Successful approaches to product portfolio optimization for companies

In today’s fast-paced business world, optimizing the product portfolio is essential for a company’s success. Product portfolio optimization involves structuring a company’s offerings to focus on products with the highest value and profitability. However, a simple ABC analysis is no longer sufficient to meet today’s market demands. Modern data sources, such as ERP, CRM and CPQ systems, provide far deeper insights, enabling even more effective optimization of the product portfolio.

The ABC analysis as a starting point for portfolio analysis

The ABC analysis is considered one of the simplest and most commonly used methods for evaluating and prioritizing products within a portfolio. Based on the Pareto Principle - also known as the 80/20 rule - it categorizes products into three groups:

• A-Products: These are the most valuable items in the portfolio, typically accounting for around 80% of revenue while comprising only about 20% of the overall portfolio. These products are often the main drivers of company success and should therefore receive special attention.
• B-Products: These products have a moderate share of revenue and often represent a significant portion of the portfolio, though they do not reach the same revenue levels as A-products.
• C-Products: These products contribute the least to revenue but are sold in high volumes. While they add less to overall profitability, they are often crucial for customer acquisition and high-volume sales.

An ABC analysis provides a quick and straightforward way to structure the product portfolio and identify key products that deserve closer examination. It thus serves as a solid starting point for deeper portfolio analyses and optimizations. However, its focus on metrics such as revenue, sales volume, or contribution margin is too narrow to achieve comprehensive portfolio optimization. Critical factors such as customer needs, market trends and the strategic roles of products - such as "door openers" or complementary products - are not taken into account.

ABC matrix: Comparison of customers to products

Simply expanding product portfolio analysis to include a second dimension - categorizing not only products but also customers using the ABC approach - highlights the limitations of basic analyses. This two-dimensional ABC matrix combines the economic significance of individual products with the specific needs and behaviors of customers. It provides a nuanced overview that clearly identifies areas for optimization within both the customer and product portfolios.

Considering the customer perspective is crucial for developing a comprehensive understanding of market and customer relationships. By classifying customers based on their value and needs, companies gain deeper insights into purchasing behaviors and preferences of their target audiences. This enables targeted communication, improved customer relationships and identification of growth opportunities, ultimately leading to more effective resource allocation and strategic decision-making.

Key aspects of considering the customer perspective include:

• Understanding customer needs: A separate analysis of customers allows for identifying specific requirements and behaviors associated with particular products. This supports targeted product development, marketing strategies, and optimization of the product portfolio.
• Optimizing customer reelationship: By analyzing customers in relation to products, companies can better meet the needs of their most valuable customers and strengthen loyalty. For instance, A-customers can receive prioritized attention, while C-customers are managed more efficiently.
• Identifying growth opportunities: Companies can discover which products are preferred by B- and C-customers and take targeted actions to motivate these customers to increase purchases or shift towards higher-value products.
• Efficient resource allocation: Considering the customer perspective enables companies to allocate resources where they will have the greatest impact, rather than distributing them evenly across all products and customers.

Analyzing the interactions between customers and products provides companies with valuable insights that enable targeted strategy optimization. By comparing these two dimensions, companies can identify how customer purchasing behavior and preferences impact the product portfolio. This nuanced perspective aids in developing tailored approaches for each customer group and in strategically deploying resources.

The following overview demonstrates how various combinations of products and customers can inform strategic decision-making:

• A-products with A-customers form the most crucial combination, generating high revenue. The focus here is on maintaining strong relationships and ensuring product availability.
• A-products with B-customers present opportunities for upselling and encouraging the purchase of A-products.
• A-products with C-customers require analysis to determine how these customers might be guided toward higher-value products.
• B-products with A-customers aim to increase purchase frequency, while B-products with B-customers offer growth potential but are a lower priority.
• C-products with C-customers represent the lowest priority, where resource efficiency and utilization are the primary focus.

The two-dimensional analysis provides a more nuanced perspective on the product portfolio and customer relationships, enabling more informed decision-making and effective portfolio strategies. However, despite the valuable insights gained from comparing customers and products, this analysis alone is not enough to support comprehensive strategy development. Such an approach primarily addresses external aspects - what is purchased and by whom. Important factors like synergies, cross-selling effects, and product lifecycle stages remain unaddressed.

