Artificial Intelligence and Omnichannel Operations

Stephen DeAngelis

July 18, 2019

Many analysts believe the future of retail rests on the shoulders of omnichannel operations powered by artificial intelligence (AI). James D’Arezzo, CEO of Condusiv Technologies, writes, “Any retailer that wants to be competitive knows it must offer a seamless omnichannel experience to its customers.”[1] At the same time, writes SAS’ Rodney Weidemann, “The impact of emerging technologies such as artificial intelligence, machine learning and cognitive computing — the latter underpinned by big data and advanced data analytics — is beginning to be felt.”[2] When omnichannel operations are married with AI, the retail landscape changes forever. Tim Tuttle, CEO and founder of MindMeld, explains, “Artificial intelligence has emerged over the past three years as a key component of digital transformation. As a result, this technology is fundamentally changing the retail industry. The ability to analyze, understand, recommend and predict based on statistics and data-driven processes promises to change how retail workers perform their jobs, how consumers buy products, and how retail organizations support and interact with their customers.”[3]

Omnichannel operations and big data

When Tuttle stresses that retailers need to “analyze, understand, recommend, and predict,” he’s talking about the importance of big data in modern retail operations. D’Arezzo explains, “Omnichannel retailers must process, analyze and use huge amounts of data for a multitude of equally important functions. An omnichannel strategy creates and executes a seamless shopping experience across mobile, online, and brick-and-mortar stores. Retailers with omnichannel strategies have a 91 percent greater annual customer retention rate, according to V12. By 2025, businesses of all kinds will be facing a 50-fold increase in data. Omnichannel retailers are no exception.” Although dealing with all that data can be problematic, it’s essential for success in today’s competitive retail landscape.

Alex Woodie (@alex_woodie), Managing Editor of Datanami, writes, “The big data revolution is changing how business gets done in all industries. That includes the massive retail market, which drives $2.6 trillion in business in the U.S. and employs 42 million Americans. The use of advanced analytics and predictive modeling is changing the face of retail, and helping us all get what we want, when we want it.”[4] He goes on to list nine ways big data and advanced analytics can benefit the retailing sector. They are:

1. Recommendation Engines. “This is one of the classic use cases of big data tech in retail (albeit mostly in ecommerce settings).”

2. Customer 360. “We expect companies to anticipate our needs, to have the products we want on-hand, to communicate with us in real time (via social media), and to adapt to their needs as they change. This is a tall order for any retailer to achieve, but it would be practically impossible to do without some sort of Customer 360 initiative. And considering how many customers a retailer must interact with, and how many data sets are involved with getting there, big data technology and real-time processing is critical to making that happen.”

3. Market Basket Analysis. “Market basket analysis is a standard technique used by merchandisers to figure out which groups, or baskets, or products customers are more likely to purchase together. It’s a well-understood business processes, but now it’s being automated with big data.”

4. Path to Purchase. “Analyzing how a customer came to make a purchase, or the path to purchase, is another way big data tech is making a mark in retail. While marketing executives have studied path-to-purchase techniques for many years, the advent of big data and big data tech is enabling them to get much more out of this type of analysis. The rise of multi-channel marketing in retailing and omni-channel selling is creating a large number of different paths that customers can take to buying a product.”

5. Social Listening for Trend Forecasting. “As a retailer, if you’re not at least listening to social media at this point — let alone actively engaging with them on Instagram or Twitter — then you’re missing out on a slew of free and potentially invaluable information that can help you spot trends.”

6. Price Optimization. “Having the right price on a product can mean the difference between making a sale and losing a customer. But what is the right price? That’s the million-dollar question merchants have struggled with for millennia. But retailers who approach this problem with big data tools may have an advantage over those that don’t.”

7. Workforce and Energy Optimization. “What’s the single largest cost for retailers? If you said ‘labor,’ then give yourself a big red star. While it’s true that big data tech can deliver benefits on the marketing and merchandising side, it can also help big retailers optimize their spending on human capital, which can have a sizable impact. … Retailers can also save big bucks by using big data tech to analyze their energy usage, which is another factor on the cost side of the ledger.”

8. Inventory Optimization. “Inventory optimization is a complicated thing that touches many aspects of the consumer goods supply chain, and often requires close coordination among manufacturers and distributors. But with the rise of omni-channel fulfillment, retailers are increasingly looking for ways to improve the availability of in-demand products.”

9. Fraud Detection. “Big data analytics can help retailers fight fraud in a number of ways. For starters, they can use predictive capabilities to create a baseline sales forecast at the SKU level. If a product deviates noticeably outside of that range, it could indicate some fishy business. Fraud committed by employees can be tough to stop. But with the power of big data tech, internal controllers may be able to create more transparency into internal activities.”

Every use of big data noted above requires cognitive technologies with embedded advanced analytics if the objective is to be achieved.

Omnichannel and artificial intelligence

Louis Columbus (@LouisColumbus), a Principal at IQMS/Dassault Systemes, notes, “AI and machine learning are enabling omnichannel strategies to scale by providing insights into the changing needs and preferences of customers, creating customer journeys that scale, delivering consistent experiences. For any omnichannel strategy to succeed, each customer touchpoint needs to be orchestrated as part of an overarching customer journey.”[5] In addition to the benefits suggested by Woodie, Kristen Deyo (@_kristendk), Digital Marketing Manager at Stantive, notes big data and AI can have a positive impact on customer experience. She explains, “The omnichannel experience is about connecting the physical world and the digital experience that we are all immersed in (using our computers, tablets, other smart devices or channels not yet created). Building off of the most recent trend of providing an omnichannel experience comes the addition of artificial intelligence. With a focus on context and using current technologies such as text based support, or even in-app support options, we have gained better digital experiences and customer service.”[6] She suggests four specific ways AI can help enhance a consumer’s omnichannel experience, while simultaneously maximizing profits. They are: 1) meeting expectations and minimizing the cost to serve; 2) maximizing omnichannel fulfillment capacity; 3) using inventory at its most profitable price point; and 4) making dynamic adjustments as circumstances change.

Concluding thoughts

I hope it’s obvious by now that successful omnichannel operations cannot be divorced from cognitive technologies and advanced analytics. As they say, it’s a match made in heaven. David Cosgrave, Customer Intelligence Lead at SAS South Africa, predicts, “AI will eventually be embraced by everyone. This includes your competitors, so those who fail to embrace it early enough will find themselves at a distinct disadvantage in the technological retail future that is just around the corner.”[7]

Footnotes
[1] James D’Arezzo, “Omnichannel Retail: Big Data Is Nice, Fast Data Is Necessary,” Tech News World, 18 May 2019.
[2] Rodney Weidemann, “Artificial intelligence and the future of retail,” ITWeb, 7 August 2017.
[3] Tim Tuttle, “How Artificial Intelligence is Transforming Retail,” Total Retail, 16 June 2017.
[4] Alex Woodie, “9 Ways Retailers Are Using Big Data and Hadoop,” Datanami, 29 July 2016.
[5] Louis Columbus, “10 Ways AI & Machine Learning Are Revolutionizing Omnichannel,” Forbes, 17 February 2019.
[6] Kristen Deyo, “4 Ways To Power the Omnichannel Retail Experience With Artificial Intelligence,” Stantive, 5 December 2017.
[7] Weidemann, op. cit.