Retailers face a big problem. Today’s consumers are willing to spend more in-store, but they also have more technology at their fingertips. From retail software-like apps to LLMs, such as Gemini on Android devices, these generations can find the best pricing on exactly what they want to buy. Retailers that overprice will see sales decline; those that underprice risk their products being resold online at a lower price.

As reported by Retail Dive, “Gen Z is projected to spend 18.4% of their consumer-packaged goods and general merchandise budget on private label brands, outspending baby boomers (18.3%), millennials (17.5%) and Gen X (17.2%).” With such shifts on the horizon, retailers need to revisit the foundations of planning. Understanding merchandise financial planning—its challenges and how AI enhances it—is crucial. 

Defining the merchandise financial planning process

Merchandise financial planning is a strategic process used by retailers to align financial targets with inventory management. Its primary goal is to optimize sales forecasting, inventory levels and profit margins by ensuring the right products are available at the right time and price.

This process combines top-down planning and bottom-up planning across assortment planning, open-to-buy budgets and location-level execution. Merchandise financial planning touches everything from category planning to markdown strategy, all tied to performance accountability through ongoing in-season monitoring and pre-season planning checkpoints.

Take the following retail examples where planning processes require substantially more than a simple yes or no:

• Scenario planning for seasonal demand - Imagine you are a fashion retailer planning for the holiday season. Strategic merchandise financial planning processes involve creating multiple "what-if" scenarios. This includes a conservative plan (e.g., if a recession or ongoing tariff issues affect consumer spending), an optimistic plan (e.g., if a specific marketing campaign goes viral) and a most-likely plan. 
• Cross-category cannibalization - A common nuance is how sales in one product category can lead to impulse purchases of other products. For example, if a department store introduces a new line of high-end activewear, teams must account for potential cannibalization of sales from their existing, lower-priced activewear lines.
• Localization of assortments for micro-markets - Even within a single chain, customer preferences can vary significantly by location. For instance, a sporting goods retailer might allocate more cold-weather gear to stores in snowy regions and more swimwear to coastal locations, even if both are within the same general geographic area. This level of detail optimizes local sales performance and minimizes transfers or markdowns of irrelevant inventory.
• Markdown strategy as a proactive tool - Instead of being a solely reactive measure for clearing slow-moving inventory, markdown schedules can be planned proactively for certain product types (e.g., seasonal apparel) to maintain freshness and drive traffic, especially towards the end of a season. This involves calculating "terminal stock" and planning markdown cadence to ensure target sell-through rates are met while maximizing gross margin, rather than waiting for products to become unsellable.

Challenges with traditional merchandise financial planning

Traditional merchandise financial planning often struggles with accuracy, which can be difficult to connect to future business outcomes. A major limitation is the over-reliance on historical data, which often lacks real-time context and fails to account for dynamic market shifts. This limitation hampers a retailer's ability to adapt quickly to demand forecasting shifts, economic trends or unforeseen supply chain disruptions.

Manual planning processes also increase the risk of errors in allocation and replenishment and make it harder to spot missed opportunities in performance analysis. Inaccuracies lead to either overstocking, resulting in heavy markdowns, or stockouts, which drain sales and reduce customer confidence. The whole process then becomes self-propagating, either eating your margin or helping it thrive.

How AI improves accuracy in planning processes

AI-based merchandise financial planning uses vast datasets, including real-time market trends and external variables. In turn, AI-enabled processes offer a more comprehensive and dynamic understanding of consumer behavior. This shift from static historical data to predictive intelligence enables data-driven decisions through, and , and through AI, retailers can:

• Identify subtle patterns - AI uncovers correlations in sales forecasting and shopper behavior that humans or traditional systems might miss.
• Integrate unstructured data - Social trends, news cycles and weather patterns can inform broader scenario planning.
• Provide granular insights - AI delivers product-, store- and segment-level insights, improving assortment planning and shelf-level execution. 

When supported by true omnichannel integration, AI transforms merchandise financial planning processes from a reactive spreadsheet exercise to a forward-looking, proactive system that improves accuracy and margin.

Enhance your merchandise financial planning with invent.ai

Retailers can no longer afford to treat merchandise financial planning as static. With consumer expectations rising and competition tightening, precision and adaptability are non-negotiable. AI doesn’t just improve the accuracy of processes but transforms them. Retailers are now fusing data-driven decisions with real-time responsiveness to protect margin, but not everyone is on a level playing field. Those who adopt AI into their planning today will outperform their competitors tomorrow. 

Connect with an invent.ai team member to see how AI-enabled merchandise financial planning processes have your best interest in mind.