Assortment planning is one of the most critical processes in retail, determining exactly which products a store will carry. Traditionally, assortment planning has been a highly manual process, relying on the knowledge and instincts of experienced planners. However, with rapid changes in consumer preferences and the explosion of new data sources, many retailers are finding their current assortment planning processes inadequate. This is where Generative AI comes in.
In particular, generative AI has the potential to completely transform retail assortment planning. Unlike previous AI techniques that relied on rules and structured data, generative AI can rapidly synthesize massive amounts of unstructured data to uncover hidden insights and make highly accurate recommendations. As a result, generative AI promises to automate large portions of assortment planning, freeing up planners’ time while leading to smarter, more optimal assortments.
Key Challenges in Retail Assortment Planning
So what are some of the key challenges in assortment planning that generative AI can help address?
Predicting Customer Demand: One of the hardest problems in assortment planning is predicting exactly what products customers will want to buy in the future. Consumer preferences change rapidly, and guessing wrong can lead to excess inventory or stockouts. Manually extrapolating demand from past sales data tends to be inaccurate. With generative AI combined with Predictive AI, retailers can rapidly analyze billions of demand signals to generate highly accurate demand forecasts.
Localization: Customers in different geographies and store formats have unique preferences. However, traditional approaches struggle to localize assortments. Generative AI can synthesize granular data on local demographics, regional sales patterns and store-specific metrics to automatically optimize hyperlocal assortments.
Omnichannel Complexity: With retail occurring across channels, assortment planning must encompass both online and brick-and-mortar environments. However, this level of complexity is often beyond the scope of manual processes. Generative AI has the scalability to rapidly account for omnichannel dynamics in both demand forecasting and assortment optimization.
Maximizing Assortment Profitability: Retailers must optimize not just for revenue, but also profitability. Factoring in costs, margins, and constraints manually leads to suboptimal assortments. Generative AI and Predictive AI together can rapidly optimize assortments to maximize overall profitability within precise business constraints.
Rapid Market Changes: Consumer preferences are changing faster than ever. However, most retailers only re-plan assortments quarterly or annually, unable to respond quickly enough. Generative AI enables continuous automated assortment optimization that can react rapidly to trends.
Scaling Product Portfolios: As product portfolios grow, the number of potential assortment permutations explodes. It becomes virtually impossible to manually analyze all the combinations to find the ideal assortment. Generative AI can efficiently explore millions of assortment options to uncover optimal solutions.
Lack of Transparency: Traditionally, assortment decisions have been driven more by executive opinion than data. This leads to suboptimal assortments and lack of visibility into the decision-making process. With generative AI, retailers can leverage massive data instead of guesswork, bringing added transparency.
Siloed Data: Relevant assortment data often resides in disconnected systems like inventory, POS, CRM, marketing, pricing and more. Manually aggregating this data together is infeasible. Generative AI power retail merchandise planning software can synthesize data from all these silos to optimize assortments holistically.
Social Media and Retail Assortment Planning
One particularly exciting application of generative AI is using social listening data to optimize assortments. Social listening broadly encompasses analyzing conversations across social platforms to understand customer sentiment, trends, and product feedback.
Here are some of the key ways generative AI can apply social listening to assortment planning:
By leveraging these social listening capabilities, generative AI gives retailers data-driven, forward-looking inputs into assortment planning that were never before possible. This helps retailers make smarter decisions, save time and resources, and create differentiated assortments that perfectly align with customer needs.
The Generative AI Opportunity
It’s an incredibly exciting time for generative AI in retail assortment planning. After decades of assortment planning relying primarily on the intuition of planners, generative AI solutions like OmniThink.AI finally bring data-driven science to this critical process.
By synthesizing billions of demand signals in real-time, generative AI solutions can automatically optimize hyperlocal assortments at a scale, speed, and granularity impossible through manual approaches. Rather than just digitizing antiquated processes, generative AI represents a true transformation.
With generative AI, retailers can:
The result is happier customers, lower inventory costs, fewer missed sales, and most importantly, sustainable competitive advantage through smarter assortments. In a market where product differentiation is increasingly difficult, your assortment may be your only true point of differentiation. Is your planning process up to the challenge?
With generative AI, the future of retail assortment planning looks brighter than ever. The time for retailers to embrace this transformation is now.
