Embracing advanced, real-time analytics
The last few months have presented a challenge for traditional bricks-and-mortar retailers in the UK. High street sales have continued to struggle in a difficult trading environment, leading to a number of high-profile retail casualties – notably companies such as Toys R Us and Maplin.
All UK Toys R US stores are poised to shut down after administrators failed to find a buyer, resulting in a store closure programme and a possible 3,000 job losses.
And yet, many of the larger online-first retailers have continued to do extremely well. Both Amazon and Asos, for example, reported sales growth of around a third across 2017.
A number of the reasons behind this have been well documented: the convenience of online
shopping; the advantages of bulk buying; reduced overheads; greater centralisation of resources – all of these are contributing factors in the success of online-first retailing.
One element that is often overlooked is that these online-first retailers have, from the start, had the advantage of being able to put advanced data analytics at the core of their businesses.
Online retailers know how to leverage advanced analytics
Online-first businesses have a number of advantages when it comes to adopting analytics. For one, these businesses tend to be run by mathematically and technically-minded people – skills which are highly advantageous when implementing and using traditional analytics solutions.
These businesses also have the luxury of starting from a ‘blank slate’, allowing them to implement the ‘latest and greatest’ in new analytical tech without being hindered by legacy skills, processes and systems.
The advantages to online retailers of taking this data-led approach has been clear. By taking a deeper-dive on their customer data, online-first retailers have been able to offer more targeted marketing and a highly-personalised online experience. They have also been able to massively optimise their supply chains, inventory holding and delivery methods.
Understanding what’s required for an effective retail analytics system
Offline businesses still have an opportunity to emulate this. Firstly, when looking to implement analytics, it is important that retailers ensure they are able to draw in and operate on, all of their data. Taking a complete, holistic view of their data can allow retailers to spot new, and perhaps unexpected, trends, and derive much greater insight.
Secondly, anyone within the business should be able to analyse this data, from the global sales director to individual store managers, and in real-time. This will allow them to explore and action data in a more agile way – eliminating the guesswork from sales meetings, for example, by allowing employees with valuable business knowledge to ‘recast’ data on-the- fly.
The challenge comes with the volume and velocity of customer data available. However, using artificially-narrowed datasets inevitably means losing out on vital insight – as does undertaking lengthy analytical projects that may be days out of date by the time they are completed.
The problem for retailers has been that analytics solutions tend to be fairly specialised, requiring trained data scientists with knowledge of programming in order to use them. Business stakeholders cannot apply their individual expertise to leverage these systems directly. Online-first retailers have long understood the critical role of advanced, real-time analytics tools, and this is part of the reason for their success.
While high street businesses might have big hurdles to overcome, I have no doubt that those that thrive will be the ones that follow the example of online-first businesses, putting advanced analytical tools at the heart of what they do.
Mark Hinds is the CEO of Polymatica, a business intelligence and analytics solution with embedded data science that works at scale and speed using revolutionary GPU and CPU hybrid architecture.
