Myths and Truths about Retail Business Intelligence Systems

The turbulent business environment has become complex and challenging for retailers. The equation of success is redefined constantly with multiple players, and many variables.

Businesses look upon IT infrastructure as great levellers to address this complexity, to solve issues of inefficiencies and to tap opportunities that will help profits soar. However, the promise of innovative solutions and quick answers to success has created a blind dependency on IT systems, especially Business Intelligence (BI) systems, which may not be best suited to a certain business. Therefore, decision-makers need to carefully weigh the pros and cons of each BI system before they buy them. In fact, after implementing a BI system, businesses have discovered that it often does not produce the right results for them, and then to remedy the specifications often costs them more than the original price of the solution itself.

The retail industry, which functions very differently from other industries, is no exception. Interestingly, within the retail industry itself, there are several different flavors of business, all of which may be similar in operation at the surface but very different as one scratches beyond the surface. For instance, retailing of apparel and grocery are very similar in their operations where numbers change constantly, sometimes every single second. However, a closer look reveals that there exist many differences like the perishable nature of groceries and the varying seasonal demand of apparels.

Therefore, it is very essential for the IT department in a company to understand the nuances of business and apply relevant parameters which result in analytics that provide game-changing information. Here are five myths that organizations have about BI systems.

BI Systems need not be retail – centric

Most analytic products claim to address issues that businesses usually face. Organizations are led to believe that the best solution is to purchase existing (but generic) ERP, CRM or BI analytics products available in the market and integrate them in their systems. This approach has a merit if the business is looked at from a surface level. Probe deeper and you will find that the demands of no two retail businesses are the same.

Take for instance the size of the network in a retail supply chain. There are multiple warehouses and many retail locations. The sheer number of items dealt within the retail stores is huge. An out-of-stock situation is a retailers nightmare. Therefore, warehouse planning is crucial to managing the flow of merchandise through optimal transportation.

Another example is of a typical grocery store in the US that, on an average, is spread across 47,500 sq. ft and sells 45,000 different items, according to the Food & Marketing Institute. Add to this the large number of vendor shipping locations to the network and you get an unwieldy and complex scenario that retailers find difficult to manage even through a generic BI analytics.

Insights from retailers

Retail-centric Intelligence can change the game around

A leading specialty retailer of Gaming products operating more than 1500 stores in Europe, implemented a generic BI platform for its management decision support.

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Competing in a dynamic market where products have a short lifecycle, where customers are always looking for new products, with seasonal events playing a disrupting role in demand predictability, the retailers needed to act quickly and decisively to capitalize on marketing opportunities. But because the BI platform was not retail-specific, the retailer was faced with the problem of delayed information which was often not actionable.

This lead the retailer to opt for a Retail Business Intelligence application that understood the business, and provided over 500 pre-built KPIs and several analytic applications across merchandising, operations, inventory, suppliers, promotions, and almost all major functions in retail merchandising.

Without having to depend on a large and complex analyst and technology workforce, the Retail Business Intelligence solution proved to be the game-changing advantage for the retailer.

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Industry generic BI helps to meet the demands of time

Challenges change. New variables come into play and some old ones become irrelevant. Conventional BI systems are not built to evolve with time. They operate on old metrics, resulting in irrelevant data for retailers.

Industry generic BI platforms cannot tackle scalability. It is true that new add-on applications can be built and integrated, but this may result in a weak and crumbling architecture churning out data that may not be appropriate for the business. Instead, it would be useful to have an architecture that can anticipate market conditions and not attempt to integrate these later.

The underlying data architecture, responsible for storing data and operating analytics displayed in business intelligence reports, should not be a black box created for a specific purpose and left alone. Proper data structures and architecture allow for different types of data and transactions to be used as needed. New and existing data sources should complement one another, allowing detailed analysis in each targeted subject area. With each targeted effort, you want to further fulfil the enterprise vision.

Insights from retailers

You need to grow with change

Within a few months after implementing a generic BI platform for the merchandising team, a leading discount retailer operating 400 stores across Germany realised that the generic BI platform required frequent modifications and developments to answer the constantly evolving business scenarios.

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Operating in a highly competitive market, the merchandising team had to closely monitor the market, plan innovative promotions and pricing strategies to drive customer traffic and sales. And every time they needed a new piece of information, the system administrators and software developers took a long time to provide the answers.

The BI system began to weaken in many areas and showed incorrect numbers that did not match their ERP data. The system crashed frequently when they needed to investigate sales at an item level, and weekly reports were often delayed by more than 24 hours.

The retailer decided to replace the generic BI platform with a retail business intelligence application. Even after having used it for more than three years now, the Retail BI system continues to handle data volumes, which grew from 250GB to 2TB. The solution continues to deliver intelligence in a few seconds and support every new requirement instantly without any development overheads.

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The ´Rear View´ approach helps managers to plan ahead

A Rear View of how your business fared is not enough to compete in today’s dynamic market. A retailer may realize the pitfalls, but that will certainly not help him in a market, where the change is sometimes fairly dramatic every single minute. What happens with most analytics and BI systems available today is that by the time you get information there is no time to react. It is not enough to provide voluminous access to information and expect good decisions. The need is to have access to predictive and actionable data that is logically presented, and to look for new, not missed, opportunities.

The reality of the market is demonstrated by dramatic shifts in consumer behavior, ranging from withholding spending to bargain hunting. This has resulted in retailers looking for ways to justify marketing investments, which can optimize revenue. In this context, retailers feel the need for innovative solutions such as Predictive Analytics to help deal with the volatile changes in the business environment.

