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Complexity of Using BI in Retail

Using business intelligence [BI] in a retail environment presents a complex multi-dimensional problem.

We can best understand this complexity by looking at product and sales management from different perspectives:


Product SKU's

Most business users analyse by dimensions such as time, cost centre and product or service.

In retail, there are thousands of SKUs across hundreds of outlets, which need to be measured against weekly forecasts.

Operating in such a fast paced and highly competitive industry, retail managers are increasingly demanding near real-time analysis. The volumes of data needed to support this capability can be massive, compared to a like sized business in another industry.

The multiplicity of data points scale in retail business intelligence into billions of data cells.

For instance, daily sales data for 1,000 SKUs in 250 stores can generate nearly 100 million data cells in a one year period. Calculating and storing analytical reports results in data explosion, which in turn can result in storage requirements increasing by a factor of 10, with nearly 1 billion potential data nodes.

Obviously, not every store sells every item every day - however the data problems cascade to needeing more processing power and more sophisticated data management techniques.

Fortunately, the advances in massively parallel processing and active data warehouse technologies are now making analysis of these vast amounts of data a practical proposition.


Fast-Moving Environment

Retail is a very fast-moving environment. Product fashion cycles have reduced from months to weeks, with many reatilers now operating on just-in-time stock levels as low as 4 weeks of supply. 10 years ago, 16 - 20 weeks stock cover was more the norm.

"Newness" is a key merchandising strategy requiring effective and targeted decision support for managing old stock out of the business. This can be the difference between making a profit or a loss.


Data Relevance

In an effort to rationalise data analysis capabilities, retailers are asking the question - what data should we store, at what level and for how long?

This challenge of data relevance is fast becoming one of the most significant issues in retail business intelligence.

The sheer volumes of data required by retail merchants and finance executives to report which regions and products are performing best and worst, and where the key actions need to be taken, requires a new generation of interactive and dynamic analytics where the software continually trawls data looking for exceptions.

This operationalised BI reduces the incomprehensible clutter of data into specific suggestions for actions to gain competitive advantage.



Changes are not only evident in the retail business model. There have also been significant developments in retail IT structures that support business intelligence.

Radio frequency identification [RFID] is transforming the accuracy of inventory data, and promises to remain one of the highest value developments in retail business intelligence over the next few years.



Supply chains are extending world-wide, with Asia now contributing for nearly 50% of global exports in the world’s clothing industry. UK supermarkets sell fresh produce from Thailand, Ecuador and New Zealand. These complex supply and cost structures must be closely monitored and managed.

Such remote stock sourcing also means that decisions about short life inventory investment must not only be finely tuned, they must be made sometimes months in advance.

The level of predictive confidence required for this decision making environment demands even more timely and accurate data, and it must be presented in meaningful ways.



In summary, retailers are becoming ever more conscious of controllable costs. Selling price deflation means constant pressure on margins, with cost control often being the only way to maintain profitability.

This requires constant, effective analysis and alerting to any exceptions to profitability rules.

Whilst collating and presenting relevant data may be time-consuming and complex, the potential for bottom-line saving is significant.

With web based tools supporting collaborative demand planning, local intelligence can be incorporated into centralised purchasing.

In addition, key performance indicators can drive supply chain and stock management processes, with alert messaging ensuring action is taken at the earliest possible moment to retain profit.

Retail business intelligence has evolved from reporting, to planning and tactical action to business performance management [BPM], and now operational performance management known as Operational BI.

At this micro level, predictive analytics is continuously applied to a body of data and manipulates it using sophisticated algorithms to provide dynamic suggestions for tactical action.

This can be used for optimising:

  • Profit
  • Merchandising
  • Supply chain

Vendors using OBI typically report increased sales volumes, margin percentage and stock turn – all at the same time. The bottom-line impact of combining even small improvements in these key performance indicators [KPIs] is potentially immense.

In spite of such compelling potential, it is important to remember that technology alone cannot assure the success of a retail business. However, Business intelligence is a tool, which when coupled with sound business strategy and effective execution processes can propel a retail chain to higher levels of performance and competitive edge than ever before.

Next: Barriers to Retail BI

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