Labels

Business Intelligence in Retail

                                                Business Intelligence in Retail

Business Intelligence (BI) is the use to computing technologies that transform raw data into meaningful and useful information for business purposes. BI can handle large amounts of information to help identify and develop new opportunities. Making use of new opportunities and implementing an effective strategy can provide a competitive market advantage and long-term stability. BI technologies provide current, historical and predictive views of internally structured data for products and departments by establishing more effective decision-making and strategic operational insights through functions like online analytical processing (OLAP), reporting, predictive analytics, data/text mining, bench-marking and Business Performance Management (BPM). These technologies and functions are often referred to as information management.

History of BI
Mr. Richard Miller Devens in his book Cyclopedia of Commercial and Business Anecdotes, used the term "Business Intelligence". He used it to describe Sir Henry Furnese, a successful banker who actively gathered the information and used it to act before his competition. This gave rise to an idea that data and past evidence, rather than gut instinct should be used for developing business strategy. Hans Peter Luhn in 1958 wrote an article titled "A Business Intelligence System", which described BI as an automatic system developed to disseminate information to the various sections of any industrial, scientific, or government organization. The article also predicted many BI trends that are present in todays time. Luhn is recognized as the father of BI. In 70s and 80s, computers entered the business world. There was finally an alternative to storing data on paper. Businesses started using data analysis to determine key performance indicators with the aid of Decision Support Systems (DSS). But the machines were bulky, complicated to use and very costly for small businesses to afford. In the 90s, internet exploded in to scene and personal computers became affordable. Using computers also was easier with the aid of operating systems like Windows 95, with that PC sales shot up exponentially and so did the number of internet users. These events lead to developments of more user-friendly data analysis software as vendors saw opportunities to cater the need of even small business owners. The 2000s saw the intribution of Cloud BI, which enabled even smaller businesses to use BI as no expensive setups were required.  Frameworks like Hadoop allowed large amounts od data to be processed in real-time. By 2005, the increasing inter-connectivity of the business world meant that companies needed real-time information, for a host of reasons. Chiefly they needed to keep abreast of the competition, and understand what their consumers wanted and what they thought of their company.BI was no longer an added utility, or a mere advantage. It was becoming a requirement for businesses looking to stay competitive, and even to remain afloat, in an entirely new, data-driven environment.