While the analysis reveals customer preferences for specific products, it overlooks the complex interdependencies within the company’s internal structure, where true complexity lies. Efficient resource allocation and maximizing synergies are essential for long-term success.

A thorough understanding of internal processes, the product portfolio’s composition, and customer relationships is required to make strategic decisions that have both short-term impact and sustainable effects. Only when companies integrate both external market conditions and internal structures into their analyses can they fully unlock potential and develop an effective, cohesive strategy.

Approaches to product portfolio optimization using large data sets

While ABC analyses (one- or two-dimensional) provide a good starting point, numerous other approaches allow for a much more informed optimization of the product portfolio. Various data sources play a crucial role in this process; in particular, the ERP system lays the foundation for further analyses.

Below are the key systems for product portfolio management:

1. Multidimensional analyses with ERP
   The ERP system (Enterprise Resource Planning) provides essential data that is crucial for product portfolio optimization. It enables comprehensive tracking of revenue, costs, and margins for each product. These metrics form the basis for analyzing financial performance and help identify products that generate high revenue but have low margins or incur high costs. Additionally, the ERP system contains information about product architecture, which can be interesting for similarity assessments. This information allows products to be grouped based on common attributes or components, which in turn fosters the identification of cross-selling potential and the optimization of product variants.
2. Customer behavior and CRM data
   The CRM system (Customer Relationship Management) complements the data from the ERP system with valuable insights into customer behavior. It captures data about which products are popular among specific customer segments and provides insights into purchasing patterns and customer preferences. This information is critical for understanding which products can potentially be sold together and where cross-selling opportunities exist. By analyzing this data, companies can align their product strategies with customer needs, thereby increasing customer loyalty and revenue.
3. CPQ data for customized offers
   In industries with highly customized products, the CPQ system (Configure, Price, Quote) plays a central role. It provides information on the configuration and pricing of product variants. The data helps identify which product variants are in high demand and at what prices they are sold. These insights enable companies to dynamically optimize the product portfolio and respond to the actual needs of customers. This ensures that not only standard products but also the most sought-after variants remain in the portfolio.

Reading recommendation: In our article modern product portfolio analyses for data-driven portfolio management, we explain how data-driven portfolio analyses are essential for meeting the complex demands of the modern market.

Phase-Out of product variants: Criteria for informed decision-making

Given the extensive data obtained from ERP, CRM, and CPQ systems, these can be effectively utilized in the context of a structured phase-out process for product variants. The product information from these IT systems can be crucial in developing a targeted optimization analysis that enables companies to systematically identify inefficient products and streamline their portfolios.

For the phase-out process of a product variant, product group, or product family, the following metrics provide valuable guidance:

• Revenue and growth: Products with stagnant or declining sales are candidates for phase-out.
• Margin and contribution margin: Products with low margins negatively impact profitability.
• Costs: High manufacturing, storage, or distribution costs indicate inefficient products.
• Inventory turnover: Low sales frequency ties up capital and increases inventory costs.
• Product lifecycle: Products in the maturity or decline phase offer little potential.
• Customer satisfaction: Decreasing demand or poor reviews indicate a lack of relevance.
• Sales focus: Products without cross-selling potential have lower strategic importance.
• Market trends: Obsolete products or declining market trends reduce competitiveness.
• Need for innovation: High investment needs with low returns suggest phase-out.
• Customer segments: Products that serve only small niches should be evaluated for profitability.

However, it is important to emphasize that considering these criteria presents a multifaceted challenge. A wide range of criteria exists, and their relevance can vary significantly depending on the company. Therefore, it is not always sensible to incorporate all factors into analyses and optimization processes. What is critical for one company may not be for another. Nevertheless, certain standards and best practices can serve as a foundation to support informed decision-making while taking the specific needs of the company into account.

Portfolio streamlining using decision trees

The effective utilization of data from ERP, CRM and CPQ systems can significantly enhance the comprehensive optimization of the product portfolio through the application of decision trees. Decision trees are a form of data analysis that enables companies to identify complex relationships among various factors, thereby reducing organizational complexity.

