Why lasting growth for retailers lies in the data


By Richard Cooke, UK Business Lead at Keepler Data Tech.

As with almost every sector of the economy, retail has been up against severe headwinds in recent times. 

By definition it is a challenging industry to operate in. Today’s retailers must strike a fine balance between their presence in the physical and digital worlds, a conundrum that is made even more complex by changing consumer attitudes, supply chain issues, and many other factors creating price pressures and squeezing margins.  

Combined, these challenges are taking their toll. In the UK, 2020 saw more than 11,000 stores shut their doors for good, with the pandemic adding extra fuel to the fire.  

Indeed, while many of these closures were in the works prior to the pandemic, the upheaval caused by the crisis has also rapidly accelerated the retail data evolution. Hybrid shopping became commonplace as the so-called ‘new normal’ emerged, with people choosing delivery and curb-side pickup to comply with social distancing advice and protocols. 

Beyond this, the way people pay for their items has also changed – contactless methods have been widely embraced and reached mass adoption on a scale that experts assumed would take years to materialise. 

The upshot of all this is that retailers are now data-rich enterprises. Despite the immense difficulties the sector faces, there is a huge opportunity lying in the mountains of information retail businesses have access to. 

From online shopping patterns to customer history and preferences, an unprecedented amount of data can now underpin the design and execution of smarter strategies to build loyalty and drive profits. This isn’t necessarily news for those big progressive players who have already understood the potential of data for cases such as understanding the user, personalising the offer, or optimising operations. While there’s still a way to go, the good news is that the sector, as a whole, is moving in the right direction.

Unlocking opportunities 

The key, however, to unlocking this potential lies in how data is managed, and this is where we are seeing challenges arise. 

The volume of data and dedicated personnel has grown exponentially, so data access and processing are hindered. Meanwhile, data silos and legacy platforms mean that analytics are often drawn from out-of-date or incomplete datasets that fail to take into consideration many of the external factors that could help them protect profitability by making better informed decisions.

To improve the agility of teams to develop new use cases, enhance existing applications and deploy better solutions requires new techniques to democratise data within retail organisations. 

And while different businesses will approach this in different ways and move at different speeds, there are numerous use cases emerging that give cause for excitement. 

Personalisation of the offer through recommendation engines is arguably the most well exploited. Ecommerce has made it possible to collect a large volume of data on user web interaction, meaning retailers can characterise users and products – and as a result, the system can estimate in real time what the customer needs. 

However, there are still improvements that can be made here. While most consumers are used to being given personalised recommendations by retailers, often these recommendations are inappropriate or poorly timed. Why? Because the data behind the analytics only paints half of the picture. 

Retailers can complete the picture by creating a single customer view that identifies the individual’s footprint across multiple channels and gives them a unique ID – what is known as 360-degree customer vision. This aggregated dataset helps retailers identify trends in customer behaviour and respond with more effective personalised recommendations, leading to increased conversion rates and reduced cost of sale. 

Demand forecasting is another key use case for retail data. For example, stock levels can be optimised based on forecasted demand by leveraging data patterns. Data can also inform how many employees are needed at stores and distribution centres throughout different times of the year. Meanwhile, on the consumer experience side, the accuracy of delivery windows and estimations can be enhanced by modern data analytics pipelines. 

Developing near real-time data capabilities 

Such systems will also enable retailers to process, understand and make use of data in near real-time. 

Firms have many data sources available to them, including point of sale, social channels, ERP systems, customer reward programmes, and inventory and logistics. 

For most use cases, what is needed are sufficiently updated data and models, with regular uploads of the latest data and updates of the systems. The update needs for each case must be considered to find the most cost-effective solutions. This is independent of the system responding in real-time - for example, a recommendation on the web.

Beyond this, high quality and easy access data have a strong impact on a company’s ability to profit from data. In this sense, teams need the right environment to enable agile experimentation and testing of solutions, and their deployment in production. Poor or difficult access to data, and a less robust environment, will lead to slow development and poorer results, and investing energy in low-value and error-prone tasks.

This is why there is an increasing focus on achieving better data governance, proper architecture design, leveraging cloud services, and using advanced approaches as the MLOps. Put simply, without these, retailers risk missing key trends and opportunities to upsell, as well as losing shoppers to other brands that better meet their expectations. In what is fast becoming a hyper-personalised retail world, consumers are willing to pay more for a richer customer experience, which in turn presents a big opportunity for retailers to cash in.

Although retailers are operating against an extremely volatile and challenging backdrop, there is no doubt that the opportunity to future proof and generate lasting growth lies in data.  Now is the time for businesses to exploit the information at their fingertips.

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