let the team remain focused in the most important aspect of the job: understanding and elaborating how to implement an insight. For instance, as gaining share on user\u2019s attention becomes more and competitive with broad adoption of tactics such as push notification on smartphones, Starbucks is bound to find new ways to bring its analytical edge to everyday interactions.<\/p>\n

What future success looks like.\u00a0<\/strong> The move towards machine learning is inevitable and developing an expertise in the area is a must for the marketer of the future. However, perhaps as importantly will be ability to interact with consumers in a meaningful way. In a world where all our actions are collected and analyzed by computers, who would you pick to win? The best algorithm or the best execution? That the question that every marketers should be making themselves.<\/p>\n

(706 words)<\/p>\n

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[1] Salesforce.com. (2017). Fourth Annual State of Marketing Report<\/em>. [online] Available at: https:\/\/www.salesforce.com\/content\/dam\/web\/en_us\/www\/assets\/pdf\/datasheets\/salesforce-research-fourth-annual-state-of-marketing.pdf [Accessed 12 Nov. 2018].<\/p>\n

[2] Intelligence.businessinsider.com. (2018). AI will revolutionize the way marketers target audiences, but preparedness is key<\/em>. [online] Available at: https:\/\/intelligence.businessinsider.com\/post\/ai-could-revolutionize-marketing-but-preparedness-is-key-2018-2 [Accessed 12 Nov. 2018].<\/p>\n

[3] Artificial intelligence in marketing market to grow 29.79 percent CAGR to 2025. (2018). Business World, <\/em>Retrieved from http:\/\/search.proquest.com.ezp-prod1.hul.harvard.edu\/docview\/2121518554?accountid=11311<\/a><\/p>\n

[4] Boulton, C. (2016). Starbucks’ CTO brews personalized experiences<\/em>. [online] CIO. Available at: https:\/\/www.cio.com\/article\/3050920\/analytics\/starbucks-cto-brews-personalized-experiences.html [Accessed 13 Nov. 2018].<\/p>\n

[5]\u00a0 (2017). Starbucks’ Digital Flywheel Program Will Use Artificial Intelligence<\/em>. [online] Available at: https:\/\/www.nasdaq.com\/article\/starbucks-digital-flywheel-program-will-use-artificial-intelligence-cm824541 [Accessed 13 Nov. 2018].<\/p>\n

[6] Marr, B. (2017). Starbucks: Using Big Data, Analytics And Artificial Intelligence To Boost Performance<\/em>. [online] Forbes. Available at: https:\/\/www.forbes.com\/sites\/bernardmarr\/2018\/05\/28\/starbucks-using-big-data-analytics-and-artificial-intelligence-to-boost-performance\/#444cae1765cd [Accessed 13 Nov. 2018].<\/p>\n

[7] Starbucks. (2018). Starbucks Presents its Five-Year Plan at Investor Conference<\/em>. [online] Starbucks Newsroom. Available at: https:\/\/news.starbucks.com\/news\/investor-day-2016-press-release [Accessed 13 Nov. 2018].<\/p>\n

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