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Business Intelligence=Smarter Decisions for Companies

Forrester Research describes Business Intelligence as a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information to enable more effective strategic, tactical, and operational insights and decision-making. Business Intelligence aims to support better decision making in an organization. And as we know smarter decisions result in better results.

With Business Intelligence, Organizations on real-time can monitor “how are we doing”, “why we are performing in that way” and how should they be doing.

For this post, I describe a case study that gives an example of how to draw insights.

Let's assume company ABC launched a Product A in 2005 and has seen significant growth since launch. The situation here is that ABC is trying to understand the impact of a competitor (Company XYZ) that launched another Product B in same market in Q3’08 and Product A will start losing share to product B. As a first step, we plotted raw data to get some understanding of product A performance in recent years. In chart 1, I have plotted the raw data and to smooth volatility of raw data, I have plotted the 4 week moving average as well.

Some of the findings from this exhibit are as follows:

  • Over last two years, product A has seen steady growth
  • Product A is a seasonal product and sales remain relatively flat till April followed by a steep increase in May in 2008 and 2009. This is consistent with expected use of the product because most consumers use this product in summer and fall
  • For 2010, there was no increase observed in May which is a concern for Company ABC

Since the data is on a weekly basis, I looked at it in a different way. We plotted each year as a separate series for a 52 week period to further investigate findings from above.

On a year-on-year (YoY) basis, 2009 was 23% higher relative to 2008. Year-to-Date (YTD) 2010 is 9% higher relative to the same period in 2009. Essentially there is a wide separation between 2009 and 2008 lines. Till April’10, there was separation between 2009 and 2010 lines but since May’10, 2010 weekly sales are tracking at the 2009 weekly sales.

Based on above analyses for Product A, it was inferred that sales since May’10 have been flat and we have not seen a summer spike in 2010. ABC received similar weekly sales data for competitive Product B from a data vendor and the next step in the process was to analyze the recent trends in Product B.

In similar format to Chart1, Chart 3 below shows the weekly sales for product B since launch in Sep’08.

From Chart 3 above, sales for Product B demonstrate steady increase since launch in Sep’08. Like product A, there was no sharp increase in May’09 and this aligns with competitive intelligence received last year that Company XYZ didn’t have all the promotional material ready by summer 2009. However since May'10, there was a significant increase in sales which is the same time period when sales for Product A didn’t demonstrate seasonal spike similar to historical patterns.

The next step in the process was to compare sales trends between the two products. Plotting the sales trends in same chart would not have provided a meaningful comparison because scales for two products were different. Growth comparison is more relevant when comparing two products with different scales. In this case, we looked at the indexed growth i.e. we compared 4-wk moving average to respective product’s previous year avg. Chart 4 shows the growth comparison between Product A and Product B.

From the above chart, it can be inferred that growth rate for Product B increased significantly in May’10 and Product A remained flat. Based on further discussion, it was found that Competitor XYZ had launched a promotional campaign which increased awareness of Product B among customers. The analysis was further expanded to compare sales between products in different geographies and different customer segments. Based on this analysis, ABC designed a new marketing program to show benefits of Product A were superior to those of Product B. This analysis helped Company ABC understand competitive threat which they were able to successfully blunt with innovating marketing programs.

The above case study is an example of insights that can be drawn from vast data and how these insights then drive decision making. With so many Business Intelligence Platforms currently available, organizations can build performance monitor dashboards metrics on a real-time basis to make smarter business decisions.

Related posts:

  1. Useful Frameworks to Drive your Business Strategy – Part 2 of 2
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