Smart Merchandising: The ground zero for Retail AI
Smart Merchandising is increasingly providing consumers with their desired products at the right price and through their preferred channels, all while bridging the digital divide. Integrating AI in merchandising is key to positioning customer-centricity at the heart of eCommerce omnichannel retailing.
The potential for AI-driven merchandising
With the current eCommerce omnichannel complexity overwhelming traditional merchandise managers, AI-driven merchandising challenges retailers to broaden their scope beyond merely the selection, curation, and organization of products. As a sign of the shift to data-driven decision-making, 58% of consumers are expected to shop more online in the aftermath of the pandemic than before, underscoring the potential for AI adoption in merchandising.
Merchandisers working collaboratively with AI can now make decisions based on real-time data-driven Customer Experience (CX) that would then influence future merchandising decisions. Through iterative A/B testing involving large volumes of high-quality data, AI adoption can enable accurate customer segmentations that capture the nuances of buying behavior, allowing merchandisers to provide consumers with personalized search to enable precise product retrieval.
Through predictive analytics, AI can process large volumes of data to generate precise demand signals and forecasts at a granular level, determining the relationship between products and dynamically grouping them based on similar attributes. By meeting consumer needs through efficient merchandising, processes such as item setup, order management, and vendor inquiries can also be automated.
Moving beyond a black-box approach to automation
Moving beyond a black-box approach to automation using image recognition and NLP, retailers can extract product attributes and optimize product assortment strategy that forecasts assortment using predictive analytics based on user behavior providing meaningful outcomes across the value chain.
Intelligent Search and Recommendation Engine
AI adoption can solve the problem of relevance and encourage product discovery using context-sensitive semantic search to inform retailers about consumer intent moving beyond text match algorithms that work with keywords typed into the search bar. Effective taxonomy classification and mapping can enable the discovery of products through intelligent search within a catalog using multiple taxonomies or constantly optimizing taxonomies.
Intelligent search to encourage product discovery
While faceted search and navigation and typeahead search allow retailers to make compelling recommendations using multiple variables, visual product discovery accounted for 50 percent faster checkouts compared to keyword search and 20-30% increased conversion rates, emphasizing why retailers need to shift their focus towards those areas. With almost all retailers agreeing on the vital role of bespoke product recommendations and nearly one-third stressing that it is extremely important to their business, curated catalogs with high-quality, tailored attributes and NLP-driven search are key to enabling this recommendation engine.
Bridging in-store and online experiences through Visual Merchandising
Visual merchandising involves designing visually appealing sales floors and stores to attract customers through optimized location characteristics and planograms. AI can greatly augment the capabilities of brick and mortar stores by money mapping (mapping the sales floor and the exact location of products) the entire sales floor through Radio Frequency Identification (RFID) tags and AI localization to create heat maps of the store.
Design efficient planograms through visual merchandising
Stores can also employ smartphones to provide a more immersive shopping experience by integrating BLE beacons, NFC, and QR codes. Through AI, retailers can adopt newer and more optimized metrics for evaluating performance by replacing sales per sq. ft. with CX per sq. ft. Classification algorithms can help with store-clustering based solely on local demand. AI-based algorithms like simulated annealing help in making efficient planograms and perform store-specific space optimization as a result of smart merchandising.
Using AI to categorize products into collections based on season or sale and pairing them creatively are formidable merchandising strategies available to enterprises that can help simplify shopping by helping retailers sell curated eCommerce omnichannel experiences to customers.
An AI-driven agile merchandising approach involves not just predicting future trends but responding to them as they emerge. It allows category managers to curate catalogs by predicting trends and their longevity and also enabling them to differentiate between fads and trends and stay ahead of them.
Understanding intent through NLP
Category managers can harness information about consumer behavior obtained from a variety of sources to collate them and produce actionable intelligence to perform category optimization using ML and NLP. Category managers can also use AI to pair content and digital assets like embedded videos and tailor them to individual preferences.
Building brand experiences
Brand experiences are a function of the choices merchandisers make in terms of the look and feel of websites, UI, navigation, layout, and so on. Merchants can distinguish their value props from those of their competitors by creating a narrative tailored to each brand by emulating the look and feel of in-store experiences online.
With purchase decisions being made in 100 milliseconds, the pivotal role of product images in guiding 67% of online consumers cannot be overstated. This data should inform the quality of the product images, the specific design, and aesthetics. It also highlights the need to infuse creativity in merchandising, factors that set one brand apart from another.
AI-powered product imagery without cumbersome and cost-intensive product photoshoots
The Taskmonk Advantage
According to Steve Gordon of Antuit, “Companies that have embedded AI into their systems have experienced up to 6% improved gross margin and a 10% increase in sell-through.” Retailers who augment their eCommerce enterprises through AI adoption require high-quality labeled data to perform these functions effectively. Although AI models can even work with sparse and missing data, there can be no substitute for high-quality labeled data.
With only 20% of retailers confident in their ability to deliver merchandising solutions in-house, platforms like Taskmonk provide the wherewithal to perform data labeling solutions that are at the core of the successful deployment of AI.
The single source of truth for human-in-the-loop labeled data
Taking a SaaS approach to merchandising is a scalable and faster solution for smaller teams so that AI adoption is driven by a business logic that is retailer-specific while appealing to shoppers on a logical and emotional level.
AI is not a silver bullet to solving the challenges faced by merchandisers and needs to be supplanted with human-in-the-loop expertise. Taskmonk’s human-in-the-loop data labeling solutions aim to create a source of truth for merchandising solutions using AI. Thinking beyond rules-based merchandising, retailers need to balance ease of deployment and distinctiveness, allowing teams to align objectives and strategies.