Investing in Artificial Intelligence: The Present and Future of Retail Marketing

Marketers have not fully absorbed the broader capabilities of artificial intelligence. While early digital developments such as chatbots have been widely adopted, there’s plenty of room for broader and more powerful uses. To be able to utilize AI to optimize operations, marketers should assess its current state as a marketing tool, then envision the potential future that lies beyond.

Artificial Intelligence

In our last article, we discussed the cost of marketing to the wrong customers. Here we’ll talk about how artificial intelligence (AI), among other applications, can help a retailer avoid the marketing missteps we discussed in our previous piece.

A transformative technology in the broadest sense, AI has the capacity to offer a variety of changes and improvements throughout the global economy. In the context of retail marketing specifically, it offers tremendous power, but is not yet well defined nor understood among all marketers. Many early digital developments, such as chatbots, have been widely adopted, but there is plenty of room for broader and more powerful uses. To be able to utilize AI for optimized operations, marketers need to understand its current state as a marketing tool, and the potential future benefits it offers.

The Current State of Marketing AI: Room for Development

The current marketing landscape as it pertains to artificial intelligence might best be described as a Wild West environment. There isn’t a strong consensus or any structured, long-term plans for the growth and use of the technology. However, as organizations learn how to harness its value, the possibility of positive growth is nearly infinite. Some retailers use few, if any, applications of AI related to marketing, but others have developed organization-wide efforts to understand, embrace and implement machine learning. However, many other companies fall in the spectrum between these two extremes and are unsure how to best utilize the power offered by thinking computer systems. Many factors play a role in current utilization, from the size of the business to the specific market segment in which it operates and the existing capabilities of staff.

Using Machine Learning to Improve the Customer Experience

A strong understanding of artificial intelligence helps businesses connect the wide-ranging powers of forward-thinking technology to their specific needs. One advantage to developing a deeper knowledge of AI is creating a seamless, personalized experience through the use of many streams of data related to customers and the merchandise they select.

Fashion retailer Rebecca Minkoff draws on machine learning to create a personalized, responsive and seamless experience for shoppers. The company has developed smart fitting rooms that identify merchandise brought into the dressing area. This allows customers to quickly make a variety of choices related to size, accessories and complementary selections. It also provides the store with more information about the shopper’s preferences. This approach supports in-store staff in an efficient way, allowing them to offer more relevant suggestions and learn from back-end data without requiring any additional work on the part of customers. This in turn allows the company as a whole to develop more effective customer profiles and leverage them to provide further benefits.

Advances like those developed and deployed by Rebecca Minkoff are examples of how innovative retailers using this new technology will win against any organizations that choose not to pursue new artificial intelligence possibilities. Making progressive change through new and expansive tools can make the difference between increasing relevance and continual struggle in a shrinking retail marketplace.

A Retail Business Must Also Be a Technology-Focused Organization

The customer experience increasingly hinges on how retailers implement and use technology, both to connect to potential customers and to drive digital and in-store purchases. Amazon’s machine learning capability, used to analyze internal and external purchases as well as personal and historical data related to its customers, led to Amazon Go brick-and-mortar storefronts. Though the stores do not operate solely on artificial intelligence, the technology has played a critical role in building the Amazon Go concept. Drawing on the desires of consumers and the types of products they want stocked in store, machine learning offers critically valuable information to retailers that makes new ventures relevant and successful.

In this way, artificial intelligence represents an extensive value proposition by offering insights into consumer behavior and needs across e-commerce sites and brick-and-mortar storefronts. While traditional marketing practices are still in use, they must be paired with technological insights to ensure that retail offers are reaching the right interested audience. If the offer is not targeted to the right potential customer base, marketing efforts will be lost with no revenue gain.

Cue the importance of a thorough understanding of technological capabilities, future goals and the best tools for the job. This understanding and development can lead to successful execution of marketing efforts that ensure the audience most likely to purchase is the one being reached. This in turn will lead to increased revenue streams. While for many businesses, this means hiring data analysts, engineers and similarly credentialed professionals to produce the crucial work and perspective for deeper insights on how AI can influence marketing operations, it remains to be seen if there are enough such qualified individuals in the workforce, or whether retailers will have to nurture talent from within their organizations.

The Future Of AI for Marketing

The broad applications made possible by thinking computer systems, and the right combination of opportunity and technology, can yield beneficial results in a variety of different circumstances, but certain areas are currently more thoroughly primed for positive change than others. In-store customer behavior tracking is one especially promising area. By observing in-store actions through footpath tracking, retail marketers can reposition product displays and overall store organization to optimize the customer experience so it is tailored toward products of highest interest.

In a similar manner, albeit a far more controversial application, facial recognition can have a major impact on the consumer experience. This area of machine learning can work to identify loyalty program members upon entrance and then offer them personalized service and promotions. This high level of customization is a unique experience that can set retailers apart from their competitors. However, some customers are hesitant to participate in facial recognition due to privacy concerns. This again highlights the need for marketers to deeply understand technology in relation to their customer base—to make informed decisions that create mutually beneficial interactions while ensuring they do not push away their target market. One potential approach to dealing with this challenge is to develop an opt-in program for facial recognition that gives customers a benefit and allows them to provide consent in return for storing their information in a retailer’s database.

Ultimately, marketers that have the capacity to intelligently consider, compare, utilize and improve the use of artificial intelligence in various applications will be the winners in a future space where technology will increasingly inform marketing decisions. Whether increasing basket size online or creating personalized in-store experiences, AI’s potential is boundless. Success or failure in harnessing it will ultimately determine who wins in the marketplace.

Author: Tim Lefkowicz

Tim Lefkowicz is Managing Director for global consultancy AArete’s Western Region and specializes in non-labor cost reduction for clients in a range of industries, including retail and consumer, manufacturing, technology, financial services, media & entertainment, manufacturing, healthcare, consumer products, law firms and higher education. Tim brings to clients his skills in supply market economics, negotiations, process improvement, team-oriented problem-solving and market intelligence research. He holds a Masters of Business Administration from The Anderson School at UCLA, and a Bachelor of Arts from The University of Virginia and is a member of Sourcing Interest Group.

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