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Business Intelligence & Data Mining Managëment - The fresh Management World
S. Maurer
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 create sense of Customer, product {896}, supplier, and operational data and draw insights that will assist them run their businesses bigger and more profitably.
BI will be defined by the retailers that have figured outside how to maximize Customer satisfaction and profitability with the fair combination of quality products, friendly and efficient service, unique value, a differentiated shopping experience, and a business design that truly serves its community - locally and globally.
We find that the retailers that are realizing the most significant returns on their investments are those that catch a purposeful, pragmatic approach to establishing an intelligence platform upon which to base all other BI solutions.
Operational and transactional systems such as merchandise Management {368}, ERP¹ [enterprise resource planning], and POS, are very great 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 - at the end week, at the end month, and last year.
BI business processes Organizations typically gather data in order to assess the business environment, and cover fields such as marketing research, manufacturer or market research {643}, and competitor analysis.
Prior to the set off of the Data Age in the late 20th century, businesses sometimes struggled to collect data from non-automated sources. Businesses then lacked the computing resources to properly analyze the data, and often made business decisions primarily on the basis of intuition.
Analysis of past performance, combined with plans and forecasts of prospect Customer behavior, leads to more accurate initial allocations of merchandise across channels and stores.
As retailers gain a bigger understanding of their customers' buying behavior, this analysis can then be used to create more effective merchandising plans for the consequent season.
Accurate analysis also results in a more efficient apply of manpower in picking, packing {936}, 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.
Improved Extract {786}, transform, load [ETL] and still recently Enterprise Application Integration tools have increased the speedy collecting of data. OLAP reporting technologies have allowed faster generation of fresh reports which analyze the data.
A system that recommends optimized fee changes should interface with the fee Management system, the item master, the system that generates labels, etc.
Data volume [capacity]; How lenghty data will be stored [data retention]; Benchmark and performance targets
BI can also improve Customer experience, allowing for the timely and appropriate response to Customer problems and priorities.
Retailers are immediately paying significant attention to BI software, specifically in the areas of merchandise intelligence [including merchandise planning {457}, assortment, size, space, price, promotion, and markdown optimization], Customer intelligence [including marketing automation {436}, marketing optimization, and market basket analysis], operational intelligence [including IT¹ portfolio Management], labor optimization, [and certain estate site selection], competitive intelligence.
A single, reliable demand forecast {937}, for instance, can also be used in mercplamising, marketing, logistics, store operations, call center staffing, etc. {363}, for operational benefit.
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