Assortment Optimization: Helping Retailers Make Smarter Merchandising Decisions with Diwo

Introduction to Assortment Optimization

In today's competitive retail environment, offering the right products in the right quantities at the right locations is essential for meeting customer demand and managing inventory effectively. Retailers must decide which products to sell, how much inventory to purchase, where to allocate stock, and how to adjust their assortment as customer preferences and market conditions change. Assortment Optimization helps businesses make these decisions by analyzing demand patterns, sales performance, inventory information, pricing, and other relevant business factors.

A poorly planned assortment can leave retailers with excess stock in some stores and product shortages in others. These imbalances can contribute to markdowns, missed sales opportunities, inefficient inventory allocation, and lower inventory productivity. Traditional approaches that rely heavily on historical averages or broad store categories may also miss important differences between customer groups, locations, and sales channels.

Modern assortment planning requires more than reviewing reports after sales have occurred. Retail teams need to understand how demand varies, identify where planned purchases differ from expected demand, and evaluate alternative decisions before committing to orders.

Diwo addresses this challenge through its decision-intelligence approach. Its Catalyst platform supports assortment optimization by helping merchandising planners examine product-level demand, compare planned assortments with demand signals, and explore potential changes through quantified what-if analysis. By connecting business data with relevant context, Diwo helps retail teams make more informed assortment and buying decisions.

What Is Assortment Optimization?

Assortment Optimization is the process of determining which products a retailer should offer and how those products should be distributed across stores, channels, and customer segments to support business objectives.

It involves evaluating factors such as historical sales, product availability, customer preferences, seasonality, pricing, promotional plans, inventory levels, and expected demand. The goal is to develop an assortment that reflects the needs of each market while considering financial and operational constraints.

For example, a fashion retailer may discover that certain products perform better in coastal cities than in inland locations. A beauty retailer may find that product preferences differ between high-traffic urban stores and smaller regional outlets. A grocery business may need to adjust its assortment based on local purchasing patterns, seasonal demand, and promotional activity.

A centralized assortment plan based only on broad averages may not capture these differences. Assortment optimization allows retailers to evaluate product decisions at a more appropriate level of detail, such as the individual SKU, store, and sales channel.

Successful optimization does not simply mean stocking more products. It means selecting and allocating products more thoughtfully so that the assortment reflects customer demand, the retailer's commercial strategy, and practical constraints.

Why Assortment Optimization Matters for Retail Businesses

Reducing Overstock and Understock

Overstock and understock are common challenges in retail inventory management. Excess inventory can increase carrying costs and create pressure to discount products, while insufficient inventory can limit sales when customers want to purchase an item.

Assortment optimization helps retailers compare their planned purchases and allocations with expected demand. By examining differences across products, stores, and channels, teams can identify where inventory may be excessive or insufficient before purchase orders are finalized.

The goal is not to eliminate every inventory discrepancy, since demand is uncertain and supply conditions can change. Instead, it is to improve the quality of purchasing and allocation decisions using the most relevant information available.

Improving Product Availability

Customer expectations vary by location, season, shopping behavior, and product category. A product that sells consistently in one store may perform differently in another.

Assortment optimization helps retailers examine these variations and determine whether product availability aligns with local demand. Better-informed allocation decisions can improve the likelihood that appropriate products are available where customers are most likely to need them.

Retailers can also assess whether their existing store groupings accurately represent demand patterns or whether more detailed product and location analysis is necessary.

Supporting Profitability and Margin Management

Retail profitability depends on the relationship between sales, purchase costs, product mix, discounts, and inventory efficiency. A product may generate strong sales but deliver limited margin, while another product may have lower sales volume but contribute meaningfully to profitability.

Assortment decisions should therefore consider financial outcomes rather than volume alone. Retailers can evaluate how changes in product quantities, assortment composition, and pricing assumptions may affect revenue and other relevant business indicators.

By examining these factors before committing to a buy, businesses can make better-informed decisions about which opportunities deserve attention.

Adapting to Changing Customer Demand

Customer preferences can change because of seasonality, economic conditions, marketing campaigns, changing trends, or new competitors. An assortment plan based exclusively on last year's performance may not reflect current market conditions.

