Data utilisation strategies in retail have to be unified

IT is important for retail businesses to know their customers. By that, I mean retailers need to know customer buying behaviour, demographics, payment preference, and most importantly understand what impacts their purchase decisions. Retailers have been collecting this type of information for decades through survey forms, loyalty programmes, etc. These days, customer information comes in the form of data - customer profiles, purchase and payment records, customer service and enquiry records, and many more.

This is why business intelligence (BI) and data analytics are such important tools for modern-day retailers. In fact, Gartner says advanced analytic capabilities are central to retailers' strategy in competing in the increasingly digitalised marketplace .

Shopping is no longer a single-channel or single moment experience. Customers are being served from all fronts - online and in physical stores - and they are expecting experiences to be just as personalised.

To be able to unlock the full potential of a data strategy, retailers need to be able to, quite simply, rethink the way data is used.

BRINGING DISPARATE DATA TOGETHER

Retail businesses should be data-rich organisations because transactions are very clear cut in retail. The number of customers, the purchases they make, number of visitors to the store (online or physical), how much each customer spends - all these are "capture-able" data. Yet, data collected by retailers are often found living in silos.

For instance, retailers may be collecting and analysing data generated by customers that reveal their buying behaviour and purchase preferences. At the same time, they have in-store captured data, often from various sources including mobile devices, Internet of Things and sensors to track and monitor foot traffic or interaction with merchandising displays. Additionally, they may have data collected by customer service. This is data of customers who have made purchases but are now contacting the business for enquires or feedback. The problem for many retail businesses is that much of this data is disparate and not connected.

While many retailers want and need agile analytics, they are hindered by their disjointed data strategy, which prevents them from accessing it in time to get the right data sets to the right people quickly. Successful retailers must be able to see and understand different types of data, in one holistic overview, in order to leverage the potential for actionable insights. As tools get more sophisticated and sources of data keep increasing, retail companies need to immediately set in place a strategy for integrating or joining data sets. This helps them to gain a multi-dimensional view on their business and customers.

Metro - an iconic retail brand in Singapore with a departmental store chain that now covers China and Indonesia - constantly collects a variety of in-store data. The Metro team reads into data that reveals peak and lull shopping periods, inventory flow, customer purchase decisions, and more. By integrating data from the multiple sources, the data analytics team at Metro is able to make informed decisions in its operational and promotional strategies based on this multi-dimensional view of the business.

One good example is when Metro's management decided to stop ordering half-size shoes when they realised through data analytics and visualisation that their stores have been storing many half-size shoes in their inventory when customers are not buying half-sizes. They usually prefer to go either one size up or one size down.

Analytics is no longer just for data analysts. Non-analysts working across different divisions in a retail company increasingly need to become acquainted with data. It is important for retail businesses to empower their employees with data tools because timing is everything. For instance, with data-driven information, retail assistants will be able to quickly track inventory availability and provide customers with the product or service much faster.

Sophisticated BI tools used to require implementation by IT teams. Some would require IT to do actual data extraction and analytics, and the entire process can take days or months. Today's retailers operating in a competitive business environment do not have the luxury of time. They need to know their customers, and make decisions fast - often making decisions right at the frontline.

Retail staff need to have the ability to see and understand data faster in easy-to-understand and intuitive ways. Self-service BI platforms make data more accessible to employees from all levels, from retail assistants to C-level executives. Self-service BI tools also replace manual reporting processes as they offer the right person access to the right data at the right time.

Furthermore, many self-service BI tools allow retail staff to generate data visualisations. That is, they allow users to automatically turn columns and rows of data into visual presentations and dashboards such as charts or graphs. This allows users to better identify outliers and trends in their data. Visual communication is now a must-have skill for all managers, because more and more often, it's the only way to make sense of the work we do .

Many retail customers have grown accustomed to knowing exactly which items are available in the region that they are in and when a product may be ready to be picked up at the nearest store. Retailers are sharing product counts on websites, mobile apps, and in paid advertisements, not only to further entice customers but to also improve customer experience. Especially when working online at Internet speed, retailers can only provide that information if they are working with real-time data.

INTEGRATING DATA STRATEGIES

Indeed, real-time data is essential these days and should no longer be considered a good-to-have. Information-loaded consumers these days have short attention spans and have many alternative options to choose from. Retail business is competitive and there are no 'ifs' or 'buts' to it. It is even more competitive now with e-commerce thrown into the picture. Consumers demand a personalised shopping experience and they will go to where they can get it if a retailer pales in comparison in this area.

To gain a competitive edge over others, retailers need to deliver beyond what is expected. This is where predictive analytics comes in. A branch of advanced analytics where users analyse data to make predictions of unknown future events, predictive analytics allows businesses to anticipate customers' needs and preferences. Making data-based projection of future trends allows retailers to operate in such a way to truly treat customers as individuals, and make informed suggestions to them and prepare their stores - such as stocking up on certain inventories - for future requirements.

Today's business climate demands that retailers be data-driven. Retailers need to be able to see the overall business environment from multi-dimensions. They need to be able to respond to customer needs and market changes immediately. Retailers need to also monitor their operations and make quick adjustments effectively and quickly.

In other words, retailers need to have a holistic data strategy that blends and integrates data. They need to put data into the hands of their rank and file staff so that they can attend to customer requests and make informed decisions on the spot.

Finally, to get ahead of the curve, retailers need to look to the future. They need to tap predictive analytics to proactively preempt customer wants and market trends. Most importantly, neither of these initiatives should be isolated from the other as data strategies need to be all-encompassing and integrated.