Why data will be integral to post-pandemic success in retail

Amidst governmental lockdowns, many shoppers have welcomed the move to online for everything from groceries, to loungewear, to ‘make it yourself kits’ from their favourite restaurants and bars. The demand landscape has shifted significantly over the course of the pandemic and will continue to do so as the UK enters a new phase of localised lockdowns, forcing high-street retailers to once again close their doors with little to no heads up.

Yet, as recent ONS reports have indicated, this hasn’t been about simply slipping back into normality. It’s been a challenging, but eye-opening transition that has pushed the need for data science and analytics into the limelight. Consumers want a faster, more immediate, and customised experience, all at a competitive cost. Retailers want to get a holistic view of their customers, their supply chain and engage with their customers in order to respond to changing demand and stay relevant and top-of-mind to boost sales, online and off.

To succeed in the new normal still ahead of us, retail must adapt their mindset. They need to stop debating how data can be used more effectively and embed data analytics centred on operational efficiencies to uncover the much-needed hidden insights needed to ultimately reposition themselves and create a competitive edge.

Take a step back and revaluate

Contrary to popular belief, retail is, in fact, one of the UK’s most data savvy sectors today. Having accumulated vast quantities of data over the last two decades, online and off, retailers are all too aware of the money hiding within this asset. And many are already putting this asset to work by using data to improve merchandising, evaluate new locations for store placement, and even guide brand expansion into new customer segments or territories. But as market conditions toughen, the cracks are beginning to show.

What has become apparent is that while some retailers may be data savvy, very few are effectively using analytics to weather the storm. Demand forecasting is the perfect example of this. Despite a whole host of predictive analytics and automation tools being readily available to retailers, the traditional process is still very much in action - retailers spending hours reviewing spreadsheets and using their past sales to anticipate future demand. While data plays a role in guiding this process, and can provide a higher percentage of accuracy, retailers could not only improve accuracy further still but also look to gain huge efficiency savings by simply introducing predictive analytics into this process.

This fresh approach to data and analytics is about moving beyond using data insights to inform human decision making within the business. It is about using predictive analytics to harness the hidden power within the thousands of disparate data sources and processes across the business to uncover actionable insights and drive automation. This is about meeting demand where and when it appears and creating dynamic merchandising and offers that are perfectly tailored to the customer to move the bottom line.

The success I speak of is not theoretical. UK sports apparel company, Gymshark, is now one of the world’s most recognisable brands within the fitness sector – and it’s down to its use of analytics that has allowed this company to stay one step ahead of its competitors.

The company has access to shed loads of data about its customers, who attend Gymshark events and pop-up stores, engage with the company on social media, and even follow workout programmes on its app. Gymshark quickly realised, like many other retailers, the value of this asset in their possession. But all of that data is meaningless if Gymshark can’t act on it—and quickly. Pre-pandemic, pop-up retail events were a major part of Gymshark’s business. Critical to the success of these events was the way in which the company decided on location with data analytics. Gymshark would use customer metrics spanning several touchpoints and business functions – app engagement, social media engagement, spending levels, and gender splits – to determine the optimum location for its customer base. While for some retailers this level of data crunching would take around two weeks, for Gymshark, the team can access this information in a matter of minutes due to the way they have embedded analytics and automation into their everyday business set-up. This approach, however, doesn’t only apply to pop-ups. As the UK were forced to lockdown and keep themselves safe, Gymshark were able to continue their growth in the market through using this same approach and applying it to product design and direct marketing.

Gymshark, of course, are not the only UK retailer to have embraced this alternative approach to data and analytics to ensure their doors remain open to the public. As the concept of the ‘weekly shop’ became a mainstay for every household in the country, the need to provide safe, accessible, and seamless experiences for all customers had never been more paramount. Nor had the need to deliver essential products in a timely manner to the most vulnerable in society, despite a drought in products taking place due to mass hysteria.

Recently, I was talking to a consumer-packaged goods company who said that without predictive analytics and automation they would not have been able to manage the increased demand for stock during the height of lockdown. Thankfully, they had already baked in predictive analysis and data science within their process, which allowed the company to be agile when panic hit. They could simply turn up the dial and meet the new demand, all while getting the right products to the right place at the right time.

Data analytics and automating business processes are no longer just a ‘nice to have’. They have become a requirement for retailers – big and small – across every sector. Now more than ever, it is vital to be able to predict customer needs and react to them in this uncertain business environment.

Please mind the gap

These success stories, however, are still few and far between – much to my dismay. While lockdown highlighted just how prominent the digital divide within the UK retail industry, even for large retailers, it also highlighted the emergence of a second ‘analytical gap’. Thriving are companies like Gymshark that have a razor-sharp focus on analytics to harness the hidden power within thousands of data sources and processes to deliver high-value, high-impact outcomes to stakeholders in every line of the business. Others are simply barely surviving in this current climate, struggling to uncover the insights that this data holds and move the bottom line.

One of the biggest myths is that it takes an army of employees with Master’s degrees in mathematics or data science to implement analytics. While the skills gap, particularly in data and analytics, is a very real problem for the UK, companies like Gymshark have shown that it doesn’t have to mean falling behind. Instead, developing analytic literacy across the business alongside self-service data science is the best approach to bridging this analytical divide. Providing simple to use tools with pre-configured workflows allows everyone to safely and accurately explore their data to discover what’s available from databases, local files, reports, dashboards, and workflows.

Data literacy should be treated as a crucial skill for pretty much everyone. That does not mean everyone needs to become a qualified statistician, data scientist or python coder. Instead, retailers must focus on building a culture of data literacy within their company - empowering workers at all levels of the company to be able to use data and analytics in their daily lives, regardless of technical acumen. In fact, what we often see, is training and championing all employees from the bottom-up allows for a widespread adoption and questioning from all angles of the business to expand a new insights-driven culture; empowering ordinary business professionals to become more sophisticated data workers.

Conclusion

The need to embrace data and utilise analytics to guide strategy is more crucial than ever to navigate this new ‘normal’ demand landscape. Valuable insight shared throughout the fabric of successful retailers and made available to all levels of employees will prove critical for retailers wanting to successfully understand what?is driving behaviour and to gain that three-dimensional view of the customer.