Industry
Retail
Omnichannel CX and Intelligent Innovation – The Future of Retail
HIGHLIGHTS
- Merchandising responsibilities and goals are expanding beyond the traditional areas of sales, profit, and growth to new areas such as customer experience, faster go-to-market strategies, online item attribution, cross-channel analytics, among others.
- As part of this transition, retailers are restructuring the buyer and supporting team roles in several ways to best serve customers. However, the lack of a single, integrated analytics platform prevents retailers from making faster, better, and intelligence-based decisions.
- Intelligent merchandising solutions empower businesses with the agility to analyze data and generate insights for faster, more informed decisions while delivering value.
On this page
The role of the merchandising function is changing
Even as several internal and external factors rewire core merchandising functions, retailers are restructuring the buyer and supporting team roles in order to best serve customers.
Central to the merchandising function is the merchant, also referred to as buyer, whose key role is to manage the sales and profit of each item. The traditional buyer role centered on selling products and services in physical stores by merchandising the ‘right products in the right stores’, recognizing and reacting to sales trends, designing promotional displays, and measuring item and category performance.
Today, the merchandising function led by the buyer, with support from category managers, pricing managers, replenishment managers, and finance, is experiencing transformation centered on serving the ‘global customer’ in an omnichannel retail landscape that includes marketplaces. Buyers and support team must deliver the right product, across the right format, at the right price, to the right customer, at the right time.
Core responsibilities of the merchandising function have gone far beyond traditional sales, profit, and growth. Newer areas of customer experience, personalization, faster go-to-market strategies, online item attribution and digital asset management, cross-channel analytics and insights, retail media networks (RMNs), and digital marketing effectiveness are taking center stage (see Figure 1). In addition to ever-changing customer expectations, the buyer and support teams must contend with aggressive competition, economic headwinds, geopolitical crises, supply chain disruptions and cost pressures, inflation, and labor uncertainty.
Figure 1
The evolving role of buyers
Figure 1
Executing the buying role and merchandising-related functions requires an understanding of customer behavior, competitor moves, and market conditions in real time. In this transition, retailers are restructuring the buyer and supporting team roles in several ways to best serve customers.
The obstacles that merchandising teams face today
The lack of a single integrated analytics platform prevents buyers and merchandising support teams to make informed decisions with agility.
Buyers and merchandising support teams conduct strategic planning and analysis, monitor supplier compliance, prepare growth plans and annual budgets, and make product-level decisions. Access to real-time internal and external data is crucial for making faster, better, and continuously improving intelligence-based decisions. But today, they lack a single analytical integrated platform to manage their business.
The reliance on traditional siloed, legacy batch processing systems and manual processes is forcing decision makers to extract data from multiple systems to glean insights, conduct analysis, and generate reports manually, resulting in 30-40% non-value-added activities. Without advanced analytics capabilities, merchandising teams cannot perform what-if analysis to simulate business scenarios and assess the impact of different decisions on key business KPIs. The unavailability of role-specific dashboards results in significant overhead costs in managing merchandising workflows as well as missed opportunities in the marketplace.
The lost sales from these operational challenges lead to diminished relevancy, customer abandonment, and loss of competitive advantage. We have observed that retailers are losing significant business (5-8% in sales). In many retail organizations, the C-suite is actively embracing digital transformation and AI and asking, 'What’s next?' to unlock the value in enterprise data and put actionable insights in the hands of commercial decision makers.
Integrated workbench for analysis and discovery
Top-tier intelligent merchandising solutions help reimagine new ways of working with a decision intelligence framework.
By breaking down barriers in tools, processes, and teams, they empower merchandising teams in strategic planning, annual budgeting, ‘what if’ analysis, supplier performance measurement, and ad-hoc analysis, by answering ‘what, why, and how’ questions across many dimensions. They also augment operational human capabilities by automating routine activities; foster cross-functional collaboration; and democratize predictive analytics by putting analytics in the hands of the everyday user in merchandising (see Figure 2).
The benefits include everyday practical AI (actionable insights) through data accuracy and real-time data availability, customizable intelligence through advanced AI, customized user interface, and prioritized and actionable alerts and notifications, and supplier access (through security).
