The retail industry has never been more competitive, with digital channels and touchpoints transforming the customer experience. Choice and flexibility are freely available to consumers who are increasingly demanding an exceptional customer experience from retailers.

This increased pressure on retailers to delight their customers while keeping costs and complexity low has led many retailers to adopt technology solutions like Internet of Things (IoT) devices, smart shelves with dynamic displays, augmented and virtual reality capabilities, and smart beacons that detect where customers are in the store and alert them to relevant specials.

This expanded digital environment is creating vast amounts of data, which can be a goldmine for retailers. Using insights from this data can help retailers make smarter strategic decisions that can dramatically improve their competitive edge and, ultimately, the bottom line.

However, to maximise the value of this data, retailers need to be able to aggregate, store, process, and analyse it. Both the volume and velocity of data can make this challenging. And, most of the data being collected is unstructured, which means it’s not easily organised or categorised.

The most valuable data for retailers includes social media mentions, online customer reviews, or contact centre transcripts. When data isn’t structured, it’s more challenging to analyse because not everything falls into neat categories. However, this data is where organisations can find the insights that can truly set them apart from competitors, so it’s essential to understand how to make it work for the business.

There are 10 ways that the right data insights can redefine retail operations:

1. Understand why products or services aren’t selling well based on customer reviews.

2. Discover customer sentiment regarding the business and its products to let the team quickly and effectively address problems before they escalate, reducing customer churn.

3. Make faster decisions based on a deeper understanding of what’s driving customer behaviours as well as the challenges that different departments face.

4. Identify which marketing initiatives are working well and which ones aren’t resonating to help direct resource allocation to where it will have the biggest impact.

5. Deliver hyper-personalised customer experiences that encourage customers to make repeat purchases and even become advocates for the brand.

6. See which customers are high-value versus those that cost too much to acquire and retain, then capitalise on high-value customers’ behaviours by reaching them in the most effective ways.

7. Optimise supply chain and operations by visualising bottlenecks and process breakdowns and seeing how products move both inside stores and when in transit.

8. Detect fraud such as stolen cards or returns of items that were stolen rather than purchased and prevent associated losses.

9. Manage pricing and competitiveness with real-time, dynamic comparisons that let the retailer ensure they’re on par with competitors’ advertised prices.

10. Forecast demand and sales volume depending on time of year, customer preferences, current offers, and more, with greater accuracy.

To fully leverage the power of data, retailers need to apply artificial intelligence (AI) and its subset, machine learning (ML). Using these technologies, retailers can bring together data from many different sources to gain a more comprehensive view of the landscape, leading to insights that would be impossible otherwise. Retailers can then make decisions based on those insights, moving more strategically and cleverly to seize opportunities ahead of their competitors.

AI and ML are particularly useful in turning unstructured data into actionable insights. Finding the right technology to facilitate this is crucial. Most legacy data infrastructure is too highly siloed and tends to separate storage into files and objects. While this may have worked in the past, unstructured data can’t be segregated in this way; it needs to be aggregated and analysed as a whole to deliver real value. Doing so requires storage platforms that support unstructured data, making it simple and efficient to store, access, manage, and analyse vast amounts of this type of data.

As the retail sector continues to grapple with uncertainty and evolving customer demands, data will help drive proactive decision-making for better results. This will help retailers cope with fluctuating demand, generate more sales and engender more customer loyalty, letting them compete more effectively.

Mark Jobbins is vice president and field chief technology officer for Asia Pacific & Japan at Pure Storage.