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Traditionally, the retail industry has lagged behind other industries in adopting new technologies, and this holds true in its acceptance of BI technology. Some industries, such as financial services, have become very sophisticated in using BI software for financial reporting and consolidation, customer intelligence, regulatory compliance, and risk management. However, retailers are quickly catching up and beginning to recognize the many areas of BI that can be applied specifically to their businesses.

Game changing

The competitive game is changing for retail. As the industry continues to consolidate, retailers have begun to realize that using technology to better understand customer buying behavior, to drive sales and profitability, and to reduce operational costs is a necessity for long-term survival.

Retailers are now paying significant attention to BI software, specifically in the areas of merchandise intelligence (including merchandise planning, assortment, size, space, price, promotion, and markdown optimization), customer intelligence (including marketing automation, marketing optimization, and market basket analysis), operational intelligence (including IT portfolio management, labor optimization, and real estate site selection), and competitive intelligence. There are many factors that have led retailers to adopt BI software: increased competition, the need to squeeze more profitability out of less space, prevalent credit card usage, the Internet’s role as an alternative sales channel, the popularity of loyalty cards, and soon, RFID (radio frequency identification). These milestones have created a wealth of data that retailers are now beginning to appreciate and use.


Intelligent Enterprise

Within individual companies, we view the history of BI in retail through a method that we devised to describe the status of any company’s evolution toward becoming an intelligent enterprise. We believe that organizations pass through five fundamental stages as they advance in their use of BI as a competitive differentiator:

Operate — At this most basic level are the companies rife with information mavericks: the guys in basement offices hammering away on desktop spreadsheets. If they go, the knowledge goes with them. There are no processes, and each request becomes an ad hoc data rebuild, resulting in multiple versions of the truth, with the likelihood of a different answer to any one question every time it is asked.

Consolidate — At this stage, a company has pulled together its data at the departmental level. Here, a question gets the same answer every time, at least within the department. However, departmental interests and interdepartmental competition can skew the integrity of the output and result in multiple versions of the truth.

Integrate — At this point in the evolution, a company has adopted enterprise-wide data and bases its decisions on this more complete information. This company is beginning to have a true awareness of additional opportunities for the use of BI to improve processes and profits.

Optimize — At this stage, the company’s knowledge workers are very focused on incremental process improvements and refining the value-creation process. Everyone understands and uses analysis, trending, pattern analysis, and predictive results to increase efficiency and effectiveness. The extended value chain becomes increasingly critical to the organization, including the customers, suppliers, and partners who constitute intercompany communities.

Innovate — This level represents a major, quantum break with the past. It exploits the understanding of the value-creation process acquired in the optimize stage and replicates that efficiency with new products in new markets. Companies operating at this level understand what they do well and apply this expertise to new areas of opportunity, thus multiplying the number of revenue streams flowing into the enterprise. Armed with information and business process knowledge, organizations approaching the innovate level will introduce truly innovative products and services that reflect their unique understanding of the market, their internal strengths and weaknesses, and an unfailing flow of ideas from continuously engaged employees.

We are finding that most large retailers have reached or are approaching the integrate stage, with many making great strides toward the optimize and innovate levels.

There is an enormous opportunity for the evolution to continue — within every retail organization.

The Presence of BI in the Retail IT Infrastructure

In the typical retail IT infrastructure, there are two fundamental categories of systems: transactional/operational systems, such as POS and purchase order management systems; and analytic/BI systems.

Operational and transactional systems such as merchandise management, ERP (enterprise resource planning), and POS, are very good at what they do — organizing huge amounts of operational data and transactions. These systems can tell retailers what has happened in their business and what their customers have done — last week, last month, and last year.

It’s critical, however, for retailers to understand what will happen: what the demand will be for a select assortment of merchandise, what impact an incremental price change will have on demand, which floor plan will sell more designer shoes, which customers will respond to a direct mail or catalog offer.

Real value comes from systems that go beyond the limitations of operational software alone, systems that can take operational data and create enterprise intelligence and predictive insights.

These BI systems must combine data management (consolidating, organizing, and cleansing huge amounts of disparate data from varying systems and platforms) with predictive analytics (data mining, forecasting, optimization). When they do, retailers can make sense of customer, product, supplier, and operational data and draw insights that will help them run their businesses better and more profitably.

Leading retailers around the globe — like Wal-Mart, Foot Locker, Staples, Williams-Sonoma, and and many others — have begun using BI and analytics to make an array of strategic decisions. These include where to place retail outlets, how many of each size or color of an item to put in each store, and when and how much to discount. The effects of these decisions can save or generate millions of dollars for retailers.

