Big Data analytics to save money for retailers

The modern retail conducted in a business environment is constantly changing and poses new challenges at all levels – from the effective management of inventory through the store management to the planning and execution of marketing campaigns, and the list goes on. The good news is the retailer is not alone in this battle. The power of Big Data analytics can bring more predictability and control, and help create greater synergies across these processes to maximize sales and profits. Retail Touch points out a blog post by Deepika Goel, an American expert on Big Data, and provides some examples on how Big Data can help your business:

Inventory Planning

Planning

• Procurement optimization — Big Data  analytics can monitor real-time weather updates and seasonal and historical purchase trends by geography, to accurately predict farm production and commodity prices. This gives ammunition to procurement teams while negotiating sourcing costs.
• Stock ups/downs — Big Data analytics can track both structured information sources like point-of-sale data by store, price, promotion, seasonality (day of the week/week of the month), and unstructured data such as social media feeds, to capture consumer preferences across multiple channels. This can increase profits through streamlined demand planned by geography and reallocating inventory to locations that expect higher demand.
• Contract management — A retailer with whom we work experienced multi-million-dollar margin dilution through overpayment for discounted inventory. Through text analytics, compliance issues in contract and invoicing were captured, which helped recover more than $5 million in one year.

In-Store Operations and Execution

• Category management — At any given point in time, an average retailer stocks over 50,000 items. With constrained store space and diversified shopper demands, it often is very difficult to select the right SKUs and determine the best location for products. With advances in Internet of Things (IoT) technology, sensor data deployed in the stores can capture the shopper traffic and hot spots. Access to this data in real time dramatically increases the ability to more effectively manage product placement, as well as negotiation with manufacturers for sweet spots based on the store’s “heat map.”
• Shopper experience — Industry analysts estimate that 60% of items in a shopping basket are prefixed or associated; i.e. they are bought together. Big Data analytics aimed at understanding the exact placement of a given item can increase sales of such products and improve the customer experience.

Marketing

• Personalized targeted campaigns — Analytics help retailers develop more targeted marketing which is critical to meet the ever-changing demands of consumers with diverse choices, backgrounds and demographics. For example, a mid-sized retailer wanted to increase men’s apparel sales through coupons for customers buying from the women and children’s sections. By analysing geographic, demographic and purchase patterns, the retailer developed a hyper-targeted Father’s Day campaign that achieved a 27% higher redemption rate at a much lower cost.
• Omni channel marketing — e-Commerce, m-Commerce, and brick-and-mortar stores are like frenemies in the retail space; analytics helps integrate these channels. For example, companies can use GPS data extensively to capture shoppers’ locations and send out campaign notifications for a nearby store to maximize sales opportunities.
• Loyalty card programs — This age-old marketing vehicle has seen a lot of advancements lately as retailers use analytics to optimize digital data in social media and predict future purchase patterns. Customers also now extensively use smartphone apps that make redemption easier, which can also significantly increase the overall redemption rate of coupons.