Why do we need BI in Retail?
In the fast-paced life of the twenty-first century, consumer demands and needs have skyrocketed. What really are these consumers looking for? The answer is simple – Instant gratification, impeccable services and the latest products to keep pace with the ever-changing trends. Data Intelligence is required to understand these needs of the customer for a better buying and selling experience. If analysed properly, such analysis can take you as far as predicting what the customer requires next. Companies who can identify their potential customers and broadly understand their requirements are already far ahead of their competitors.
Understanding client purchasing examples is vital to conveying an upgraded retailing background. With the moving time, retailers are starting to understand that to upgrade execution and income; they should comprehend where their clients are originating from. Few known examples of retail analytics solutions is m2r.
Adopting Retail mobility solution which provides retail analytics is a powerful way to work towards your retail goals. With the worldwide Business Intelligence and Analytics market reaching $16.9 billion in 2016, it goes without saying what business intelligence is capable of. Leveraging the features of business intelligence and data management services paired with the expertise of analytics and data integration, a Retail Business Intelligence Solution may be just the solution that you need.
Data intelligence comprises of organizing and analyzing data for future endeavors and to expand the business. The internal and external data is gathered and carefully studied to find out the loopholes and make more informed decisions in the future. Data intelligence includes activities like Business performance, data mining, online analytics, and event processing. Business intelligence and data intelligence is about transforming data into information, information into knowledge, and knowledge into value
The main strength of a company lies with its people and its data. Business intelligence urges the companies to empower their people using the data at hand. Improvising on its data handling mechanism is the most significant internal revolution that a company can make. Knowledge is power; proper data analysis will empower the companies to make better schemes and sell their products smartly. By exploiting this technology to the maximum, perhaps we can turn the vision of drones zooming through the skies to deliver packages that haven't even been ordered yet, into a reality.
Walmart, the world's largest retailer, uses BI is various sectors across their business. It helps make their pharmacies efficient by helping to understand number of prescriptions per day, determining peak hours in day, week and month and scheduling manpower accordingly. BI also helps anticipate store demand by suing predictive analysis. They use BI is their Supply Chain Management extensively to find out best routes and tracking number to steps from dock to store. BI also helps is analyzing customer preferences and patterns that help is formulating strategies for shelving and merchandising. They provide personalized shopping experience through mobile deals according to their preferences.
Amazon also uses BI to recommend personalized shopping with the help of Comprehensive collaborative filtering engine (CFE), which helps users choose similar items from purchase history. Recommends books in Kindle through reading patterns.
Advantages of business intelligence in the retail industry
1.Helps in making better decision for the firm by analyzing the past data available: As BI helps in making the better decision for the firm by going through the history available. based on the data / information it helps in getting best of decision which will help the company to make decision favorable and feasible for the company growth and sustainability in the market for the longer growth.
2. Personalization and customer centric approach: The traditional in-store retail marketing paradigm inherently paints with broad strokes. Promotions and signage may be targeted to a given demographic or target market but drilling down to the individual level is near-impossible using traditional marketing tools. these subjective efforts aren't nearly as effective as data-driven approaches made possible by BI.
3. Reduced operational expenses and enhanced operational efficiencies: BI makes data-driven, efficient operations achievable in ways never possible. From a staffing standpoint, BI can help you understand how to staff your retail location to match the ebb and flow of traffic in the store ensuring you have enough staff to keep customers happy, but aren't over-staffing and being wasteful due to inaccurate, subjective predictions.
4. Optimize floor plans and product placement: One of the biggest drivers of sales in retail is creating a floor plan that is conducive to sales. Floor space is limited and maximizing revenue per square foot is vital to turning a profit. BI takes the guesswork out of getting your floor plan right. Using insights from BI, store managers can not only tell where customers are spending most of their time in the store (by tracking "dwell time") but also identify slow-moving product and displays that are not attracting much customer attention. This allows managers to not only create displays and sales to help push slow moving product, but also to plan a floor layout to better drive traffic to the right places
5. Enhanced customer experience: The fundamental advantage brick and mortar retail has over online shopping is person- to- person interaction. BI helps retailers lean into that and make sure that the look and feel of the store, as well as the interactions with retail staff, are strategically designed to enhance the customer experience.
Conclusion
Brick and mortar retail isn't going anywhere anytime soon. It's evolving and beginning to leverage the tools and techniques e-commerce has benefited from in recent years. By leveraging BI, organizations can make their brick and mortar retail experience highly personalized, data-driven, customer friendly, and engaging while also improving efficiency in operations. As BI continues to mature in the retail space, we will see continued growth and adaptation in the market, likely bringing the level of intelligence in brick and mortar retail on par with or exceeding that found online. In the years to come, wearables for sales staff may enable them to react to and engage with customers based on data inputs from nearby beacons and sensors. Whatever the future may hold, retailers that embrace a data-driven, BI powered approach to retail are well positioned to compete moving forward.

Disadvantages of business intelligence in the retail industry:
1.Piling of Historical Data: The major objective of Business intelligence system is to stockpile past data about a firm's deals and reveal it in such a way that it permits professionals in decision making. On the flip side, this information generally amounts to a small portion of what the firms require functioning, besides its restrained worth. While in other situations, the user may not have interest in historical data as many markets that the company regulates are in frequent alteration.
2.Cost: Business intelligence at times can be a little too much for small as well as for medium sized enterprises. The use of such system can be expensive for basic business transactions.
3. Complexity: Another disadvantage of BI could be its complexity in implementation of data. It can be so intricate that it can make business techniques rigid to deal with. In the view of such premise, many business experts have predicted that these intricacies can ultimately throttle any business.
4. Limited use: Like all improved technologies, business intelligence was first established keeping in consideration the buying competence of affluent firms. Even today BI system cannot be afforded by most of the companies. Although, traders in the past few years have started modifying their services towards medium and small sized industries, but the fact is that many of such firms does not consider them to be highly essential, for its complexity.
5. Time Consuming Implementation: Many firms in today's fast paced industrial scenario are not patient enough to wait for the execution of Business intelligence in their organization. It takes around 18 months for data warehousing system to completely implement the system.
Conclusion
Hence, it becomes vital for the firms to give due thought to the business intelligence aspect. Due to the intricacy of these systems, the BI system can create an existence of their own in the firm. It must be understood by the firm that storing data in the business intelligence system just for the sake of it does not increase its worth but results in vice versa effect.
 Author:

Chetan Das



No comments:
Write comments

Please do not enter spam links

Services

More Services