Modern product portfolio analyses for data-driven portfolio management

Product management today is confronted with increasingly complex demands. Markets are more volatile, decisions need to be made faster, and demographic changes are exacerbating the shortage of skilled workers, adding further uncertainties for companies. At the same time, sustainability requirements and the circular economy are leading to longer and more complex product life cycles. These developments make informed decisions in product portfolio management more difficult.

Data-driven product portfolio analyses are indispensable for addressing these challenges. Systems like ERP, CRM, CPQ, PIM and PLM provide the necessary data to identify risks early and strategically optimize the product portfolio. By leveraging linked data structures and automated analysis processes, decisions can be made more quickly and on a solid basis, which strengthens the company's resilience. This benefits not only product management but the entire organization, as deep decision-making foundations are created, enabling a comprehensive understanding of the portfolio and ensuring long-term stability.

Why product management is the driving force

Product management is optimally positioned to drive data-driven product portfolio management. As the central interface to various stakeholders, such as sales, marketing, development, procurement, and production, it has the best overview of internal data sources. These contain valuable information such as sales data, production costs, customer feedback, technical specifications and lifecycle data, often distributed across different systems.

This data already exists in manufacturing companies and needs to be systematically linked to create a deep understanding of the existing portfolio. It is not necessary to capture all information immediately. What is crucial is establishing the foundation for informed portfolio analyses, which typically resides in ERP systems. Leveraging internal data provides a solid basis for sound decisions in product portfolio management.

In addition to internal data, external sources must be incorporated to define the target portfolio. This includes market data, competitor analyses, market segmentations and risk analyses, all of which help align the portfolio strategically and prepare for future market demands. This external knowledge is also essential to ensuring long-term success.

External sources und portfolio alignment

Unlike internal data, external sources are often harder to access and not always easy to capture. Some are readily available, such as publicly accessible market data, while others are more complex, like detailed competitor or specific risk analyses. The preparation of this external data is often labor-intensive and rarely automated. Frequently, these data need to be gathered, analyzed and tailored to the company within the framework of consulting projects. Nevertheless, these sources are essential for strategically aligning the product portfolio and the overall business.

Defining the target portfolio is often done in consultation with senior management and is commonly supported by tools like the BCG matrix. This method enables a strategic assessment of products by classifying them along two dimensions - market share and market growth. Products are divided into four categories:

• „Stars“ - Products with high market share in a rapidly growing market
• „Cash Cows“ - Products with high market share in a stagnant market
• „Question Marks“ -  Products with low market share in a growing market
• „Poor Dogs“ - Products with low market share in a stagnant market

Despite its usefulness, the BCG matrix has significant limitations. It does not consider the internal synergies between products, which are crucial for a deep understanding of product portfolios. Moreover, applying it is often time-consuming and resource-intensive. In today’s fast-paced business environment, the BCG matrix alone is not always sufficient to respond flexibly to changes. Given the increasing product complexity and the availability of numerous systems that provide relevant information, alternative approaches are needed to analyze and continuously improve the product portfolio more effectively.

How product-centric data connections change portfolio analysis

In today’s portfolio analysis, a connected structure is crucial. Data is at the heart of successful decision-making, and many companies already possess valuable information deeply embedded in their systems - especially in the ERP system, which serves as the foundation.

The ERP system provides critical data about customers, selected product variants, their material structures and the resources used in production, as well as the sources for materials. This fundamental product data offers indispensable insights into the existing product portfolio and its associated optimization potential. Relevant data includes: 

• Sales data: This provides insights into sales figures, regional differences and demand trends, aiding in the identification of successful products and potential phase-out models.
• Variant lists: Information about different product variants and their market performance helps target less profitable variants for elimination or improvement.
• Bill of materials (BOM): This includes the material composition of each product, allowing for more precise resource planning and optimization of production and procurement processes.
• Procurement data: This data reveals procurement costs, supplier performance and material availability, enabling strategic optimization of purchasing.
• Production data: Information about production times, costs and capacities helps identify bottlenecks and make production more efficient.

There is no better way to gain a deep understanding of the existing portfolio than through comprehensive portfolio analysis based on this data. The targeted linking of this information allows product managers to recognize synergies and interactions between different products and to take a holistic view of the entire portfolio.

Product-centric data connections also help identify opportunities and risks early, enabling product managers to respond flexibly to market changes. This connected structure not only enhances agility in product portfolio management but also strengthens the company's resilience. Consequently, decisions can be made on a solid, data-driven basis, continuously optimizing the product portfolio and adapting it to changing market conditions.

Strategy and depth - BCG Matrix with data-driven approach

The BCG matrix is a proven method for strategically evaluating and prioritizing products in a portfolio by categorizing them as “Stars",“Cash Cows”, “Question Marks” and “Poor Dogs”. However, this method captures only market share and market growth, failing to account for the full operational complexity of modern product portfolios.

A data-driven approach that incorporates information from ERP, CRM and CPQ systems broadens this perspective. These systems contain detailed product information, such as production costs, material structures, and customer preferences, which extend far beyond simple market positioning. By linking this internal data with the strategic evaluation of the BCG matrix, the company gains a more comprehensive overview of its portfolio.

A deep understanding of the existing portfolio helps to define the target portfolio more precisely. Instead of relying solely on market performance, product managers can evaluate products based on their internal efficiency and value to the company. This comprehensive perspective allows for better recognition of which products should remain in the portfolio, be further developed or be eliminated.

The linked structure lays the foundation for today’s strategic portfolio management by ensuring that relevant information is interconnected in a holistic approach. This integrative viewpoint is crucial for aligning product strategy and enables unprecedented transparency.

For more information, we recommend our blog article on the importance of data connectivity in product portfolio management for a holistic view.

Thus, the combination of strategic alignment through the BCG matrix and a detailed analysis of internal data leads to informed decisions that optimize the portfolio and make the company more resilient.

Data-driven efficiency in product portfolio analysis

The demands for effective product portfolio management are more complex than ever. To make informed decisions, it is crucial to filter and process relevant information. A linked data structure forms the foundation for strategic portfolio management, as it enables a comprehensive analysis by consolidating product-centric data from various sources.

To achieve the maximum effect, it is essential to automate the labor-intensive analyses in portfolio management. In the past, product portfolio analysis often occurred sporadically and was time-consuming. However, modern systems that focus on product-centric information allow for a more efficient and in-depth analysis. The transition "from data to actions" becomes tangible.

One approach to addressing these challenges is known as Product Mining. This method for analyzing complex product portfolios helps companies identify hidden relationships and dependencies within the portfolio. At the same time, Product Mining generates automated decision-making foundations that enable targeted portfolio optimization. A software solution that supports this approach is the MYNR APP.

Benefits of a thorough analysis:

• Transparency: Companies gain a clear overview of how their products are performing and where opportunities and risks lie.
• Decision support: The combination of data analysis and decision intelligence provides valuable insights that support the decision-making process.
• Resource efficiency: The use of appropriate software promotes more efficient resource utilization and strengthens long-term competitiveness.

Reading recommentation: Everything you need to know about Product Mining (German).

By implementing such solutions, the process of product portfolio analysis becomes automated, saving time and costs. The wealth of data available within the company's own systems is effectively integrated into the decision-making process. Companies that pursue this strategy can significantly optimize their product portfolio management processes and successfully position themselves in the competitive landscape. There are numerous approaches to portfolio optimization that companies can utilize to further enhance their competitiveness.

Overall, the combination of linked data structures and the automation of analysis processes is a crucial step in overcoming the challenges of modern portfolio management and ultimately securing sustainable business success.