Here’s how it works:

1. Data aggregation: Initially, relevant data from various systems is aggregated to create a comprehensive data foundation. This includes sales data from the ERP system, customer preferences from the CRM, and configuration data from the CPQ system.
2. Model development: Next, a decision tree model is developed. This process involves analyzing historical sales data and identifying patterns that indicate which products are successful in specific situations or among particular customer segments. 
    
   • A possible logic for structuring a decision tree could initially filter by revenue, quantity, or margin, similar to the ABC analysis. The top candidates, for example, would fall into the category of High Runner Product. All other products would be examined based on additional factors. 
   • An important aspect is whether the products are appealing to A customers or if they can be sold in bundles with other products, specifically the High Runner Products. Additionally, products can be assessed for similarities based on their components. If similarities are found, these should be categorized as Co-Product - products that have a dependency on the High Runner Product.
   • All remaining products would be classified as Red Product, indicating that they are potentially inefficient and should undergo closer examination or a phase-out process.
3. Decision making: The decision tree helps formulate clear decision criteria. For instance, the model could reveal that products with high margins combined with certain customer preferences are particularly profitable. These insights enable companies to make targeted decisions regarding product placements, marketing strategies, and potential phase-outs.

Example of product portfolio optimization using ERP data

In this exemplary approach, the phase-out application case is examined using data from the ERP system. The categorization into High Runner Product, Co-Product and Red Product is also applied in this context. Below are the steps for identifying phase-out candidates: 

1. Identification of High Runner Products accordiung to pareto: Initially, an analysis is conducted to determine which products generate 80% of the revenue. These core products are categorized as High Runner Product. All other products are addressed in the next step.
2. Assessment of customer similarity: Next, it is checked whether the products were delivered to the main customers (A customers). If so, they are classified as Co-Product. All other products continue to be considered.
3. Component analysis: The next step involves checking whether a significant portion of the components or parts is found in the core products. If this is the case, these products are also classified as Co-Product.
4. Examination of shopping baskets: Finally, a shopping basket analysis is conducted. If the products are frequently sold together with the core products, they are again classified as Co-Product.
5. Categorization of remaining Products: All products that do not fall under High Runner Product or Co-Product are classified as Red Product and should be removed from the assortment.

This structured approach enables companies to establish a suitable analysis with relevant criteria for the phase-out of product variants. Through this systematic methodology, companies can effectively identify inefficient products and streamline their portfolios.

The right logic for your business: Customizing is key

One of the most important principles in product portfolio optimization is the individuality of solution approaches. There is no universal method that fits every company. The choice of the right logic depends on the industry, company size, available data, and specific objectives.

• Small businesses may benefit the most from a simple combination of ABC analysis and margin evaluation. These approaches allow for a clear overview of the most important products with minimal effort.
• In contrast, medium to large companies that have extensive data sources should rely more on multidimensional analyses and decision trees. These methods help optimize the diversity of the product portfolio in a more informed manner and facilitate strategic decision-making.
• In B2B companies that heavily focus on individual customer requirements, CPQ data will play a key role in optimization. They enable a targeted customization of offers to meet the specific needs of customers.

The challenge lies in utilizing the right data and finding the appropriate logic that aligns with the company's strategic objectives.

In this context, it may be beneficial to consult experts and utilize specialized software solutions for product portfolio management, such as that offered by MYNR. Given the extensive amounts of data generated in modern businesses, using software is essential for efficiently analyzing this data and deriving valuable insights. MYNR specifically links product-centric data to analyze the product portfolio using data from various systems, enabling informed decision-making. This methodology is referred to as Product Mining.

For more information, please refer to the Guide on Product Mining (German).

Conclusion: Data-driven product portfolio optimization as a success factor and resilience for the future

The days when a simple ABC analysis sufficed for product portfolio optimization are over. In today’s data-driven business world, a deeper analysis is required to recognize the true value of a product. ERP, CRM and CPQ data provide valuable insights for this optimization. However, each company must find the appropriate methodology that aligns with its objectives and constraints.

A customized, data-driven product portfolio optimization is crucial for competitive success. By optimizing the product portfolio, you not only increase efficiency but also strengthen your market position in the long term. In an era of growing uncertainties, such as volatile markets and rising customer demands, the ability to make data-based decisions becomes essential for a company's resilience and sustainable success.