In today’s changing market dynamics, the retail business is getting more competitive by the day. Increasing competition by online marketplaces, along with evolving consumer expectations and shopping patterns are making the category management decisions more complex. As customers’ options increase, so do their expectations. Category management and merchandise planning are at the core of retailing. Assortment planning decisions made by these teams need to be more dynamic and localized as manual analysis based on broad assumptions and averages do not suffice anymore. Managing complexity and maintaining agility becomes the main concern for retailers. Here are the vital capabilities for surviving in the new world of retail.
Digital transformation is taking its place on the agendas of the CEOs and CIOs across the board. While it has been a trending topic across retailers for the last few years, the recent COVID-19 crisis has proven that this is not a “nice-to-have”, but indeed a “must-have” capability in order to stay alive. In the context of category management, this means having integrated workflows between financial planning, assortment planning, initial allocation, replenishment, promotions planning, and markdown optimization.
Implications of the recent crisis on planning, buying, and distributing products have once again highlighted the importance of early detection and fast decision-making processes while understanding the inter-dependencies across different functional teams. For faster decision making, category managers need automated diagnostics such as alerts on delayed receipts, insights into under-performing stores and products, variations in sales performance across channels, and impact of competitor price actions on their demand.
Data Analytics & Automation
In today’s competitive landscape, almost every retailer is competing with Amazon, Alibaba, or some other digital marketplace and/or they participate in it. Keep in mind these companies are built on data analytics and thrive on automation. High-performing retailers with proven track-records are utilizing advanced analytics, artificial intelligence, and automation in driving their planning decisions. They are working with big data to identify and optimize opportunities across different store formats, and different customer segments. They turn insights into more customized product and price offerings and take actions in minutes rather in hours or in days.
Retailers understand they have to keep learning and evolving. Legacy systems supporting siloed decision-making are not keeping up with this new way of doing business. They need advanced solutions with machine learning and automation to detect and execute opportunities for localized assortments.
Localized Assortment Planning
Fashion or specialty retailers selling long lead time, short life-cycle products, need to minimize their risk by making the right decisions upfront or detect risks and opportunities as soon as products start selling. How much to buy and where to position the goods that have 6, or 8 weeks of prime selling period have huge impacts on profitability. Since these products do not have their own sales history, buying and allocation decisions need to take similar items as reference.
Automating similar item selection through attributes or visual recognition brings efficiency and standardization to one of the crucial steps in buy planning. However, finding the right similar product does not necessarily guarantee the right buy quantity. Through machine learning technology, next-generation solutions incorporate factors such as stock-out adjustments, normalizing weather effects, and accounting for promotions and markdowns.
Grocery chains or mass merchants selling long life-cycle products have the advantage of learning from sales data and adjusting the assortments as they gain insights on how customers are reacting. Managing new product introduction, modeling lifecycle, and the substitution effects, and automating the delisting decisions are among the decisions ripe for use of advanced analytics and automation.
Asena Yosun Denizeri is the Head of Retail Solutions at Solvoyo. Asena has more than 20 years of experience in implementing Planning, Pricing, and Optimization solutions in global companies in the U.S. and Europe. She has led cross-functional teams in large-scale implementation projects touching different aspects of Retail Management, including Product Lifecycle Management, Category Planning, Assortment Localization, Demand Planning, Size Optimization, Promotion Planning, Allocation & Replenishment, Markdown Optimization, and Supply Chain Management. Following her consulting tenure with Silicon-valley based software companies, she brought her Advanced Analytics and Business Process Engineering experience to Apparel Retail where she worked at Gap Inc. and Cache. She led Merchandise Planning and Distribution teams to adopt new capabilities in Forecasting, Assortment Localization, and Price Optimization to improve sales and profitability.
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