Amazon is growing on-demand services after reporting a 20% sales increase in 2015.
Knowing what their customers are doing as they’re doing it helps some online retailers stay ahead of the game
Imagine collecting data about customers and their activity across retail channels in real time and having the ability to analyze conditions as they happen.
Success stories include Overstock.com, an online retailer using fresh data to refine marketing campaigns, personalize services in real time and, ultimately, better serve customers to drive additional business. Demand-driven merchandising is the new model for success: having the right product available at the right time (and price), and knowing who will buy it. Real-time technologies have arrived, giving forward-thinking retailers a winning I.T. strategy and an important competitive edge.
The Internet enables retailers to maintain a dialogue with customers. In the last several years, retailers have also been working toward collecting more complete and timely customer data from their store point-of-sale as well as online order management systems. They’re doing so to leverage critical customer data and fine-tune the total customer experience across multiple channels.
Without complete and accurate transactional data coming from in-store POS, as well as from online and catalog channels, no investment in better business intelligence systems or tools will make a difference. Systems must be capable of not only capturing accurate product information but also customer information related to each transaction to better match consumers and the products they want, and when and how they want them.
The good news is technology solutions are available to help retailers know and keep customers. For example, most point-of-sale vendors support an XML standard-developed by the Association of Retail Technology Standards XML committee of the National Retail Federation-to facilitate the real-time exchange of POS data with upstream transactional and business intelligence systems. Many retailers use internal messaging subsystems for the asynchronous exchange of messages in XML format to other systems as a method of integrating different generations of business applications across channels.
Unlike their tightly bound predecessors, modern POS systems use many of the same technologies as those found on web-based applications, with browser-based or browser-like thin presentation layers, an application layer, and data layer, and with continuous XML messaging capabilities. This enables retailers to use the same set of business rules and data regardless of the presentation.
These systems also provide data that enables the retailer to relate a customer to a store market basket: not only does the retailer know what gets sold, but what gets sold together with other products, and who the customer was. This information is an invaluable new piece of business intelligence because retailers now can understand the affinities between items purchased, and also begin to understand who their best customers are and what they buy. Combined with information gleaned from online and call center channels, retailers gain powerful insights into true customer demand across multiple channels.
As timeliness of data becomes critical, more organizations are moving toward continuous feeds into business intelligence databases so they can eliminate data and analytic latencies to access fresh data and respond more quickly to changes in the selling environment. Survey responses from a July 2007 RSR Research benchmark study, “The Next Generation of Business Intelligence: Driving Customer Insights across the Retail Enterprise,” show many retailers are moving toward continuous feeds into a data warehouse.
Retail winners (those companies that outperform their peer group) are more aggressive, with 48% indicating they now collect data continuously from the selling environment. Winners are clearly aware of the opportunity to improve customer satisfaction, top line revenue, and gross profit profitability by using technology to proactively respond to conditions in all channels.
Real-time customer insight
First and foremost, retail winners have a focus on the multi-channel customer dimension, in addition to the traditional dimensions used for merchandise planning (product, location and time). It is the integration of data, technology and process that sets them apart. These winning organizations understand demand because it is based on actual customer data gathered from the shopping experience, and they demonstrate shared best practices, which include:
l A focus on the quality of data. Legacy POS hardware and software can only take a retailer so far. But a well-architected and modern POS system that can accurately capture customer-related data with the market basket, and is integrated to multi-channel business intelligence capabilities, will provide accurate and timely information that can be used to customize the online shopping experience to meet the true demands of consumers.
l A focus on real-time access to real-time information. Fresh information gathered from the shopping experience is invaluable and drives decisions about what products and services to offer, when to offer them, how to promote and price them, how to present them most effectively, and even who the target customers are. And the faster a retailer can get at the information, the faster it is able to fine-tune marketing and stock to effectively meet customer demand.
l A focus on a single view of the customer. Technology can address the challenges of gaining the single view of the customer across channels (online, phone, store) either by having one physical repository of customer and sales history data or by creating one logistical view of customer and sales history data through data integration. Recognizing that retail data sets can be huge-measured in many terabytes-the problem won’t get any easier by waiting. Retailers of any size should address this now to avoid negative impact to business.
Moving to real-time data
What differentiates any successful retailer is not its products but its services, and these are increasingly driven by the information asset. What does it take to deploy a real-time business intelligence system? First, some homework.
Many organizations are hesitant to move to a real-time architecture for fear of exorbitant cost and complexity. Yet recent cost studies have shown that deploying real-time technologies and continuously streaming data may be no more expensive than their batch or ETL extract transform load counterparts. (ETL refers to the use of a single tool to extract, transform and load information between multiple databases.)