How Retailers & Brands Can Get Maximum Value from Their Data

In today’s rapidly evolving business landscape, data has emerged as a powerful tool for driving decision-making and building trust. However, a recent Salesforce study reveals that despite 73% of leaders recognizing the importance of data, less than 35% of businesses are effectively utilising it for key decisions. The primary reason behind this gap is that business leaders often find themselves overwhelmed by the sheer volume and complexity of data, struggling to generate actionable insights. To help retailers and brands navigate this challenge, here are some key considerations to develop a winning data strategy for the retail and wholesale industry.

Prioritise Key Outcomes

One of Steven Covey’s 7 habits for highly effective people is “begin with the end in sight”. Although that ties more into the principles of personal living, your organisation needs to adopt a similar mindset that starts with identifying and prioritising the most valuable outcomes you aim to achieve.

This involves understanding the specific data and metrics required to support these outcomes and determining the sources from which to gather them. For example, a robust assortment planning process would require product attribution data, while optimising markdown and promotional activities would necessitate SKU/store-level weeks of stock cover data.

Quality vs Quantity

When it comes to data, it’s crucial to strike a balance between quality and quantity. Different use cases may require structured or unstructured data. Your organisation needs to prioritise capturing data at the most granular level, such as capturing inventory levels at the SKU/store/day level, allowing for more accurate analysis. For fashion businesses, obtaining a clear picture of sales, stock, and sell-through at a granular level is essential.

Invest Time in Data Hierarchies

Establishing well-defined data hierarchies is vital for effective data management. Key hierarchies to consider include product, time, location, and customer. Mapping these hierarchies to your business’s specific needs ensures consistent and organised data. Historical merchandise data should be organised into a comparative calendar for accurate demand forecasting and analysis. Master data management plays a critical role in maintaining data integrity and consistency, while product homogeneity within hierarchies ensures accurate insights.

Dismantle Data Silos

Data silos hinder collaboration and inhibit data-driven decision-making. Despite 73% of retail value-chain leaders relying on spreadsheets as their primary planning tool, it is crucial to move towards a centralised data source. Storing information in individual spreadsheets saved locally creates data fragmentation. Instead, consider implementing a merchandise data warehouse in the cloud to centralise data and provide easy access. Additionally, prioritise understanding the use cases of different stakeholders and provide cross-functional data access to promote collaboration.

Adopt a Data-centric Mindset & Culture

Building a successful data strategy requires more than just technology implementation. It involves transforming the way people work and think. The IT department alone cannot drive this change. Identify key roles and responsibilities across the organisation and provide the necessary resources to foster a digital, data-centric mindset. Focus on deriving insights from data rather than relying solely on data itself. Emphasise understanding the “why” behind trends and outcomes, supplementing human insight with data-driven analysis. Utilise technology to eliminate human biases in forecasting and explore multiple predictive scenarios simultaneously.

Understanding Your Customer

While third-party data can provide valuable insights, it is essential to gather relevant data directly from customers. Clear and honest communication with customers encourages them to provide accurate and useful data. Collecting data such as size, style, and colour preferences rather than just birthdays enables more effective personalised product and marketing strategies. Historical trends still remain invaluable for forecasting, providing a foundation for predicting future customer behaviour.

By implementing these considerations, retailers and brands can develop a winning data strategy that maximises the value of their data. With a focus on actionable insights, collaborative data practices, and a data-centric culture, businesses can leverage data as a powerful asset to drive success in today’s competitive retail landscape.