Spending on AI is forecast to double over the span of four years, growing from just over $50 billion in 2020 to a whopping $110 billion in 2024. According to analyst firm IDC, the industry that will spend the most on AI is retail. In fact, the global market size for AI in retail is expected to reach a massive $23.32 billion by 2027!
“Companies will adopt AI — not just because they can, but because they must,” said Ritu Jyoti, Program Vice President for AI at IDC. “AI is the technology that will help businesses to be agile, innovate, and scale. The companies that become AI-powered will have the ability to synthesize information by using AI to convert data into information and then into knowledge, the capacity to learn — using AI to understand relationships between knowledge and apply the learning to business problems — and the capability to deliver insights at scale with AI supporting decisions and automation.”
For retailers specifically, AI will prove to be game-changing in three key areas: Improving quality and speed of decisions, enhancing the customer experience, and streamlining operations.
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AI: Better decisions, faster
The COVID-19 pandemic proved that shopping behavior can change overnight, which has huge implications for retailers that lack the agility to turn on a dime. Initial pantry-loading activities by U.S. consumers at the start of the pandemic, for example, caused out-of-stock issues that continued in supermarkets through the rest of the year, leading to nearly $3 billion in lost sales, according to market researcher NielsenIQ.
According to research by data analytics firm Exasol, 87% of U.S. retail organizations have been under pressure to make faster decisions than ever before. Its research also shows that 71% of these retailers say they are responding to this pressure, with 79% agreeing that shorter decision-making cycles will become the new normal for their business.
What all this means is that retail leaders and in-store workers alike need immediate insights. Putting the right piece of actionable intelligence in front of the right person at the right time leads to better decision-making that increases sales conversion, revenue, and customer satisfaction. AI is absolutely fundamental to this.
That’s why product leaders should look to create retail analytics solutions that enable the kind of real-time, high-performance queries that lead to effective scenario planning. These solutions should be accessible to all actors across a retail business.
“The COVID-19 crisis revealed just how rigid retailers’ operational practices have become over the last generation, as companies emphasized efficiency over agility,” explained Brian Kilcourse, a Managing Partner at Retail Systems Research (RSR). “While something as irrational as the great toilet paper shortage that occurred in the early days of the pandemic probably couldn’t have been predicted, scenario modeling techniques would have helped retailers and their manufacturing partners respond much more quickly. With better real-time analytics, retailers could have better anticipated the products and services needed when and where they were needed, and adjusted their operational strategies accordingly as the pandemic worked its way through markets. These capabilities are enabled by AI analytics.”
The RSR research shows that retail winners are far more interested than other retailers in improving their reaction time to sudden shifts in consumer demand. “They seek intelligent process automation and digital assistants to help them to respond to operational conditions at a hyper-local level,” explained Brian. “This implies analytical engines that can recommend ‘next best actions’ to operators in real time — so this should be where product leaders focus their AI efforts.”
Latin American retail giant masters inventory management
Unisuper, a major retailer in Latin America, is a great example of what can be achieved with powerful AI-infused data insights. As part of its bid to compete head-to-head with Walmart, it chose the EreaBI Analytics Platform for Retailers from Erea Consulting.
Thanks to Erea’s solution, built on the Sisense platform, Unisuper has empowered its supplier teams to track and better plan their inventories at an SKU-level per store by leveraging advanced forecasting models that enable a degree of agile inventory allocation never before seen in the industry. Stock can be transferred from one store to another where it’s flying off the shelves and customers experience fewer stock-outs on their supermarket trips.
All of this data is incredibly valuable. In fact, after just five months, Unisuper was able to start monetizing key information with its suppliers. Today, the company generates close to $2 billion by selling data back to suppliers via its white-labeled UNIBI platform.
Improving the shopping experience with AI
Forrester research has found that companies that lead in customer experience (CX) outperform laggards by nearly 80%. For businesses looking to improve CX, data is a precious commodity: It has the potential to tell them much about their customers’ digital journeys, enabling them to address issues and adopt the intelligent product discovery and recommendations that will deliver more personalized service.
According to McKinsey, more than 85% of companies that report a more mature customer analytics function say they achieve a significant value contribution, compared with around 20% for those with a low usage of analytics.
Meanwhile, almost 60% of companies say they’ve experienced an increase in customer retention and loyalty after investing in analytics (according to research by Harvard Business Review Analytic Services).
