Getting Your Data Ready for AI: Tips For Merchandising Teams

This is the first installment in our series on AI readiness for merchandising teams.

Merchandising teams interested in adopting AI solutions have four critical areas to assess before getting too far into the process: data availability, computational ability, strategic scalability, and change management. In this blog we’ll cover data.

As a merchandising team member in the apparel or footwear industry, you're likely inundated with data. From point-of-sale numbers to web analytics, your team has access to more information than ever before. However, the question remains: are you prepared to leverage this data effectively using artificial intelligence (AI)?

As you explore AI merchandising solutions, it's crucial to assess your data readiness. But don't let imperfect data availability stop you from getting started.

This article explores the importance of data preparation and outlines steps you can take to ensure your organization is AI-ready.

The Big Data Challenge And Opportunity In Merchandising

Today's retail landscape is data-driven. Merchandising teams have access to a wealth of information sources, including:

• Point-of-sale (POS) data
• Enterprise resource planning (ERP) system information
• Web and store analytics
• Social media insights
• Customer feedback
• Inventory levels
• And much more

This abundance of data promises better insights and improved performance. The underlying assumption is that more comprehensive knowledge about customers and products leads to better decision-making.

AI is a powerful tool for handling large volumes of data, identifying patterns, and making predictions. AI systems can process information at a speed and scale that far surpasses human capabilities, potentially providing insights that might otherwise take weeks or months to uncover manually.

However, before implementing AI solutions, it's imperative to ensure your data is in optimal condition. This preparation is analogous to organizing and cleaning your workspace before embarking on a major project — it sets the foundation for success.

3 Data Priorities For Merchandising Teams

1. Update Your Data Hygiene Practices

The first priority is to establish and maintain robust data hygiene practices. This involves a comprehensive assessment of your current data management processes:

• Identify the individuals or teams responsible for data management
• Determine which datasets require cleaning or updating
• Evaluate the tools and systems currently used for data management
• Establish protocols for data updates, cleansing, and archiving

Developing clear answers to these questions will facilitate the creation of an effective data management plan. Even if immediate implementation of all improvements isn't feasible, having a structured plan in place is a significant step forward.

2. Improve Data Naming Consistency

The next priority is to enhance consistency in data naming conventions. This involves examining how items, categories, and attributes are labeled across your systems:

• Do product names and identifiers remain consistent across different platforms?
• Have naming conventions evolved over time, potentially creating inconsistencies?
• Do buying, allocating, and planning teams use uniform labeling systems?

Consistent naming is critical for effective AI implementation. Inconsistent or ambiguous labeling can lead to confusion in AI systems, potentially resulting in inaccurate analyses or predictions. The adage "garbage in, garbage out" is particularly relevant in the context of AI and data quality.

3. Consolidate Your Data

The final priority is data consolidation. This involves assessing how your data is stored, shared, and organized:

• Consider the number and types of data repositories in use.
• Evaluate whether your data is centralized or distributed across multiple locations.
• Assess the methods used for data sharing between systems (APIs, SFTPs, direct integration, etc.).
• Determine if a standardized data model is applied across all data sources.

Centralizing and standardizing data access can significantly enhance the effectiveness of AI systems. As an added bonus, data consolidation can help address the common issue of data sparsity in retail, where limited sales data for individual products can hinder accurate predictions.

Leveraging Technology Partnerships To Manage Data Complexities

Preparing your data for AI implementation is undoubtedly a substantial undertaking. However, the potential benefits make it a worthwhile investment:

• Detailed, product-level insights become accessible.
• Data analysis can be performed at unprecedented speeds.
• Teams can redirect their focus to high-value strategic initiatives.

These benefits drive improvements in key performance indicators such as sell-through rates, inventory management, and profit margins.

If the process seems daunting, it's important to note that external support is available. Many AI providers offer partnership models that include assistance with data preparation. These technology partners understand the unique challenges faced by merchandising teams and can provide valuable guidance throughout the process.

For instance, at Syrup, we incorporate a comprehensive data review as part of our onboarding process. This involves identifying potential issues and collaborating with your team to enhance data quality from the outset. We also provide ongoing data monitoring to proactively address any emerging issues.

Get Your Merchandising Data Ready For AI

Preparing your data for AI integration can seem like a significant undertaking, but it's a crucial step toward enhancing merchandising decision-making. By focusing on data hygiene, naming consistency, and data consolidation, you can establish a solid foundation for successful AI implementation.

Don’t let perfect be the enemy of good. Most retailers have data that is perfectly fine for a new AI project.

As you continue, initiate a process of continuous improvement that will progressively increase the value derived from your data and AI investments. With a strategic approach and appropriate support, you can transform data challenges into opportunities for growth and innovation in your merchandising operations.