What does a data-driven retail organization look like?

By Chase England, Senior Manager Data Analytics and AI at Ciklum

The phrase “data is king” is not new.  In the retail industry, harnessing data-driven insights can empower a business to optimize every aspect of its operations from marketing and sales to supply chain management and customer service. For example almost 35% of Amazon’s sales are a result of personalized recommendations and over 50% of these transactions are likely to result in a repeat purchase. Similarly, Walmart observed a 10-15% increase in online sales for $1 billion in incremental revenue as a result of how they’ve leveraged their data.

While industry giants may be making their data work for them, many brands are struggling to get to grips with the governance, technical architecture and culture required to turn their data into actionable insights. In this article we look at the foundational components that enable a retail organization to use data to deliver and measure business value.

A focus on customer data

It goes without saying that a data-driven retail organization prioritizes collecting, analyzing, and using customer data to better understand their needs and preferences. This includes data on customer demographics, purchase history, shopping behavior, and feedback. Retailers can use this data to personalize the shopping experience for customers, recommend products based on their interests, and even predict their future needs. Using data in this way also provides retailers with a single view of their customers. This ensures they have an aggregated and consistent representation of their customers’ interactions with their business across multiple brands and channels. 

A modernized data platform

Legacy data silos are a common impediment to retailers unlocking the value that their data footprint holds. To effectively leverage data, a retail organization should have a modernized data platform that aggregates and organizes data from various sources, such as point-of-sale systems, e-commerce platforms, and customer feedback channels. 

The platform should be able to consolidate and process large volumes of data in real-time (or close to). This is incredibly valuable for two reasons. Firstly, it enables analysts to identify insights that drive action across the value chain, ranging from supply chain management to new product development decisions. Secondly, it supports cutting edge technologies such as third party cloud AI services like ChatGPT to use data more intelligently.

A data-driven culture

A data-driven retail organization has a culture that values data-driven decision making over intuition or guesswork. This means that every decision, from marketing campaigns to product design, is informed by data analysis and insights. The organization should have a team of data analysts and scientists who are responsible for analyzing and interpreting data and providing actionable recommendations. More broadly, embedding this mindset across the organization means providing the right stakeholders with access to accurate, up to date data.

Cultivating a data-driven culture also means promoting a continuous improvement mindset amongst teams. A data-driven retail organization is always looking for ways to improve and innovate based on insights from data analysis. Naturally this means that the business should be prepared to experiment with new strategies and technologies, and to quickly pivot based on feedback and results. Netflix is a perfect example here. The highest-earning media services provider runs approximately 250 A/B tests each year, presenting two different versions of experiences to users to see how they react to proposed changes. This ensures that whatever content is shown on the platform is driven by A/B test data and not guesswork.

An emphasis on data governance

Data governance guides and directs all other data activities and should be treated as a critical component of an effective data strategy.  It is the method of controlling data assets, and encompasses data management, security, privacy, and decision making. As retail organizations collect more customer data through a growing number of sources, it is increasingly important to prioritize data governance. 

However, it can be difficult to self assess an organization’s data governance and other data management activities. To assist with this Ciklum has created a Data Maturity Self Assessment, intended to help identify an organization's data strengths and weaknesses. As well as this, we offer an additional in-depth assessment to help data leaders take control and implement a data-driven retail organization. 

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