Building a product that improves CX means making every possible effort to leverage as much customer data as possible. This data could include everything from email open rates and customer feedback to frequency of website visits, average value of spend, and even social media behavior. This data should be easily analyzed and presented to users in ways that make sense to them, resulting in meaningful intelligence that helps to build a comprehensive picture of the customer journey — and one that can detect friction and analyze the reasons behind cart abandonment.
Brian said his research backs this up — and argued that product teams should put a greater focus on leveraging data of all kinds when building AI tools for retailers.
“We asked retailers to rate the importance of non-performance specific data in their decision-making processes. Retail winners consistently find this data more important than others do. These data include market metrics, competitive data, community health information, location-specific data, social signals, and weather — and the list of external non-transactional data available to retailers continues to grow. Combined with AI analytics, all of this data can help retailers both model operational scenarios and respond more quickly when exceptional conditions do occur — factors that have a big impact on the customer experience. When data is not prized, reacting to problems becomes a way of life that distracts from forward-looking planning.”
Brian predicted that AI-enabled analytics will be fundamental to retailers’ efforts to deliver exceptional customer experiences. “Retailers recognize that what’s key is finding and selling the exact products customers want to buy and making sure those products are in the right place at the right time,” he said. “Half of all the retailers we surveyed are confident that AI-enabled analytics will fundamentally change customer interactions.”
However, retail winners go beyond merely being able to better anticipate consumers’ paths to purchase. “They are moving ahead on using AI-analytics in managing products, the supply chain, and even evaluating store performance,” Brian explained. “So these are key areas that product teams should focus on.”
Streamlining operations with AI
Having the actionable insights necessary to turn on a dime to a) meet new demands thanks to better supply chain management and logistics, b) adjust pricing accordingly, and c) improve the customer experience as a result can have a massive impact on the bottom line.
That’s because these businesses are leaner, more efficient, and deliver a far better customer experience.
The statistics speak for themselves: Brands that create personalized experiences by integrating advanced digital technologies and proprietary data for customers are seeing revenue increase by 6% to 10% — two to three times greater than those who don’t, says research from analysts at BCG.
Product teams can help expedite this by building solutions that help to break down data silos. According to Adobe, data silos were listed by 37% of businesses as the biggest barrier to creating a comprehensive view of their customers. Building an analytics platform that brings together all key data into a single location can help retailers to deliver the exceptional experience that today’s customers demand — and deliver huge improvements to the bottom line as a result.
This is where U.K. retailer Pet Corner, part of the Pet Family group, is seeing exceptional results. With ambitious plans for growth and development, Pet Family needed to ensure it could act quickly, allocate budget and resources where needed, and continue to make the best decisions for the business.
YourDMS and Truth implemented the Sisense BI platform to provide Pet Family with oversight of its entire operation. As a result, users can now act on the insights from data within hours, rather than weeks. Sisense has improved access to data throughout the business due to its user-friendly, self-service dashboards.
This has allowed both Pet Family and its suppliers to order the correct amount of stock, allowing it to reduce costs, operate more efficiently, and avoid overstocking (and leaving products sitting on the shelf) or understocking (and disappointing customers).
Big AI opportunities for every retailer
Ultimately, product teams have a fantastic opportunity to equip retailers with the tools that can revolutionize almost every aspect of their operations.
“All retailers are looking forward to simpler analytical tools to use, but winners are particularly focused on tools that can help them transform non-transactional data into something that can be analyzed,” Brian surmised.
He added that winners also are much more interested in making the insights derived from AI analytics available to both executives and operational management via mobile devices — in other words, to get actionable information into the hands of decision-makers. “It’s a smart strategy,” Brian said. “What is the point of an ‘agility’ agenda without also empowering managers closest to the point of impact?”
And Brian had one final piece of advice for product teams: “Don’t forget traditional business analytics,” he said. “AI analytics don’t replace traditional analytics and reporting — instead, AI analytics are additive! A surprising number of average and under-performers undervalue the traditional analyses of the data gathered from operational systems. In this day and age, that’s intolerable. Over the years that RSR has been studying BI and analytics, retail winners have consistently demonstrated a better appreciation of the value of those analyses — and for good reason: They offer a wealth of insight that is fundamental to success. Product teams shouldn’t lose sight of that fact.”
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Lindsay James is a journalist and copywriter with over 20 years’ experience writing for enterprise business audiences. She’s created copious copy for some of the world’s biggest companies and is a regular contributor to The Record, Compass, and IT Pro.