Build Your Data Strategy to Improve Your Product Mix

Identify value-driven data strategy use cases to transform your product mix.

Analyst perspective

Maximize the value of your data to drive optimum product mix.

A comprehensive data strategy allows retailers to leverage real-time actionable insights generated through advanced analytics tools that are underpinned by AI / ML technologies. The insights are especially important for retailers in developing their product mix as they can quickly identify and predict trends based on customer preferences and market shifts.

The insights give retailers the advantage of providing a focus on product mix development that is optimal to staying ahead of trends; in coordination with an integrated business process, these insights allow retailers to quickly action across the organization to purchase, distribute, and sell the optimal product mix being sought by their customers. This ultimately provides retailers a competitive advantage.

Without a comprehensive data strategy to better understand how data impacts product mix development and to connect data to advanced tools that support optimum decision-making, retailers risk being left behind by their competition.

Donnafay MacDonald
Research Director, Retail Industry Practice
Info-Tech Research Group

Executive summary

Info-Tech Insight

A product mix data strategy needs to be focused on how data will be used to achieve the corporate mission, not just loosely linked to it. Retailers must ensure that the value in a data strategy that drives better product mix aligns with and supports the organization’s objectives through a business-focused strategy.

Surge in data generation necessitates a comprehensive data strategy

The surge of data generated from multiple retail systems creates challenges such as point-of-sale, e-commerce platforms, and inventory management tools. Retail teams are inundated with vast amounts of data from multiple sources, and while this wealth of information has the potential to provide a clear picture, the sheer volume and complexity presents formidable challenges.

Massive Data Volume

175 zettabytes of data is estimated to exist globally in 2025.
1 zettabyte = 1 billion terabytes

Source: Forbes, 2022

Unstructured Data

80% of global data is considered to be unstructured.

Source: Forbes, 2022

Siloed and Inaccessible Data

48% of retailers struggle to connect multiple data sources to make data accessible.

Source: Salesforce, 2024

"To effectively use data for decision-making, retailers must cultivate a data-driven culture within their organizations."

Source: C4R, 2024

How Discount Tire transformed operations by aligning data

A case study on driving data excellence

SECTOR
Retail

SOURCE
Informatica, 2021

Retailers must overcome challenges when implementing effective data strategies

Overcoming the challenges that retailers face is imperative to implementing an effective data strategy to improve product mix.

Data management challenges:

• A staggering 60% of retailers admit to a lack of maturity in data management technology, and half of them struggle to process big data quickly enough for decision-making (WNS Triange, 2023).
• The technology gap prevents retailers from harnessing the full potential of their data assets, leading to inefficient operations and missed opportunities.

Cybersecurity realities:

• Retailers must be cognizant of the vast amount of personal data they are gathering about their customer base and have cybersecurity front and center.
• Data breaches not only cost the business in operational downtime, post-breach responses, lost sales, and regulatory fines, they also cost customers by eroding trust.
• In 2023 alone, data leaks cost companies up to US$4.99 million (IBM, 2024).

Data quality concerns:

• Data quality is paramount for making informed decisions, yet only 37% of respondents to a Statista survey reported success in improving data quality.
• This widespread challenge across industry sectors suggests that retailers are working with unreliable or incomplete information, leading to poor decision- making (Statista, March 2024).

Artificial intelligence (AI) adoption hurdles:

• As AI becomes increasingly important, the lack of data quality and expertise is a significant barrier.
• With 43% of employees citing a lack of awareness, understanding, or expertise when using AI tools as a key challenge, retailers risk falling behind competitors who successfully integrate AI into their operations (Statista, Dec. 2024).

High-quality data drives advanced technologies

High-quality data must be a top priority and can be achieved by having a robust and comprehensive company-wide data strategy. The level of accuracy required to achieve the desired outcomes must be driven by the business and by the benefits it will help the organization achieve. For example, 80% accuracy may be sufficient in some cases, while in others it could be possible that 100% accuracy is required for decision-making; it comes down to the trade-offs to be considered in achieving the level of accuracy needed (Shelly Palmer, n.d.).

Retailers’ competitive advantage is their proprietary data, which, when combined with integrated processes and advanced technologies, can return high-value insights into customers, products, and operations that can be used to implement efficiencies and capitalize on new opportunities (Accenture, 2024).

Cross-company collaboration is limited when data is locked into silos and functional domains. Integrated business planning, underpinned by high-quality data, enables the reinvention of end-to-end planning and decision-making, cutting across value chains and business functions (Accenture, 2024).

Effective decision-making relies on accurate insights, and accurate insights are a result of high-quality data inputs. The phrase "garbage in, garbage out" is more important than ever for retailers to stay relevant and competitive as the need for advanced technologies increases.

Data is a growth driver.

Data-driven organizations achieve 10%-15% revenue growth.

Source: Accenture, n.d.

PUMA revamped how it managed its product information

A case study on optimizing data to increase sales

SECTOR
Retail

SOURCE
Informatica, 2024

Data strategy plays a critical role in product mix optimization

Product mix is a retailer’s complete set of products that are offered for sale. It is often referred to as a product range, product portfolio, or product assortment. Like products are grouped and organized into product line hierarchies. When deciding on the product mix, merchandisers rely on data to help inform the optimal direction to take in future buying cycles.

Retailers who can leverage advanced data analytics, technologies, and aligned processes will have a competitive advantage.

Integrated business planning (IBP)

IBP aligns all product mix decisions to the company’s strategy, operations, planning, and finances to ensure agility in responding to the market.

Real-time data analytics

Access to real-time data provides retailers the ability to quickly adjust product mix based on changes to market trends, market dynamics, and customer buying behavior.