The Strength of the Market for BI in Retail Today

The market is very strong and getting stronger. While it is difficult to find a comprehensive suite of retail-specific BI offerings that spans the spectrum from competitive intelligence to merchandise planning and optimization (product, price, promotion, and placement) based on customer insight, to knowing how to maximize the ROI on the next marketing campaign, to understanding where to build the next store, to reducing supply chain costs. Retailers are telling us over and over that they are seeking a single, stable, reliable, and proven provider of superior BI solutions. They are implementing projects that span multiple years and will deliver value for years to come.

The Retailers that are Realizing the Most Benefits from BI

We find that the retailers that are realizing the most significant returns on their investments are those that take a purposeful, pragmatic approach to establishing an intelligence platform upon which to base all other BI solutions. A single, reliable demand forecast, for instance, can also be used in merchandising, marketing, logistics, store operations, call center staffing, etc., for operational benefit. BI that remains segmented by functional area can provide some value, but retailers can realize a much larger return by building the foundation upon which the rest of the house will stand. This is true of both top-tier and midmarket retailers, regardless of segment.

Photo by rawpixel on Unsplash
Photo by rawpixel on Unsplash

Specific Areas in Which Retailers can Benefit Most Include:

Merchandising — This is clearly the most important area of a retailer’s business and an area where retailers are beginning to exploit the full value of BI. Analysis of past performance, combined with plans and forecasts of future customer behavior, leads to more accurate initial allocations of merchandise across channels and stores. Assortment and size optimization that are based on customer demand patterns ensure that the correct assortments, size, and case-pack distributions get sent to the correct stores. Daily price, promotion, and markdown optimization ensures that items are priced for optimal profitability, both preseason and in season. Space automation and optimization ensure that departmental sales and profit per square foot are maximized, and products are given the correct inventory and space on the shelf or on the rack. Optimized fulfillment ensures that products are allocated or replenished based on demand. Accurate analysis also results in a more efficient use of manpower in picking, packing, and shipping the first wave of product, while minimizing additional, costly payroll expenses to facilitate transfers between stores, vendor returns, changing signage and labels for markdowns, and otherwise correcting mistakes.

Marketing — By understanding customers better — whether by profiling, segmenting, gauging propensity to respond, or using market basket analysis — retailers can create better-defined targeted campaigns, reducing expenses (printing, paper, postage) while increasing response rates, revenues, and gross margins. Also, as retailers gain a better understanding of their customers’ buying behavior, this analysis can then be used to create more effective merchandising plans for the next season.

Operations — Understanding and predicting changes in demand — by hour, by day, by location, by promotion, by price change — means that the store floors, the catalog call centers, and the fleet crews delivering replenishment orders from the DC to the store are all appropriately staffed. This understanding also leads to optimal productivity since store-level human capital costs can be scheduled better and managed more efficiently.
The Integrated Solution

It is important to note that a good BI solution will be able to integrate with any other system or platform. That said different BI solutions need to interface with different operational systems for different purposes.

A solution seeking to use customer behavioral data to make better merchandising or marketing decisions needs to interface with sales transaction systems, loyalty systems, in-house credit systems, coupon redemption systems, catalog and Internet customer data systems, and so forth. A system that recommends optimized price changes should interface with the price management system, the item master, the system that generates labels, etc.

There must be a closed-loop interface between the operational systems that retailers rely upon to conduct day-to-day business and the BI systems that help them conduct that business more efficiently and profitably.

The Future of BI in Retail

BI will be defined by the retailers that have figured out how to maximize customer satisfaction and profitability with the right combination of quality products, friendly and efficient service, unique value, a differentiated shopping experience, and a business model that truly serves its community — locally and globally. How will this be accomplished? It starts with understanding the customer and then linking that insight into every decision that is made, from merchandising to marketing to distribution to store operations to finance, so that retailers can predict how to best serve their customers’ ever-changing needs and desires.

Our vision for the future of retail BI provides for that very scenario, through our intelligence platform and our solutions for customer, merchandise, operations, and performance intelligence that are combined in a suite designed to equip retailers to become truly innovative.

A solution seeking to use customer behavioral data to make better merchandising or marketing decisions needs to interface with sales transaction systems, loyalty systems, in-house credit systems, coupon redemption systems, catalog and Internet customer data systems, and so forth. A system that recommends optimized price changes should interface with the price management system, the item master, the system that generates labels, etc.