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The name 1-800-Flowers.com is a charming legacy anachronism: These days, most of the gifting brand’s customers don’t dial a phone number, and a clear majority order more than bouquets. In fact, the now 40-plus-year-old parent, 1-800-FLOWERS.COM Inc., is today primarily an e-commerce business whose revenue, since its acquisitions of brands such as Harry & David, Cheryl’s Cookies, Wolferman’s, and The Popcorn Factory, comes largely from food-related gifts.
Its floral origins notwithstanding, the company has been on the cutting edge when it comes to using machine learning (ML) to enhance customer experience. Since 2016, 1-800-FLOWERS.COM Inc. has launched several noteworthy marketing innovations to enhance the customer experience. Partnering with IBM Watson, the company introduced the AI-powered personal gift concierge GWYN (Gifts When You Need) to customize suggestions to online shoppers. In addition, customers can order gifts via chatbots and voice.
Amit Shah, CMO of 1-800-Flowers.com, is committed to furthering machine learning in the organization. While ML technologies are still in the nascent stage at the company, the innovative uses of ML are already “training the muscle memory of the organization very deeply,” Shah says. “I think what we will find, five years down the road, is that the people who took the early bets in artificial intelligence actually achieve the learning that cannot be copied. I don’t think you can short-circuit your way the way you can with other channels.”
Shah is hardly unique among innovative retail marketers in taking ML seriously. Research conducted for our global executive study of strategic measurement, “Leading With Next-Generation Key Performance Indicators,”1 reveals that retail executives believe that ML can improve their KPI outcomes and they are investing in the technology for marketing at high levels. Notably, even those retail executives who don’t believe strongly in the importance of ML report investments in the technology at relatively high rates. They are evidently aware that there are strategic risks to falling behind the competition.
Adoption of Machine Learning in the Retail Industry
We surveyed 1,600 senior North American marketing executives and managers about their use of KPIs and the role of ML in their marketing activities; 653 were from the retail sector. Of these retail marketing executives, 72% believe that their current functional KPIs can be better achieved with greater investment in automation and ML technologies. In the overall sample, a similar percentage of respondents (74%) said the same thing. Sixty-two percent of retail executives said their organization has incentives or internal functional KPIs to use automation and ML technologies to drive marketing activities. In the overall sample, that number was lower: Only 49% of respondents reported having such incentives. Finally, 72% of retail executives said that their organization is investing in new skills or training in 2018 to make marketing more effective in using automation and machine learning. In the overall sample, 63% reported such investments. (See Figure 1.)
Clearly, many marketers in the retail industry, a sector greatly disrupted by digital media, mobile devices, and platform technologies, are actively investing in ML. Despite their overall commitment to ML, however, some retail marketers are far more likely to invest in ML than others. Specifically, organizations more advanced in KPI usage and alignment are more likely to use ML in marketing.
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Measurement Leaders Pull Ahead
Our study grouped all survey respondents into three categories — Measurement Leaders, Measurement Capable, and Measurement Challenged — based on the level of sophistication with which they use KPIs to guide their organization. Survey respondents from the retail sector skewed toward the more sophisticated range of our maturity index. They had more Measurement Leaders (29%) than the overall sample (20%) and fewer Measurement Challenged (12%) than the overall sample (20%). The numbers of Measurement Capable were roughly equivalent. Measurement Leaders look to KPIs to help them lead and to find new growth opportunities for their companies, and the retail sector’s Measurement Leaders are far more likely than other retail organizations to be heavily invested in machine learning.
Measurement Leaders in retail strongly believe in machine learning’s potential to help achieve KPI outcomes. Consequently, they provide investment and incentives to make good on their belief. Asked whether their current functional KPIs could be better achieved with greater investment in automation and ML technologies, an overwhelming majority of Measurement Leaders — 90% — said yes. (See Figure 2.)
When asked whether their organization was investing in new skills or training in 2018 to make marketing more effective in using automation and machine learning, 90% again answered in the affirmative. When asked whether their organization had incentives or internal functional KPIs to use more automation and ML technologies to drive marketing activities, 88% said yes. In essence, the Measurement Leaders in retail have almost completely aligned their levels of ML investment and incentives in marketing.
Machine Learning for Competitive Advantage
In the retail sector, top competitors like Amazon, Walmart, Target, and Home Depot clearly and publicly rely on data, analytics, and machine learning to create their market edge. As a result, ML is fast becoming a commonly used tool among retail marketers. Companies not yet exploiting ML technologies risk being left behind. Two actionable steps follow from this research:
Make ongoing investments in machine-learning capabilities. These investments take various forms depending on enterprise goals. For instance, one emerging KPI shared by several CMOs involves turning customers into brand advocates. “One of the best assets that you can have for a brand is to have a number of people who are excited loyalists and advocates for that brand,” notes Laura Beaudin, partner and global marketing lead at management consulting firm Bain & Co. ML’s ability to analyze social media posts and shopper marketing behaviors creates unique capabilities for identifying which shoppers might wield the most profitable online influence and which social media influencers should be part of a retailer’s targeted outreach. Influences on influencers are not fixed. ML technologies are not static, either. Consider making investments in ML training an ongoing process that familiarizes marketers with ML technology basics, new developments, as well as what data sets are (and could be made) available to the marketing team.
Move beyond investment to incentives. Investing in ML is not enough; it must be matched by incentives to use the technology. With ML’s broader use, new metrics emerge and assume primacy. These metrics can be tied directly to the achievement of specific strategic goals. Clear communication about the value of ML applications to these broader organizational goals is essential to advancing the development and effectiveness of ML capabilities. This is especially true in those companies where ML adoption is in the early stages. Incentives to use ML can be invaluable in creating new efficiencies and improving organizational learning.
Shah of 1-800-Flowers.com notes, “All of our AI efforts are helping us learn about our customers, learn about ourselves, and ultimately learn about how we leverage technology.” From inventory management and staff scheduling to timed and targeted promotions, retail executives will increasingly delegate decisions to data-driven automated algorithms with the capacity to learn.
“Where the rub is,” says Simon Atkins, North America senior vice president and brand director for Adidas America, “is the ability for our brand team to upskill and understand e-commerce’s core KPIs and for the e-commerce team to understand the brand’s core KPIs around sentiment, brand attributes, and sell-through, and to marry both of those in a single view that then sets a strategy and very particular tactics in order to execute. I’d say we’re at the starting point of digging much deeper into those KPIs.”
ML innovations, widely regarded as a reliable path for extracting value from massive data volumes, are already remaking the retail sector. Mobile shopping apps married to ML algorithms are already used to improve recommendations and boost loyalty; latent factor analysis helps retailers better segment their customers and identify possible social media influencers. It is little surprise, then, that our survey finds retailers investing in ML for marketing at higher levels than other industries. Indeed, even retail’s ML pessimists say their companies are investing heavily in the technology. Whether they invest to innovate or simply to stay afloat, retail marketers are aware that companies that neglect machine learning do so at their peril. The risk of not embracing machine learning now outweighs the pain of committing to it.
1. M. Schrage and D. Kiron, “Leading With Next-Generation Key Performance Indicators,” MIT Sloan Management Review, June 2018.
i. “Machine Learning,” Techopedia.