For years, retailers have been ahead of the game in collecting consumer data. Consider loyalty card schemes: from the retailers’ perspective, these have little to do with loyalty and a lot to do with gathering data and getting to know the customer. Some have been operating for decades – think of the wealth of data collected. However, the landscape has changed, and such schemes offer one piece in a huge and complex business intelligence puzzle, as a smorgasbord of connected devices and new data sources have joined the mix. Big data analytics makes it possible for retailers to effectively connect the dots and bring actionable, data-driven insight to their decision-making processes.
Let’s look at some of the key sources retailers can tap to win big with their data.
Whether it’s purchasing data from loyalty cards or online sales records, retailers collect a huge amount of data about customer buying behaviour, generating real insight into their customers, from identifying buying trends to monitoring behaviour patterns. However, to extract real value and deep insights, retailers must apply big data analytics at every stage of the retail process, turning analytics into actionable predictions. Understanding what the popular products will be, predicting consumer trends, and accurately forecasting demand gives retailers an enviable competitive edge over the competition.
Big data analytics really comes into its own when dealing with historical information. By capturing large volumes of data, retailers can identify trends and begin to forecast what the future holds. By using predictive analytics to analyse customer activity captured over a long period of time, retailers have access to extremely detailed insight into consumer behaviour and likely actions. Analysing all this historical data also enables retailers to detect and identify customers at risk of churn, identify product cross- and up-sell opportunities, and profile target demographics. Think about those long-standing loyalty schemes I mentioned – they get even more valuable when subjected to modern analytics tools.
Retailers can also utilise big data to vastly improve customer experience and ensure shopper satisfaction. Big data analytics offers an opportunity for interactions to be tailored to each customer by understanding their attitudes and factoring in elements like real-time location to deliver a personalised experience.
Such analytic insights are not excluded from the brick-and-mortar world. In-store beacon devices will also enable retailers to monitor the consumer buying journey as shoppers progress through the store. By applying big data analytics to this data, retailers can, for example, optimise the position of stock and promotional stands within the store to ensure greater customer engagement.
Supply chain data
The supply chain is another wellspring of actionable data. Retailers receive a large volume of B2B transaction types from their suppliers on an annual basis. With big data analytics, retailers can analyse these transactions and pinpoint supplier performance trends in order to streamline processes.
Supply chain analytics is relatively new, however, the retail industry is beginning to take advantage of the insights and business intelligence it can provide to improve supply chain performance. For example, many retailers request suppliers exchange Advanced Ship Notices as these notify the retailer of an impending delivery of goods to their store. Retailers can use this to track trading partner performance across their supply chain.
Beyond tracking historical performance, smart technologies can take other factors into account. If a supplier in Brazil regularly ships content to Europe, but an Atlantic storm is brewing, it might be best to order that stock from a separate supplier on that occasion to ensure a smooth delivery. Big data analytics offer the ability to see beyond issues with the supplier itself, incorporating external factors which may previously have been overlooked.
Analysis of historical information plays a large role in improving logistics processes as it can also be utilised to make more accurate forecasting estimates. These insights can then be supplied to the operations and logistics department. As one example, retailers can consider retail spend over Christmas and then use this information the following year to predict the levels of new stock required to cover the holiday period. While forecasting potential stock levels has previously been considered risky, big data analytics allows retailers to more accurately estimate both required stock levels and also potential consumer spend levels.
Internet of Things
Developments in connected devices and the Internet of Things (IoT) mean that big data will play an even larger role in transforming visibility across the supply chain. IoT extends beyond consumer wearables or connected white-goods; it enables online businesses to track activity and monitor the environment through the wider connected network. As such, retailers will be able to fine-tune logistics by monitoring shipment delivery information, optimising delivery routes by factoring in environmental changes, and monitoring third-party providers via B2B transactions exchanged between the company and suppliers, as well as examining the timeliness of their deliveries.
Connected devices will enable retailers to monitor product consumption patterns within stores, so new inventory can be ordered as soon as the analytics platform detects that stock is running low. Retailers have traditionally implemented Vendor Managed Inventory processes with their suppliers. Moving forwards the combination of B2B analytics and IoT will enable the adoption of ‘Device Managed Inventory’ processes. While particularly useful for higher value goods, in theory, retailers can take advantage of this process for any item to improve overall customer satisfaction levels by ensuring stock is always available.
Big data analytics is dramatically changing the retail landscape for both business and customer. Retailers can benefit immensely from an analytics-driven approach that will help them understand how their customers are using their products and services, and how their operations and supply chain are performing. Retailers should look to apply big data analytics to generate deeper insights across the entire value chain of retail operations and streamline their decision making processes. Right now, the ability to utilise these analytic insights is a real competitive advantage, but in the not-too-distant future, it will be a necessity, and those that have failed to adopt a suitably transformative approach will find themselves lagging dangerously behind.
Mark Gamble, Senior Director of Technical Marketing for OpenText Analytics