By Shridar Jayakumar

As competition increases, retailers are interested in solutions that will help them differentiate themselves from their competitors and attract customers. Big data offers retailers this opportunity as it allows them to study patterns in customer behaviour and tap into unmet customer demands.

Challenges facing retailers
Modern retailers are exploring technology solutions to better analyse structured data from diverse sources across their operations, including sales and marketing process, shelf space management, payment/refund, customer service, logistics and warehouse operation, strategic sourcing, procurement, financial management, back-office operation, merchandising and promotion. They are also looking for tools to gain value from unstructured data like social networks such as Facebook, Twitter, Google, web and software logs and information-sensing mobile devices.

These different types of data – structured, unstructured or semi-structured – together make up ‘big data’ and can add up to hundreds of terabytes or petabytes, so managing and extracting intelligence from it can be incredibly challenging. According to a recent industry survey, 34 per cent of respondents said they do not have a big data strategy. In fact, most organisations do not distinguish ‘data’ from ‘big data’.

In the retail market where margins are under constant pressure and product duplication is almost immediate, retail leaders need the capability to swiftly respond to changes in customer demand. They require technology solutions that allow improved decision-making and drive faster response times to market needs.

Getting that competitive edge
Retailers are interested in solutions that help them differentiate from their competition and maximise customer experience. They are compiling more data on customers and their purchases than ever before. This data provides a potential wealth of insight into the demographic attributes, tastes, preferences and buying patterns of the customer.

Big data can give insight customer churn by helping identify the reasons behind the churn and help retailers come up with solutions to address them.

The insights can be leveraged to optimise product offerings and promotions, give offers to specific customer segments or even precisely target consumers and drive higher returns and greater customer satisfaction. Retailers can use analytics to create loyalty programs and draw in customers to their stores. Even competitors’ customers can be tracked and analysed to understand industry trends and customer propensity to buy certain products or services.

Improving customer satisfaction
Retailers understand that improving customer satisfaction is vital. But it is more than simply tracking complaints. Combining structured data from sales, marketing and supply chain with unstructured or semi-structured data from surveys, syndication data and other outside sources can give retailers a new perspective of their customers.

For example, merging structured with unstructured content to find underlying customer satisfaction issues allows enterprises to proactively monitor customer satisfaction levels. In many organisations sales and customer service work in separate silos and customer feedback is often not allowed to flow freely between the different operations, resulting in ineffective distribution channels. However a COO would be interested in the convergence of sales information and call center operations to get a holistic perspective of customer engagement.

Tracking the social media and analysing feeds from Twitter and Facebook can help retailers find a correlation between product sales, support and customer voice to validate the true issues impacting customer satisfaction.

Another customer satisfaction issue solved by big data is to identify the most valuable customers from a 360 degree view; to be able to reward them with offers and benefits relevant to them, and to exclude those customers who merely take advantage of discounts without shopping from the merchants again.

The smooth transition
The ‘browse but not buy‘ behaviour is another well-known problem for brick and mortar stores around the world. In light of this, new dynamic customer experiences driven by big data analytics is becoming relevant for building more cohesive loyalty on the basis of assessing customer need.

Today’s customers expect a seamless shopping experience across the multiple sales channels – web, mobile, and physical store location. Before making a purchase online, they compare prices on the web, scan QR codes and browse in the stores. During the process, they want the hand over from one touch point to another to be as smooth as possible. While multi-channel management or connecting channels was the key issue in the past, omni-channel management is today’s challenge.

The big data platform and predictive customer behavioral analytics are now under serious consideration as CEOs of every major retailer feel the need for a holistic view of customers in terms of interaction models, channels, behavioral segmentation, responses, marketing strategy, and marketing execution.

Today’s reality is very different to the days of the traditional bricks and mortar retailing. Retail is now all about multiple online/offline channels, product proliferation, globalisation, easy comparison shopping, group-buying sites and individualised offers that can be delivered anytime, anywhere.

Big data helps retailers understand customers and truly see what actions trigger what behaviour in all the different segments and channels. It allows for real-time marketing execution at the time of purchase. It also enables improvements to loyalty programs by revealing what factors truly impact customer loyalty and retention, such as customer experience, ease of use, value for money and effect from rewards programs.

Shridar Jayakumar is the program director of EPM, BI & exalytics at Oracle APAC.