Big data offers numerous potential benefits to online retailers. However, due to the often overwhelming nature of big data analytics, many opportunities for customising the consumer experience are going largely untapped.
Australian retailers have been slow to embrace ‘big data.’ Many are finding it expensive and difficult to implement. But they can’t afford to ignore it. In fact, big data is crucial and has the largely untapped potential to transform almost every facet of retail marketing.
For those who don’t know, big data is the term used to describe data sets too big to process using traditional techniques. For example, it might combine data from web browsing patterns, social media activity, store foot traffic, purchase history, and industry forecasts.
With new technology transforming how consumers shop, and consequently creating huge amounts of data for retailers to sift through, big data is becoming increasingly relevant to retailers. But this should be seen as an opportunity, not a threat. Savvy retailers are using big data to predict trends, understand markets, prepare for demand and monitor real-time results.
Consumers are increasingly demanding customised retail experiences. So now more than ever retailers need to understand their customers and treat them as individuals, with relevant and targeted offers; big data helps makes this possible.
A recent survey by Infosys found that 78 percent of consumers say they’d be more likely to purchase from a retailer again if they provided offers targeted to their wants and needs.
Some of Australia’s biggest retailers are starting to get on board. In 2013, Woolworths bought a 50 percent stake in analytics firm Quantium. Woolworths is now using information gleaned from credit cards, loyalty cards and its e-commerce sites to find growth opportunities.
Even though big data can seem overwhelming, the possibilities are limitless. Here’s how big data can help:
Customer service: Retailers should keep track of their customers’ individual. Also, if a customer has made a complaint — formal or otherwise — it should be recorded and acknowledged for quicker resolution. Large data sets about customer service queries can even be used to predict how satisfied a customer is, as used by companies such as Vodafone.
Increasing sales: Big data’s pay-off lies in its ability to find the most high-value customers. If data about your customers’ purchase habits, preferences and other interactions with the brand are recorded, this data set can be used to predictively map the highest value customer segments. Understanding what behaviour leads to a high-value customer can lead to initiatives that prioritise communications to this customer set to increase their affinity with the brand.
Predictive analytics: This involves using big data to identify events before they occur. For example, retailers can predict demand for certain products to ensure they have enough stock. Or, store foot traffic analysis can help retailers understand how to best set up their bricks and mortar shops.
Managing fraud: Large data sets can also help to increase fraud detection. Retailers should process and analyse their sales transactions against defined fraud patterns.
Big data allows retailers to broaden their market and connect with consumers in the digital age. Introducing it can be a gradual process. It doesn’t need to happen overnight. But the sooner it happens, the better.