Retailers need to combine historical sales with more recent demand signals and relevant business context. This can help merchandisers recognize where past patterns remain useful and where new information suggests that the plan should be reconsidered.

Assortment optimization creates a structured way to evaluate these changes and adapt buying decisions as new information becomes available.

Common Challenges in Traditional Assortment Planning

Relying on Historical Averages

Historical averages provide useful information, but they can hide meaningful differences between stores, products, and channels. Applying the same allocation assumptions to groups of stores may result in a plan that does not reflect local demand.

Retailers need to understand when aggregated views are sufficient and when decisions require a more detailed level of analysis. SKU-store-channel evaluation can reveal differences that broad averages might obscure.

Working with Disconnected Data

Assortment planning often involves information from sales systems, inventory applications, purchasing records, pricing tools, promotional calendars, and spreadsheets. When these sources are disconnected, planners may need to combine information manually before they can evaluate a decision.

Manual data preparation can delay analysis and make it difficult to maintain a consistent view of the business. An effective decision-intelligence approach connects relevant data with its business context so that users can understand not only the numbers but also the factors affecting them.

Delayed Decision-Making

Retail buying decisions are often time-sensitive. A merchandising team may identify a demand shift but still need to prepare reports, reconcile assumptions, arrange review meetings, and seek approvals before changing the plan.

By the time a decision is finalized, the opportunity to act may have narrowed. Connecting analytical insights with decision workflows can help teams investigate important changes more efficiently and evaluate options while they are still relevant.

Limited Scenario Analysis

Traditional planning processes may make it difficult to evaluate multiple alternatives quickly. Merchandisers might want to know what would happen if units were shifted between store groups, prices were adjusted, or a marketing calendar changed the demand outlook.

Without accessible what-if analysis, these questions may require additional spreadsheet work or analyst support. Decision-intelligence tools can make scenario evaluation more accessible by helping users explore possible changes and examine their modeled business impact.

Key Factors to Consider in Assortment Optimization

Historical Sales and Demand Patterns

Sales history provides an important starting point for understanding product performance. Retailers can analyze sales by SKU, store, channel, customer segment, and time period to identify recurring patterns and unusual changes.

However, historical sales should be interpreted carefully. Low sales may reflect weak demand, but they may also result from stockouts, limited product visibility, pricing changes, or other constraints. Effective assortment planning should distinguish observed sales from the demand the retailer was potentially able to serve.

Product and Store-Level Differences

A product's performance can vary significantly across locations and channels. Product assortment, purchasing power, climate, customer demographics, local competition, and store format may all influence demand.

A useful optimization strategy considers these differences rather than assuming that every location needs the same product mix. It also reviews whether the available data is sufficiently detailed to support the desired level of decision-making.

Inventory and Supply Constraints

A purchasing recommendation must account for more than estimated demand. Retailers must also consider current stock, purchase commitments, supplier lead times, minimum order quantities, delivery schedules, and available budgets.

These constraints influence which buying decisions are practical. A demand opportunity may be commercially attractive but impossible to fulfill within the required timeframe, making constraint-aware planning essential.

Pricing and Promotional Activity

Prices and promotions can influence both the quantity of products customers purchase and the timing of demand. Promotional calendars, planned discounts, product launches, and marketing initiatives should therefore be considered when developing an assortment and buying plan.

When these signals are evaluated early enough, merchandisers can assess whether planned inventory aligns with the expected commercial activity. Any projected impact should be treated as an estimate based on the model's assumptions rather than a guaranteed result.

How Assortment Optimization Improves Retail Buying Decisions

Moving from Broad Store Groups to SKU-Level Decisions

Some retailers organize purchasing decisions around store groups because this simplifies planning. However, grouping stores can hide important differences in product demand.

A more detailed approach considers purchasing requirements at the SKU-store or SKU-channel level. This enables planners to evaluate which products need different quantities in specific locations rather than relying entirely on an average allocation.

Diwo's Assortment Optimization use case illustrates this approach through product-level buying questions, comparisons between planned assortment and demand, and the evaluation of allocation changes.

Comparing Planned Assortment with Expected Demand

A critical step in assortment planning is identifying the gap between what a retailer intends to purchase and what available demand signals suggest may be needed.