Predictive Analytics in retail can deliver a clear profile of ideal customers, ways to tap them, predict new opportunities, anticipate needs, and proactively engage customers and prospects. This dynamic data and analysis helps retailers to make informed decisions that give them the critical speed and agility they need.

Insights from retailers

Pace ahead: But trace the path now

While operating in a network of 6 million members and over 1000 partners, an Asian retail loyalty management organization needed a system that not only analyzes their historical sales and customer data, but also predicts customer response to marketing programs.

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Rather than building the analytic capabilities they needed, this marketing organization selected a best-of-breed Retail Business Intelligence solution that contained pre-built applications, advanced predictive analytics, and data mining for areas like customer segmentation, churn modelling, sales forecasting, market basket analysis and optimization models like RFM for targeted campaigning.

Having jumpstarted their BI strategy, the company was able to successfully focus on value adding activities like analysing profitable segments, running high ROI campaigns, capitalizing on cross-sell and up-sell opportunities, and constantly innovating their marketing offers, instead of getting bogged down with managing technology.

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Data is what helps in decision making

BI solutions provide data to aid informed decision making. But what if the data that is collected is inefficient and wrong? Recently, there has been a huge concern on the quality of data that BI systems deliver, says the Data Quality in Business Intelligence Survey; 50% of respondents agreed. The faulty data led to ineffective decisions, and defeated the purpose of BI.

The Data Warehousing Institute estimates that data quality problems have cost U.S. businesses more than $600 billion a year. And they were not just talking of the unnecessary printing, postage, and staffing costs associated with bad data. When organizations have no grip on the quality of their data, the confidence of their customers and partner communities erodes. If for instance, the data records cost $ 1 per unit, you can save $50 by capturing erroneous data directly at the source before it reaches your customer. This obviously puts pressure on systems which generate clean data.

Inefficient data is not the only issue. A bigger problem is that BI becomes a storehouse of data, instead of it being actionable analytics that focuses on finding facts and giving insightful analysis. Today’s retailers need more than just data. They need a business intelligence system with built-in analytical systems along with information-gathering tools.

Insights from retailers

Grasp the information right at one glance

With multiple ERPs and critical data scattered across several systems, decision-making was a huge challenge for a leading Asian retail conglomerate.

The retailer operating in eight different retail formats under multiple store brands was unable to generate a single, consistent view of information across all their businesses for management and financial reporting. Sales and stock figures varied across ERPs, Financial Accounting, and BI systems. Management found it hard to trust the data or make decisions based on what the BI systems reported.

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The retailer realized that the problem rested in the way their business intelligence and data warehouse was built and managed giving rise to several data quality and integration issues.

The retailer implemented a Retail Business Intelligence solution, which understood the nuances of data integration in retail environment, and also provided a retail data model that ensured that the data remains clean, accurate and ready for analysis and reporting. The retailer now gets a single view of corporate performance across several businesses, even as the systems and the data are frequently changing to match the needs of the business.

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Technology helps make better decisions

An astounding 80% of an average company’s employees have zero access to BI systems because end-users find BI systems too hard to use. Crucial decision-making information rests in the hands of a privileged few who have either built the system or are responsible for using the system and delivering the information to decision-makers. Many of the analytic tools available in the market today have failed to address the issue of information democratization in a meaningful way.

People, not technology, are the decision-makers in a business. Therefore, BI technology should make it possible for people (the actual end-users at all levels) to acquire role-specific data upon which business decisions can be based.

What businesses need is an application that anyone can use to fulfil their job requirements. Which means the system must be built taking into account different requirements of a typical retail organization. The applications need to anticipate the specific types of data and insights each user needs and package them in a way that fits perfectly with the way the user performs his or her job.

What this also means is that technology has to be user friendly. According to a survey involving 350 organizations, by Gartner, 65% of those polled said they viewed business intelligence technology too complex and unusable.

Using information delivered from BI systems is not the job of IT. BI systems have to be user-friendly and accessible to people who will help make informed decisions and thereby propel profit.

Insights from retailers

Decisions in your hands

A leading US grocer with a turnover of over $1billion, had difficulty making timely, active and effective decisions across the organization.

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Their BI system was being managed by about 25 analysts and technology administrators. The reports had to be further manually processed before they could be delivered for the management reporting needs of about 20 decision makers, who, in turn, found it difficult to use the BI system.

Realizing the limitations of the existing BI environment, the CIO replaced the existing system with a Retail Business Intelligence solution. Retail BI, designed with familiar information, navigation and usability features, was not only easy to use, but gave the decision-makers the power to use and control the systems on their own. They created and published their own dashboards, scorecards and alerts.

Over a couple years, the usage of the Retail BI solution spread to many other functions and is now used by over 300 users. With a majority of the administrative staff moving to more value-adding jobs, the Retail BI systems is managed by a team of only two people.

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Business intelligence is evolving. Traditionally, business intelligence technologies have focused on methods, processes and techniques of analyzing data and presenting information. Today, the focus of business intelligence is to get closer to the business it serves. And that means addressing the needs of specific roles, processes and decisions of people in a business. The days of buying generic BI tookits, and struggling with implementation, usability, performance and scalability are over. The next generation of BI provides specific business applications and industry solutions. To profit from it, retailers have to take cognizance of these trends shaping today’s retail business intelligence technologies and redefine their expectations from BI.