Artificial intelligence / machine learning models

Predictive algorithms can forecast demand and recommend optimal product mix.

Info-Tech Insight

Product mix is more than just an offering, it is a strategic lever that directly impacts customer satisfaction, operational efficiency, and profitability. In today’s hypercompetitive environment, data is the foundation of an effective product mix strategy.

Babymarkt optimizes product mix

A case study in data analytics

SECTOR
Retail

SOURCE
Strategy Software, 2025

Neglecting a robust product mix data strategy leads to higher cost

Poor data quality is not just an inconvenience to retailers, it’s a major financial drain.

Neglecting data is costly. One of the more alarming consequences is the impact on inventory management, where stockouts have cost retailers upwards of US$1.235 trillion globally (IHL, 2022), putting product mix at the forefront.

Individual losses have a compounding effect beyond lost revenue:

• Inaccurate demand forecasts
• Reduced loyalty driven by customer dissatisfaction
• Inefficient operations
• Reduced competitiveness
• Flawed decision-making

Retailers cannot afford to neglect their data strategy. Given the stark financial reality, retailers must address data quality to improve their product mix.

Data mismanagement has hidden costs.

Productivity is plummeting.

Data mismanagement can lead productivity to decrease by 20%.

Operational costs are soaring.

Data mismanagement can also lead to a 30% increase in cost.

Source: Esri, 2024

Understanding product mix assists in building effective partnerships

Align to execute by building effective business–IT partnerships. Understanding the definitions of fundamental business concepts leads to faster and better decision-making across teams.

Product mix is a retailer’s complete set of products that are offered for sale. It can also be referred to as a product portfolio or product assortment. Like product is grouped together and organized into hierarchies.

Product mix optimization helps retailers improve customer experience, increase profitability, and gain a competitive edge. This is done through data analysis of proprietary and external data using AI/ML models that provide actionable insights. Successful product mix strategies hinge on the ability to rapidly action on real-time insights to purchase, distribute, and sell product.

Product mix strategies include:

Expansion: Add product lines.

Contraction: Reduce product lines and/or depth.

Deepening: Add variations to existing product lines.

Alteration: Change an existing product.

Trade up: Add higher cost products.

Trade down: Add lower cost products.

Underpinning data:

Proprietary data examples:

Historical sales

Customer buying behavior

Forecasted sales and demand

Operations

External data examples:

Competitor data

Trends

A visual representation of a product mix and product hierarchy:

Source: Product Plan, 2024

Rockport optimizes assortment and pricing

A case study on data shaping product offering

SECTOR
Retail

SOURCE
First Insight, n.d.

How to use this report

This report is designed to complement Info-Tech’s comprehensive Build a Robust and Comprehensive Data Strategy blueprint. It functions as a retail-specific supplement to Phase 1 and a substitute for Phase 2. Once you have completed the activities within this report, return to the core research to progress through the remaining phases of the broader strategy.

Realize all teams are unique; you may feel that some sample information may not be relevant to or represent your organization well due to the particular type of products and services you are engaged in, the geographic area you are located in, etc. We recommend that you adjust and customize the template as needed to be organization-specific and to create the most valuable data strategy for your organization.

You will use this report as a research-based accelerant input as you work through the first two phases of Build a Robust and Comprehensive Data Strategy blueprint.

Phase 1

Understand Your Corporate Objectives & Initiatives

Phase 2

Gather the Key Inputs for Your Strategy

Data strategy roadmap activities

Info-Tech’s methodology to Build a Robust and Comprehensive Data Strategy

This retail-specific research complements phases 1 and 2 of Build a Robust and Comprehensive Data Strategy

Optimizing product mix through a comprehensive data strategy

1.3.1 Define your value streams

1.3.2 Identify business capabilities

1.3.3 Categorize business capabilities

1.3.4 Develop a strategy map

2.1.1 Interview functional business teams

2.2.1 Identify risks to the data strategy

Output of this research piece to be combined in the overarching data strategy for the entire company.

Info-Tech Insight

Data strategy is more than just a framework, it is a strategic lever that directly impacts customer satisfaction, operational efficiency, and profitability by optimizing product mix.

Activities

You will complete these activities from the Build a Robust and Comprehensive Data Strategy blueprint as you work through this retail-focused report:

Phase 1.3

1.3.1 Define your value streams

1.3.2 Identify your business capabilities

1.3.3 Categorize your organization’s key business capabilities

1.3.4 Develop a strategy map tied to data strategy

Phase 2.1

2.1.1 Interview functional business teams

Phase 2.2

2.2.1 Identify risks to the data strategy

2.2.2 Conduct the Data Culture Diagnostic (optional)

This collaborative approach in building a shared understanding of how the business delivers value is the foundation for building a robust data strategy that serves the business.

By the end of the process, the team will have a clear, aligned view of the business’ data needs to improve product mix as well as the key elements the overarching data strategy must include to drive results.

1.3 Get clear on your value streams

Understand and align to business drivers

Activities

1.3.1 Define your value streams

1.3.2 Identify your business capabilities

1.3.3 Categorize your organization’s key business capabilities

1.3.4 Develop a strategy map tied to data strategy

Work through these activities after completing steps 1.1 and 1.2 of Build a Robust and Comprehensive Data Strategy.

• Leverage your organization’s existing business capability map or initiate the formulation of a business capability map.
• Determine which business capabilities are considered high priority by your organization.
• Map your organization’s strategic objectives to value streams and capabilities to communicate how objectives are realized with the support of data.

Outcomes

• A foundation for data strategy initiative planning that’s aligned with the organization’s business architecture, value streams, business capability map, and strategy map