Change or Fail. Why predictive retail analytics is a must.
Cold feet. Hot metrics.
9 min read
Taking your first steps to action your data
September 20, 2019
A common challenge faced by apparel retailers is understanding what their data can – and cannot – do to support a data strategy. It is common in apparel retail for many industry-specific issues to be present in the data. These issues can come from a variety of sources including assortments that constantly change, lack of standards with regards to how SKU names are created, SKU’s that continue to stay the same for products that change attributes, loss of data consistency upon product liquidations and more. This can result in a lack of information being captured or preserved in the data collection and management process.
Fear, concern or simply a general unease as to the state of the company’s data can lead apparel retailers to get cold feet and hesitate. This decision paralysis is often compounded by the lack of clarity within most retailers of exactly what data is required and for what use cases the data will be used to address. The reality of the situation is that these concerns can generally be quickly overcome when embraced with the guidance of qualified internal teams or a third-party partner.
Being empowered changes what has been a source of limitation and turns it into a sense of strength and purpose.
By starting on a data-centric journey, retailers will quickly learn what use cases their data can support, where their data is lacking and what steps should be taken to create a more robust data ecosystem. The process will enable retailers to develop a set of best practices and governance procedures based on business used cases and strategies. In turn, the sense of confidence and data discipline will align all the teams inside the retailer around a shared data view and enable them to make data-driven decisions to achieve business objectives.
Getting started, apparel retailers should look at their product, customer, store and inventory data sets.
In general, most apparel retailers do a good job capturing much of this data. However, few retailers have conducted a data audit, determined the capabilities of their data to support business use cases or have a rationalized data governance plan. The plan enables retailers to prioritize addressing any issues that may exist in their data tables. For instance, an apparel retailer with a business objective of planning down to the attribute level should work first on improving the depth and consistency of their product database while deprioritizing work on their customer data for instance.
Retailers should begin with one use case and build from there. The process of increasing use cases based on a priority of business objectives will provide short-term wins, increasing data hygiene and discipline while laying the groundwork for a very successful framework for long-term data empowered decision making.
Once the retailer has its internal data cleaned and being captured effectively, additional sources of data can be layered into to increase the resolution and capabilities of the team. Weather and social media are at the top of the list for apparel retailers. New use cases will present themselves with the addition of more varied data sources.
It is becoming increasingly unacceptable for apparel retailers to delay acting over concerns of their data availability or quality.
Getting started with data analytics is a must-have tool to stay viable in the increasingly competitive landscape of apparel, footwear, and accessory retail. We as customers are all being trained the likes of Netflix, YouTube, Instagram and more to get what we want, when we want it. Retailers that get started – that lean into their data – and begin to systematically audit and improve their data and their culture to use the data to drive decisions will find their revenues, margins and turns steadily increasing.
At FIND, we offer FIND Plan and FIND Periscope. FIND Periscope is an excellent place to start. Periscope provides retailers a deep dive into their data, enabling them to understand exactly what use cases their data can support, where there are improvements to be made and receive a set of best practice recommendations. Whether taking the journey with your own teams, a third party or FIND, it is critical that retailers get started to measure just how AI-ready their data is.