Ranking top big data objectives, the solution to customer-centric outcomes using big data is a top priority of retail organizations to understand consumer behaviors, their perceptions towards brands, their opinions about the quality of retail goods being sold to them and many more. A data-driven approach to achieving goals of customer-centric analysis is being more insightful and drives predictable outcomes for retailers.
Second is operational optimization to achieve goals in supply chain analytics, resource utilization, minimizing operational costs, increasing value propositions and achieving organizational goals. Risk management, financial management, working on new business models, and employee collaborations are third, fourth, fifth and sixth priorities respectively for retailers using big data approach.
This is consistent with what we see in the marketplace, where retailers are transforming from product-driven organizations to customer-centric organizations in which the customer is the central organizing principle around which data insights, operations, technology, and systems revolve. By improving their ability to deliver the right merchandise assortments to the right outlets at the right price and manage their inventories to these data-driven consumer demand signals, retail organizations are better positioned to seize market opportunities by delivering new customer-centric products at more predictable costs.
Big Data Objectives:
- Customer-centric outcomes
- Operational optimization
- Risk management
- Financial management
- New business model
- Employee collaboration
Major online retailer uses analytics to make more effective use of its customer data and create more focused and successful marketing campaigns using data-driven approaches. They rely on Internet-based photo services, relies heavily on the use of electronic marketing campaigns, including e-mail and newsletters, for its sales efforts. To increase sales, build loyalty and reduce churn, they need to better segment its customer base to more accurately target promotions and meet customer needs.
Retailers now develop customer profiles to help predict what a customer will buy according to customer attributes, previous purchases, and other factors. It then adjusts its marketing campaigns in terms of timing, frequency, offers and messaging to optimize sales. Customers’ DNA can also be tapped to present the most relevant offers and a more dynamic, personalized experience for each type of customer.
By recommending the right buy to right customers with exciting discounts and coupon increases the percentage of satisfied customers with any purchase. Also, customers’ DNA can help retailers to increase customer loyalty substantially. Not only this, the customers’ DNA based recommendation system can minimize the un-subscription rate and percentage of retainer customer can increase substantially. By tailoring customer interactions and using a more targeted marketing campaign using customers’ DNA, the retail organizations can increase their sales substantially per year and can beat competitors with a huge gap in yearly sales.