Comparing the plan with expected demand can reveal products at risk of being overbought or underbought, as well as differences between locations that may warrant further investigation.

These findings help merchandisers focus their attention on decisions that matter most. Instead of reviewing every product with equal priority, teams can investigate the largest or most commercially relevant discrepancies first.

Evaluating Alternatives Before Purchase Orders

Before committing to a purchase, buyers often need to compare different options. They may consider shifting units between stores, changing planned quantities, adjusting prices, or accounting for new marketing information.

Scenario analysis allows teams to explore these alternatives and estimate their potential effects under different assumptions. This can improve the decision process by making trade-offs more explicit.

The results should still be reviewed against operational constraints, forecast uncertainty, financial targets, and practical supply conditions before a final commitment is made.

How Diwo Supports Assortment Optimization

Diwo approaches assortment optimization through decision intelligence, connecting business data, analytical findings, and decision workflows to help merchandising teams determine what action to consider next.

Its Catalyst product is designed to let users explore business questions conversationally and investigate the factors behind product and demand patterns. Within Diwo's assortment optimization use case, this approach is applied to SKU-store-channel buying decisions.

Demand-Driven Buying Recommendations

Diwo Catalyst supports the evaluation of planned assortment against demand signals, helping buyers investigate where planned quantities may diverge from expected requirements.

Instead of depending exclusively on store-group averages, planners can explore product and location differences in greater detail. This helps identify which products and allocations may require attention before purchase decisions are finalized.

The usefulness of the recommendation depends on the quality and coverage of the data available, as well as the assumptions used in the underlying analysis.

Conversational What-If Analysis

Merchandising planners regularly ask practical questions about how a change in product quantity, location allocation, or price might affect business results.

Diwo's use case demonstrates how planners can investigate such questions through conversational interactions and quantify modeled outcomes. For example, a buyer can examine the potential implications of shifting units from one store group to another or adjusting a product's planned price.

This enables teams to compare alternatives and understand the reasoning behind a possible decision before taking action.

Incorporating Business Context

Sales and inventory data alone may not explain every important change in demand. Retail decisions can also be influenced by marketing calendars, product launches, customer behavior, seasonal events, and other unstructured business information.

Diwo's assortment optimization approach describes the use of a Semantic Knowledge Graph to connect structured sales information with relevant unstructured context. This can help provide a more complete view of the factors that influence a decision.

Bringing context into the analysis is especially valuable when buyers need to understand not just where the plan differs from demand, but why that difference may matter.

Prioritizing the Most Important Decisions

Retail teams often have more potential issues to review than they can investigate at once. An assortment decision-intelligence approach helps direct attention toward the discrepancies and opportunities most relevant to business outcomes.

By examining the expected impact of potential changes, merchandisers can prioritize the decisions that deserve further review. Prioritization should consider the confidence of the analysis, the feasibility of the proposed action, and the consequences of getting the decision wrong.

Connecting Insights with Business Action

The purpose of assortment analytics is to improve buying and allocation decisions, not merely to produce another report. Decision workflows are most valuable when relevant findings can be reviewed, validated, and incorporated into the systems and processes where retail teams work.

Diwo's Catalyst use case focuses on helping planners move from demand analysis and scenario evaluation toward more informed purchase decisions. The precise workflow depends on the retailer's data environment, governance requirements, and operational processes.

Learn more on Diwo's official Assortment Optimization page.

Industry Applications of Assortment Optimization

Fashion and Apparel Retail

Fashion retailers must plan assortments around changing trends, seasonal collections, store locations, and product attributes such as size and color. Demand can vary significantly by region, making allocation decisions particularly important.

Assortment optimization helps merchandisers evaluate which products should be available in particular locations and how quantities should be distributed. It can also support the evaluation of markdown risks and seasonal buying alternatives.

Beauty and Personal Care

Beauty retailers manage products across price points, categories, brands, and customer preferences. New product introductions and promotional campaigns can further complicate buying decisions.

By analyzing product performance and differences in demand across locations and channels, retailers can evaluate whether their assortment is aligned with local requirements. They can also consider how upcoming promotional activity should influence planned quantities.

Grocery and Consumer Packaged Goods

Grocery and consumer packaged goods retailers often manage large product ranges with differing demand patterns, replenishment needs, shelf constraints, and expiry considerations.