Figure 2
A decision intelligence framework for merchandising teams
Figure 2
An intelligent merchandising solution will allow retailers to pivot to a modern system of engagement and hyperscale operations. We outline the key features of such a system:
- Product management: Accelerating the speed to market to maximize revenue potential
Omnichannel retailers have ever-changing and expanding assortments. They onboard more than 5–10% of their assortment annually, typically taking four to twelve weeks to complete onboarding. On average, around 30–50 suppliers are onboarded annually, depending on the number of categories and size of the retailer. The onboarding process typically takes three to six months to complete. The volume rises manyfold in marketplace models. By leveraging traditional AI and generative AI (GenAI) powered actionable insights for enterprise data quality management, product attribution, enrichment, auto categorization, and workflow automation, retailers can accelerate item onboarding to ensure that the right item is launched at the right time and across the right formats in the marketplace. This will also enable product categories to be GS1 supported for a single version of the truth, maximizing revenue potential while enabling end-to-end product management. - Supplier management: Creating transparency and enhancing collaboration with suppliers
Without a 360-degree view of supplier data and service level agreements (SLAs), purchase orders, payments, and credit notes, managing supplier performance, risk, and compliance can be complex and can also result in litigations. Additionally, because supplier partners do not have access to merchandising planning, budgeting, and analysis systems, there is a need for manual coordination. For example, supplier cost changes, deals, and promotions must be entered into systems manually for each format and is challenging to administer. This increases the time to market for new product introductions. Dashboards, alerts, and notifications help to continuously monitor supplier performance at the item level, with actionable insights on financial and operational impact mitigation. Internal and external parameters can be factored in to generate compliance and risk rating scores—an indicator for supplier reliability. AI chatbots can automate supplier support, improve collaboration, and help execute decisions quickly. - Content enrichment and management: Enabling faster product discovery for consumers
Product content enrichment is a time-consuming and resource-intensive process, prone to errors and data inconsistencies. It includes details like measurements, color variations, related products, images, descriptions, textiles, stock level, delivery, and accessories. Actionable insights on attribute enrichment, product copy and image management, taxonomy derivation, and automated translation bring together data from multiple sources to develop customer insights and model behavior. Maximizing AI-based rich, taxonomy derivation, and automated translation bring together data from multiple sources, allowing shoppers to drill-down into the product hierarchy and understand how the product will fit into their lives. This improves search results, builds trust, increases engagement, and leads to higher sales conversion. - Pricing and promotion management: Executing strategy to deliver profit goals
Retailers are under immense pressure to offer the best price and promotions across key products, at the right time, to delight loyal customers and attract new shoppers. Grocery retailers, in particular, make thousands of price changes and routinely negotiate promotions with suppliers across multiple departments and categories. Close collaboration with suppliers is vital for success. An AI-powered intelligent merchandising solution with optimized workflows for cost or price management will enable merchandising teams with actionable insights to analyze cost changes, evaluate optimal retail price points, and recommend negotiation points, which will come in handy during periodic supplier business reviews. It can enable differentiated localized pricing and provide a real-time view of live and historical price changes to support pricing teams. The system will improve execution, accuracy, and supplier coordination, creating transparency across processes for agility in pricing and promotion strategies. Suppliers, merchants, pricing managers, and finance can easily analyze the impact of retail price changes, price gaps with competition, impact of promotions on KPIs, and monitor promotion program timelines, as well as use automated price reverts to execute promotional strategy and deliver against profit goals. - Category management: Delivering unified, customer-centric experiences
In-store shoppers expect a consistent and seamless buying process that covers their expectations from both online and physical store formats. But many retailers struggle to coordinate activities such as store planogram design, assortment planning, space management, and coordinate design with supplier partners. They also need to coordinate with supplier partners to execute scheduled, seasonal, and exception-based space and range resets in stores. An intelligent merchandising solution with optimized workflow approvals for space and range process management and analytics can provide actionable insights to coordinate planogram drawing, validation, approval, space change management, and distribution to physical stores, enabling merchants to deliver a customer-centric experience. Supplier partners benefit through coordination and transparency with retailers in store planogram design requirements, automated change and approval management, and space and range reset process execution.
Putting theory to action
The real power of intelligent merchandising solutions lies in their ability to take a holistic and integrated view and perform complex, routine, and ad hoc analysis of enterprise data to drive informed decision making.
Here are some examples of how such a system will provide actionable insights to decision makers:
- Scenario 1: A social media phenomenon is causing a drastic rise in demand for a new probiotic weight-loss food category in the Gen Z, millennials, and baby boomer consumer segments. An intelligent merchandising solution can identify the main suppliers and items to be onboarded to secure item placement in store and online, the right timeline for product introduction, the volume requirements to meet estimated demand across each format, the optimal item price points, the marketing plan to reach the targeted segments, and the projected sales and profit.
- Scenario 2: A retail business’ key supplier is based out of Southeast Asia and the region is experiencing adverse weather event. The merchandising system can provide actionable insights to identify a course of action to procure, distribute, and facilitate sales by procuring the inventory needed from secondary sources in Central America, allocating the inventory, and calculating the incremental deliver costs to each distribution center to support merchandise volumes requirements for three top-selling products in the category, securing SLAs for all retail partners.
- Scenario 3: A leading global food retailer is trying to understand the recent market share decline in a top selling food category. Merchants, category managers, and pricing analysts can use auto-generated insights and alerts to answer ‘why’ the market share declined to understand key drivers and anomalies across complex data; the ‘how’ of their pricing actions; and the ‘what’ effect of competitor promotions to discover business drivers of share erosion.
To conclude, the cost of insufficient data, outdated processes, and legacy systems is just too high, and can lead to missed opportunities, plummeting efficiencies, and financial losses for the retailer. Intelligent merchandising offers a data science based approach that broadens the capabilities to predict outcomes and identify patterns, transforming decision-making in omnichannel retail. Through comprehensive assessment and targeted implementation, retailers can fundamentally modernize their approach to data-centric, insights-driven decision-making, empowering the business with agility, to respond faster to market dynamics while maximizing the value from their decisions, and ensuring people and systems work effectively together as technology evolves.
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