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AI Driving More Effective Assortments for Retailers

By Robert Hetu | October 26, 2020 | 0 Comments

Retail TrendsMerchandising ProcessRetail Digital Transformation and Innovation

Why Retail Assortment Optimization Applications are Critical

AI will drive more effective assortments for retailers. Product selection, availability and pricing remain the heart of retail business; however, accuracy of the location distribution plan and resulting procurement and consistency of best-practice execution have never been more urgent. Diverse fulfillment and expanding touchpoints will increase inventory requirements, unless the entire planning process is orchestrated leveraging of advanced analytics and extensive data sources. Effective inventory management will enable dramatic improvement of free cash flow for digital investments. Key to this is the elimination of excessive safety stock, promotional overbuying and the resulting dead inventory, where manual processes and gut feel no longer are enough.
Customers’ expectations of a unified retail commerce experience continue to challenge multichannel retailers as they pursue digital business transformation. Digital business transformation in the retail industry includes the usage of cloud, mobility, social engagement, mixed reality, algorithms, AI and the Internet of Things (IoT) to connect and capitalize on existing selling channels. Recently, the impact of a global pandemic (COVID-19) has accelerated many of the technology-driven digitalization trends. Taking advantage of the digitalized store base, in conjunction with diverse touchpoints, is critical for a retail experience that delights customers. However, this requires a significant refinement of the assortment offered across touchpoints as well as high-quality execution. RAOAs (Retail Assortment Optimization Applications) help to facilitate this transition.  

The RAOA functional “footprint” primarily covers 12 optimization types that augment planning for both short and long life cycle products, including:

  • Customer decision trees — Graphical records leveraged to understand consumer buying habits and the decision-making processes followed by individuals while shopping a category.
  • Customer needs identification — Gathering the voice of the customer in the initial creation process.
  • Market basket analysis  Support for analysis of customer baskets as part of the assortment optimization process.
  • Unified assortment planning  Planning assortments within and across customer touchpoints to support unified retail commerce.
  • Pre-period sales analysis  Supporting the sales analysis process for early planning activities.
  • Preseason plan creation/seeding — Initial plan development using analytics; often occurring up to 12 months in advance of active periods in certain categories.
  • Product attributing — Identifying the attributes of the products planned for the assortment.
  • Product selection  Determining the products that will be included in the assortment.
  • Size and pack optimization  Ensuring that multisize products are optimally ordered by size and case pack configuration.
  • Store clustering — Analytic approaches for creating store groups by using a wide array of possible attributes.
  • Transferable demand analysis (halo, cannibalization and substitutability) — Understanding the inter-item customer demand implications of changes to any one item in a common category.
  • Visual merchandising — Leveraging product images during the assortment planning process. (Does not include detailed planogramming covered in an alternate Market Guide.)

AI in RAOA

Retailers are aware of the significant opportunity to leverage vast amounts of customer data to drive in-depth customer analytics that enhance merchandising processes. The goal is to create customer-centric assortments across every digital and physical touchpoint, optimally priced and available to consumers when and where they expect to browse, transact and acquire items. RAOAs offer increasingly advanced capabilities that can deliver more accurate predictions. Gartner research What Retail CIOs Need to Know About AI for Merchandising provides a robust view of merchandising use cases for AI. AI is leveraged in combination with advanced analytics, control parameters and algorithms to improve accuracy of optimizations. This figure shows the most common AI techniques used by every vendor tracked in our recent market guide for RAOA.

Graph of AI capabilities leveraged by RAOA vendors

The New Retail Scenarios  Provide an in depth look at the future of consumerism and how technologies such as AI and RAOA will play a vital role in supporting the future retail mission.

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