Assortment optimization can help evaluate which products are appropriate for different store formats and how purchasing decisions reflect local customer needs. Depending on the available data and capabilities, retailers may also incorporate inventory constraints, seasonal demand, and promotional activity into their planning.

Electronics and Home Retail

Electronics and home retailers face decisions involving products with different price points, buying cycles, specifications, and availability constraints. Demand may vary by local market, product launches, or promotional periods.

Analyzing product performance across locations and channels helps merchandisers understand where assortment changes may be worth investigating. Scenario analysis can support comparisons between different product quantities, allocations, and pricing assumptions.

Best Practices for Successful Assortment Optimization

Establish Clear Commercial Objectives

Before changing the assortment, define the outcomes the business is trying to improve. These might include product availability, sales, gross margin, inventory efficiency, markdown exposure, or customer choice.

Clear objectives help teams choose appropriate metrics and evaluate trade-offs between competing priorities.

Use Reliable and Relevant Data

An effective assortment plan depends on accurate, timely, and sufficiently detailed information. Retailers should review product identifiers, store mappings, stock records, sales history, pricing data, and promotional calendars before relying on analytical recommendations.

Teams should also document data limitations and distinguish between observed sales and estimated demand where stockouts or other constraints may distort the picture.

Evaluate at the Right Level of Detail

Store-group analysis may be sufficient for some decisions, but it may be too broad for products with sharply different demand patterns. Retailers should choose the level of analysis that reflects business needs and data availability.

More detailed analysis is useful when it reveals actionable differences, but additional complexity should be balanced against operational feasibility.

Include Business Users in the Decision Process

Merchandisers and buyers understand practical constraints that may not be fully represented in a dataset. Their experience can help interpret analytical recommendations and identify issues requiring further consideration.

Combining data analysis with business judgment creates a stronger decision process than relying exclusively on either manual intuition or automated recommendations.

Track Outcomes and Improve Over Time

Retailers should compare planned decisions with actual results and investigate meaningful differences. Relevant measures may include forecast accuracy, stock availability, markdowns, inventory turnover, sales, and margin, depending on the objective.

Reviewing these outcomes helps teams refine planning methods, improve assumptions, and identify where additional data or process changes may be needed.

The Future of Assortment Optimization

The future of assortment optimization will increasingly involve connected data, predictive analytics, artificial intelligence, and decision-intelligence tools that help retail teams evaluate commercial choices more efficiently.

AI-assisted interfaces can make it easier for users to explore business data and ask questions in natural language. Predictive techniques can support demand estimation, while scenario analysis can help teams examine the potential consequences of changes in buying quantities, product allocation, and pricing.

These technologies are most useful when they are connected to trustworthy data and practical workflows. Retailers must still consider uncertainty, supply constraints, business policies, and the risks associated with automated decisions.

Decision intelligence represents a broader shift from simply presenting analytics to helping teams identify, evaluate, and act on important business opportunities. For merchandising teams, this means connecting demand signals and business context to the decisions that shape assortment and purchasing.

Diwo's decision-intelligence platform focuses on turning enterprise data into recommendations and decisions. Its assortment optimization use case demonstrates how this approach can be applied to retail buying questions and product-level allocation decisions.

Conclusion: Make Smarter Retail Decisions with Assortment Optimization and Diwo

Assortment optimization helps retailers determine which products to offer, how much inventory to purchase, and where products are most likely to meet customer demand. By considering product performance, location differences, inventory constraints, pricing, and promotional activity, retailers can build a more informed approach to merchandising and buying.

Traditional planning methods that depend on broad averages or manually assembled reports may not provide enough detail to evaluate every important decision in a timely manner. A data-driven approach can help teams identify demand discrepancies, compare alternatives, and prioritize the actions that deserve further attention.

Diwo supports this process through its decision-intelligence approach and Catalyst's assortment optimization use case. By enabling SKU-level buying analysis, conversational what-if exploration, and the integration of relevant business context, Diwo helps merchandising teams investigate decisions before committing to purchase plans.

Ready to make more informed assortment decisions? Explore how Diwo can help your retail team connect business data with practical buying insights through its Assortment Optimization use case.