Big data in Retail - Hindsight, insight, foresight.
Updated: Oct 19, 2020
The scientific element to art of marketing.
Big data may be seen as a scientific element to marketing and is an integral component in any competitive retailers arsenal. In today’s consumer savvy, consumer centric retail space, the abundant data (both structured and unstructured) extracted from the customers digital footprint is a virtual goldmine for retailers.
Modern consumers crave a seamless, personalised shopping experience across all mediums and devices and the insights revealed enable retailers to create targeted marketing campaigns. It is imperative businesses understand what motivates and appeals to its different customers in order to personalise the customer experience.
To stay competitive in a rapidly transforming industry, online/offline retailers are adopting a ‘data-first’ strategy to understand the buying behaviours of their customers, mapping them to products and developing cost effective marketing strategies.
Data driven insights assist retailers in understanding their customers wants, needs and desires using a host of metrics, including but not limited to, social media preferences, internet browsing behaviours, device preferences, POS (Point Of Sale), loyalty programmes, in-store video surveillance, virtual dressing room technology, reservation of products, in store sensors, geolocation and the IOT (Internet Of Things).
Based on a customer’s purchase history, big data can help analytics to predict what a customer is likely to purchase next and by training machine learning models on historical data, the retailer can generate accurate recommendations even before the customer leaves the Web page!
Leveraging highly accurate insights into the way in which shoppers engage with products enables retailers to tailor their merchandising, promotions and marketing campaigns to meet the ever demanding expectations of their customers. High-priority customers can be identified in this way and advertised to specifically. This personalised customer experience translates into customer satisfaction, loyalty longevity and ultimately ROI (Return On Investment).
The benefits of big data analytics are myriad in the many other facets of retail:
Understanding data driven insights into customer purchasing habits, retailers are able to best assess which products are in demand, which they should discount, and which they should discontinue thereby meeting exacting customer needs. Based on this, retailers can invest appropriately and cut costs.
Trend forecasting algorithms in big data assist brands in monitoring demand fluctuations in real-time enabling them to make smart market predictions and forecast consumer trends yielding a greater ROI.
Insights into real-time customer transactions allow retailers to price products accurately for optimal sales and competitive edge. Big data technology can also be utilised for what is known as ‘markdown optimisation’, that is, when prices of specific items should be dropped.
Market basket analysis
Market basket analysis is a standard technique used by retailers to reveal which baskets or groups of items customers are more likely to purchase together. Analysing and understanding 'the path to purchase' is an excellent method for retailers to understand customer buying patterns. Social media sharing platforms such as that on Instagram increment the mass of data retailers use to generate group/basket recommendations.
Logistics and inventory
Big data is used in the optimisation of ‘back end’ supply chain management and logistics. Analytics systems allow retailers to collate their historical purchase and stock data to accurately predict demand for products and dynamically manage their inventory.
The power of big data technology creates transparency into internal activities in order to find fraudulent patterns. Retail fraud such as employee fraud, refund fraud, discount abuse can be exposed by employing big data analytics. Predictive capabilities create a baseline sales forecast at SKU level and any anomalies detected could indicate fraud is at play.
Big Data Analytics are used in risk management assessment enabling businesses to evaluate benefits, identify areas of weakness and gain insights into potential risks.
Labour and store hours
Staff scheduling, store hours and even store expansion are aided by big data retail analytics. Retailers can forecast customer demand by analysing data and allocate their staff and hours of business accordingly. Mobile location analytics provide insights into real-time consumer behaviour, showing foot traffic patterns and high density locations.
Today's tech savvy consumers expect a smooth retail experience across a host of mediums and devices. Insights derived from these various devices and mediums are used to create omni-channel campaign strategies thereby exponentially increasing customer base, satisfaction and retention.
Online payment security
Perhaps one of the more obvious benefits of big data technology is that it integrates the different payment functions into a centralised platform thereby reducing the risk of fraud in real-time. Analytics can detect money laundering transactions that appear as legitimate payments and notify customers, banks and credit card companies before permitting the purchase to go through.
The adoption of a data driven analytics approach therefore enables retailers to understand their risk management profile, optimise logistics and inventory, implement secure online transacting, understand demand, predict market trends, make sharper pricing decisions, foster brand awareness, create seamless omni-channel shopping experiences, cultivate and retain loyal customers and ultimately, to increase ROI.
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