How merchants can drive velocity with AI
The desire to curate irresistible product assortments is what draws many job seekers to the merchant role—people with a passion for creativity, a love for design, and the analytical skills to spot and translate trends into commercially successful product assortments.
While many aspects of the merchant’s job haven’t changed in years, the rules of the game are shifting. Merchandising has always been about moving units at the right margin to drive velocity and profitability through a mix of art and science. But new challenges have intensified and driven complexity to many of the more data-driven, science aspects of the job and it’s now harder than ever to manage these challenges with today’s most used tools.
The emergence of micro-seasons creates shorter and faster business cycles and product lifecycles that reflect more short-lived trends driven by fast fashion and retail media. Merchants feel pressure to identify and act upon trends to be competitive—and that requires a deeper understanding of consumer behavior, data analytics, and market research. Retailers must then balance inventory across physical and ecommerce channels, requiring an agile orchestration of product throughout the supply chain to match supply and demand. AI can help support retailers on the science part of the role so merchants can lean more into the art, reduce manual effort, and unlock creativity.
As Microsoft Excel has served merchants as the common tool for decades, it’s likely generative AI will become foundational moving forward. Every retailer stands to benefit from AI—but since the technology is accelerating so fast, those who wait will be left behind. Already, more than 60% of the Fortune 500 are using agents1 to accelerate business results and empower their teams with AI, helping workers feel more productive and more satisfied in their work.
Here are four ways AI can reinvent the merchant experience and help merchants drive velocity with Microsoft 365 Copilot.
Conversational data querying
Getting from ideation to the sales floor requires a combination of instinct and intensive data analysis. Buyers and planners live in back-end systems of record and data sources where they’re continually looking for patterns and clues to shape forecasting and reordering plans. But because data often resides within email attachments, spreadsheets, and other disparate sources, the role can be highly manual.
Generative AI transforms data visualization and reporting with conversational data querying
Put your data to work by using generative AI to find patterns in data, ask questions of your data, and automate processes to drive predictive merchandising and planning strategies.
Take for example a merchandiser trying to plan for the upcoming season. Imagine being able to ask, “based on omnichannel sales data and competitive landscape signals, what is the optimal mix of products for a specific store or region?”
Natural language prompts rescue merchandisers from manual reporting and arduous data analysis across multiple data sources, and help teams anticipate customer needs and adjust their inventory and marketing strategies. And while the ability to discern customer trends with data is not a new concept, unlocking such insights in near-real time is a game changer—and one that creates more space to marry insight with instinct.
Reduced design to feedback cycle
Staying competitive in retail demands a high level of agility and responsiveness to sudden shifts in consumer behavior. Peaks and troughs in the data make it difficult to know what to stock and where and to stay ahead of consumer demand. Buyers and planners play a key role in this process by helping organizations predict demand based not just on historical data but with insights into hyper-local market trends. Because merchants collaborate with vendors in a highly complex supply chain to ensure products are delivered on time, merchants are often the linchpin in this process. But traditional methods of curating assortments across multiple vendors rely heavily on historical data and manual processes like email—making the work time-consuming and prone to errors.
With generative AI, merchants can bring new assortments to market faster and reduce feedback cycles from months to days
By quickly analyzing inputs from social media, sales, and other data sources, AI also helps teams create more dynamic product assortments and curate portfolios that resonate with customers and emerging trends. Retailers can plan at the individual store level and get ‘hyperlocal’ with product as opposed to the cluster or store level.
By harmonizing data, AI facilitates the creation of personalized product assortments that cater to individual customer preferences and help retailers be more adaptable to sudden shifts in a rapidly changing market.
Forecasting with customer intent and unstructured data
Forecasting customer demand is increasingly complex as customer signals often get lost in a sea of unstructured data. In the same way that over-reliance on historical data hampers reporting and data analysis, historical data is often an imperfect indicator of future sales from a forecasting perspective. Short product lifecycles and a proliferation of micro-seasons—combined with varying demand across online and offline channels—complicate the effort. While consumer buying habits and trends are available as raw data across multiple systems (both internal to the retailer and from external sources), it’s hard to convert this data into actionable insights.
Large language models and generative AI help retailers make use of unstructured data like never before
By making sense of unstructured data, AI can unlock valuable customer signals to enrich plans and forecasts. It can help planners forecast based on customer intent by tapping into consumer sentiment from social media channels, online customer reviews, digital forums, demographic information, local events, and even weather patterns. This enables merchants to respond to customer needs with real-time velocity—reducing the risk of stockouts and overstocking.
AI-powered insights help optimize inventory levels, minimize shipping and logistics costs, and improve overall operational efficiency. By leveraging AI, merchants can create more accurate and dynamic forecasts, ultimately enhancing customer satisfaction and staying ahead of the competition.
Streamlined meeting preparation using a copilot
Preparing for meetings can be a daunting task for merchants, involving multiple steps that require significant time and effort. Due to the nature of a merchant’s work, vendor collaboration and negotiation often occur through email, Excel files, and other unstructured data sources that makes it difficult to summarize end-to-end agreements and commitments. These challenges can detract from the merchant’s ability to engage in more strategic and creative activities, making efficient meeting preparation a must. Compiling information such as pricing details from multiple disparate sources is a highly manual, painstaking process and error prone—writing quality pre-meeting emails and follow-ups takes time, focus, and care.
Merchants can achieve more by using AI for meeting preparation, execution, and follow-up
Generative AI can streamline meeting tasks, making the process more efficient and less burdensome. For instance, AI can assist by summarizing emails and chats about recent price negotiations, helping merchants quickly gather relevant information. It can also analyze profitability reports and present data insights in clear, visual formats, supporting merchants in making informed decisions. When compiling information from vendor agreement documents, AI can help draft content and suggest ways to rewrite it for clarity and impact. It can also assist in creating pre-meeting emails for suppliers, ensuring all necessary documents are attached.
By leveraging AI, merchants can enhance their meeting preparation and execution, freeing up time for more strategic and creative endeavors while always staying in control.
Companies using generative AI to transform merchandising
We’re seeing evidence of how companies are starting to unlock the power of generative AI to help merchants lean more into the art of their jobs while upping their game with the latest technology.
Microsoft partner Blue Yonder can help retailers deliver higher plan accuracy, more relevant AI-powered insights, and a better planner experience. Blue Yonder is developing generative AI solutions that combine large language models’ natural language capabilities with the company’s deep supply chain IP to accelerate data-driven decision-making. By integrating these AI capabilities directly within the Blue Yonder Platform, which operates on Microsoft Azure, retailers inherently benefit from robust security measures, auditing, reliability, and cost control.
Streamline your growth with generative AI
Merchants need to drive velocity and profitable sales growth by moving units at the right margin, a combination of art and science that makes the role so vital and fulfilling. AI reduces time spent on mundane tasks, allowing merchants to be more creative, strategic, and customer focused. Merchants can simplify their work, gain more agility, and speed up decision-making processes by using an agent as an intelligence layer across all the data and systems they count on every day.
By leveraging generative AI, merchants can streamline data analysis, enhance forecasting accuracy, and reduce the time from design to market. As the retail landscape continues to evolve, those who embrace AI will be better positioned to stay ahead of trends, meet customer demands, and maintain a competitive edge. Shifting the way merchants work with new tools can seem daunting, but the actions retailers take in the short-term will position them for success in decades to come.
Learn more
- Using Copilot in Retail with Microsoft 365 Copilot
- Microsoft Cloud for Retail
- Microsoft’s responsible AI principles
1 Microsoft WorkLab, AI—A Whole New Way of Working.
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Source: Microsoft Industry Blog