For retailers, the answers to many of their most pressing questions lie in Big Data — the volumes of structured and unstructured data generated across the gamut of customer interactions. The good news is that the answers are there. The bad news is that data coming from point of service and mobile devices, e-Commerce and social media sites, customer loyalty systems, even UPC and RFID readers is just as likely to conceal insight as it is to unveil it.
The data is messy, it doesn’t fit neatly into defined data structures, it is high volume and it needs to be processed quickly. It can be overwhelming to wrestle the data into something meaningful and deliver it in a way that is useful for merchants, marketers, e-Commerce and store managers, sales associates and customer service representatives.
Ready Or Not, Here It Comes
Most retailers know they must get a handle on their data but find their core infrastructure systems unprepared to support even the most basic data analysis. When asked by Oracle in 2012, nearly one in three retail CIOs gave their organization a “D” or “F” in preparedness to manage the data deluge, and 53% said they cannot give business managers access to relevant information. While those findings are cause for concern, the fact that the path forward is uncertain is more troubling still, especially when executives estimated they lose as much as 10% in potential revenue simply because they cannot apply the incoming information.
Blazing The Big Data Trail
To help retailers find the path forward, here are five “must have” components of a successful Big Data strategy.
- Establish a common data model. Centralizing analytical data in a common enterprise information model is the first step to providing a single accurate view of business insight throughout the enterprise. The common data model establishes conventions such as fields, naming, attributes and relationships so that items, or customers, appear the same across pricing, inventory, transactional and other systems. This is a critical first step to ensuring day-to-day and longer-term business decisions are in sync and reflect a holistic view of what’s happening in finance, with customers, suppliers and promotions.
- Deploy a scalable, open-standards system. Using an open-standards platform allows retailers to leverage existing systems while reducing IT costs and gaining flexibility in terms of serving the business. Systems adhering to open industry standards are readily available and are preferred to proprietary systems for a number of reasons, not the least of which is their ability to integrate with existing legacy systems, systems from multiple other vendors and future add-on solutions. It is the open system that affords options, preventing a reliance on one vendor or group of vendors and ensuring the ability to leverage emerging technologies.
- Accommodate external data. This is an extension of the first two items but warrants mention. Deriving meaning from Big Data requires integrating the basics, like transaction history, purchase frequency and web-behavior, and accommodating another set of external environments such as third-party market analysis and CPG vendor data, social media, demographics, weather and finances. The platform must be able to harness information in multiple ways, from structured databases and distributed predictive analytic systems, to mining unstructured data.
- Model dashboards once and deploy them anywhere to any device. Today, information can be accessed on everything from mobile phones and tablets to personalized dashboards on laptops and cloud-based netbooks, or via in-store portals. Establish a common infrastructure for producing and delivering enterprise reports, scorecards, dashboards, ad-hoc analysis and OLAP analysis and empower end-users with real-time, 24×7 access to self-service BI, mobile BI, and the ability to create their own BI content and personalized dashboards using a simple, easy point-and-click interface. In effect, high performance BI tools are designed to be robust and yet consumer-friendly, where users can easily search reports by keyword, and information can be displayed in many visually orientated formats, including tables, interactive charts and maps, and spatial graphs.
- Provide users with actionable “closed-loop” analytics. When retail analytics leverage a full range of enterprise and retail business systems, the next step is to ensure that users can act on the information without leaving the application and opening another. These actions can include things like triggering a workflow to order more stock, kicking off a promotion based on events, or metric thresholds being crossed. They can also be simple things like notifying people of key information like guiding someone to do further analysis. This type of closed-loop, cross-domain analytics between retail and ERP/CRM ensures that Big Data will have an immediate informative and beneficial impact on day-to-day retail operations.
Establishing the foundation for leveraging Big Data is worth the effort. Understanding customer segments, improving customer loyalty and knowing what determines market basket size — these are insights a sound strategy affords. When business users can take action right from the retail analytics dashboard, the impact on operations and customer experience is immediate.
As Solutions Market Director for Retail Business Intelligence at Oracle, Marisa helps retailers leverage new merchandising and customer analytics tools to increase sales and performance across the enterprise. She has extensive experience in Business Applications and Business Analytics software.