Business Intelligence tools and Business Value addition

Before advent of Business Intelligence (BI) tools such as Cognos, and Business Objects , the regular operational and analytical reports were developed using combination of tools. For example, getting summarized data using Oracle SQL and then producing reports in Microsoft Excel.  Many organizations are still pursuing this approach for variety of reasons.

The Business Intelligence (BI) tools have helped some of the organizations by

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    • Shifting focus from developing the reports to analyzing the reports
    • Improving efficiency of report development process
    • Providing flexibility in slicing and dicing the data

Some of the challenges associated with the Business Intelligence (BI) tool adoption approach for Management & Business reporting are:

    • Lift and Shift approach: Typically, the reporting end users are from the business and they decide the layouts and formats of the reports. Since, they already had reports available, they demanded the almost same report be generated, leading to complicated data model and longer development cycle. If an existing report was coming of Base SAS, multiple aggregation and steps would have used to develop the final report. When the same report is required to come off a BI tool, the complications were manifold. The reason, BI tool is meant to generate all the reports with limited data duplication and standard data model.
    • Known and Static Requirements: I am a critic of the Business Intelligence tools. The reason, an underlying assumption with BI tool is that the reporting requirements are static and known. This is not an appropriate or valid assumption in most of the scenarios. For example, due to customer migrating to direct channels quite a few reports may not be relevant. Also, there will be additional reporting requirements which may not be directly available out of the BI tools. And the typical BI tool deployment cycle– requirement understanding to go live- is around a year.  So, the business has its own concerns on initiating one more development cycle to incorporate the new or changes in the requirements. The outcome, BI tool and the reports remains unused.
    • New Trends remain buried in obsolete groups: Typically, the variable groups are defined once at the time of BI tool deployment and remain static. The changes in customer behavior may be buried under the groups defined. For example, initial age bucket was 25-35 years, and due to economic and technological changes customers aged between 25-30 years may behave differently.  But, the new trend and insight remained buried in the group due to averaging or aggregation.

Real time offer management in Retail scenario using Big Data

Retail customers walk & search across store aisles and pick up products. The list of products in the customers’ basket depends on customers’ actual and perceived need. The perceived need could be generated and altered by the promotions and offers.

I am sure it would have happened to you. When you went a supermarket to buy couple of products, but ended up buying a lot of additional products. Whether you realize or not the additional products selections are driven by the promotions in the store.

In another scenario, a supermarket advertised discounts on a list of items if purchased together. For example, if a customer buys 5Kg floor, 5Litre Oil and 5Kg Sugar then overall cost 20% less. The customer may not require buying sugar but s/he is buying due to the offer.

The product combinations in an advertisement and the in-store promotions could be developed based on the customers’ transaction patterns and product basket analysis.  But those may not necessarily align to individual customer needs.

Shopping CartA few and probably quite a few customers visit different aisles and SKUs, looks at the products but do not put in the basket.  Hypothesis is that if a customer visit an aisle and spend time looking at the product SKUs, the customer have the need. The need is expected to be stronger than the perceived need or the identified by statistical models.

Critical and key points are tracking customer movement across store aisles and identifying the product SKUs which the customer have looked at and the amount of time spend by the customers.

When the customer completes its basket and reaches the counter, based on the above information, the customer’s basket and historical spending pattern, a real time and personalized offer could be solicited to the customer. The offer so made will be far more effective and personalized to the customers.

The processing of the above the information in real time requires sophisticated algorithms and analysis capability. The big data platform and big data analytics could be looked at as a potential solution.