Relieving retail's merchandising data stupor with AI-powered analytics

In today's fast-paced retail environment, merchandisers are drowning in data but are parched for actionable insights. 

Their role demands rapid decision-making on a wide variety of issues: product selection, inventory management, promotional strategies, store layout, shelf placement and much more. 

The era of relying solely on intuition and limited data points has long passed. Modern merchandisers have access to a deluge of data, including real-time sales data, customer behavior analytics, market trends, supplier information, and competitive intelligence, all of which they can leverage to optimize their product assortment and placement strategies.

The crux of the problem lies in the fragmented nature of this data ecosystem. Sales figures reside in POS systems, while inventory data is tucked away in ERP platforms. Customer insights are scattered across CRM tools and personalization platforms, while shipping and purchase data lives in yet another suite of applications. 

This is the key challenge for merchandisers, who are retail experts, not data analysts. Without extensive integration efforts from IT teams, this wealth of information remains siloed and challenging to leverage effectively. 

The constant need to switch between multiple systems and interpret disconnected data often leaves merchandisers in a state of data stupor, struggling to extract meaningful insights to make critical decisions in a timely manner.

The limitations of conventional analytics

While business intelligence (BI) tools are critical platforms for retail data analysis, they haven't fully solved the problem. Modern BI platforms can indeed integrate data from various sources using different techniques.

However, traditional BI heavily relies on sophisticated visualization to present insights and requires the creation of numerous dashboards and reports tailored to different aspects of merchandising. 

A single dashboard cannot encompass all the insights needed for decision-making, especially when each merchandiser's data requirements differ from their colleagues'. And while dashboard development doesn’t exactly require a degree in computer science, it does require training and experience to do it well and efficiently. 

The fact is most merchandisers are not equipped to be self-sufficient in dashboard creation or maintenance, even with tools marketed as "self-service analytics." With retail IT resources already stretched thin, endless cycles of dashboard development and an ever-growing backlog of report requests to IT are inevitable. 

Ultimately, traditional BI is overly complex, and static dashboards become just another destination that merchandisers struggle with to make daily decisions. It's evident this approach fails to address the speed, simplicity, and flexibility needed by non-technical professionals to capitalize on enterprise intelligence.

AI-powered analytics

Fortunately, creative applications of generative AI are beginning to address the data stupor problem. By combining generative AI with advanced analytics platforms, complex data can be navigated and understood via natural language, making it more accessible with less effort. 

For example, imagine a merchandiser planning the layout for a new product line. Rather than hunting through a series of dashboards comparing quarterly sales performance of related products, they could simply ask an AI-powered dashboard, "Show me the sales performance of X, Y, and Z products across different store locations in the Southeast region over the past quarter." 

The merchandiser could then dive deeper, asking impromptu follow-up questions, such as, "Break down this data by customer demographics and purchase time." When merchandisers can interact with dashboards using conversational language, they can make better decisions, faster, without depending on a data analyst.

However, generative AI has its limitations. It's built on large language models (LLMs) designed for text processing, not number crunching. When tasked with complex data analysis, generative AI alone may produce errors or fabricate information. 

When generative AI is integrated with a robust BI platform, merchandisers get the best of both worlds: dependable, sophisticated analytics accessed through an intuitive, natural language interface. Further, with modern, containerized architectures, AI-enhanced analytics can be embedded directly into modern enterprise applications. 

This means eliminating "destination dashboards" and allowing merchandisers to capture new insights from the systems they use most. By incorporating AI-powered analytics into ERP, POS, CRM and other applications, retail professionals can effortlessly inquire about sales trends, inventory levels, customer preferences, and market dynamics to optimize their merchandising strategies. 

In addition, this data can be presented in the most suitable format for each merchandiser: charts, tables, or even plain language summaries. This approach not only streamlines workflows and saves time but also enables greater data-driven decision-making across all frontline knowledge workers, which improves product assortments, enhances customer satisfaction, and drives profitability in the competitive retail landscape.

In the future, merchandisers will be able to employ AI capabilities that anticipate their needs based on context, but the current ability to easily request impromptu data insights on-demand is already transforming decision-making processes. The combination of generative with modern BI is the new standard for an intelligent enterprise, providing a competitive edge in the ever-evolving world of retail merchandising.