Data rich, information poor: Revenue Analytics
Forensic Focus - December 2016
Today, organisations are ever becoming ‘data rich’, generating gigabytes upon gigabytes of data annually. However this doesn’t always equate to businesses being ‘information rich’.
To shift from ‘information poor’ to ‘information rich’, organisations cannot just scrape through reports row by row to find the needle in the haystack. Instead we are finding organisations more commonly employing the use of modern tools, techniques and people who can immediately highlight the insights to lower costs.
In our article Data rich, information poor: fuel card analytics, we highlighted how even seemingly dull datasets (fuel card data) can provide rich insight.
Most organisations have revenue data. And the traditional approach to looking at revenue data is to generate reports on the sales and profit margins. These reports are often static and do not provide enough detail to allow users to find answers to important questions like “How can we improve our margins?”, “Who are our key customers?” or “What are our key products?”
A client approached us recently to help them on their journey to becoming ‘information rich’ by running analytics over their revenue data, focusing on trends and outliers.
The top graph in diagram 1 shows how much sales and margins each customer is making. (Each customer is represented by a circle, with the colour of the circle signifying the location of the customer). The bottom graph in diagram 1 shows the sales and margins for each order for all customers.
What stands out immediately is that there are three customers that each account for around $40 million in sales each year, but with significantly varying margins. Here is where the power of modern analytics tools shine - we are able to quickly and easily drill down into these customers by simply clicking on them within the graphs to see what is causing their margins to vary.
By clicking on the customer with the lowest margins (Customer 600402) shown in diagram 2, we can see that the graph below changes to reflect the orders that this customer made. Looking at this data more closely, we can see that the majority of their orders produced positive margins except for the four orders on the left hand side. This insight provides an excellent basis to determine the cause for this high revenue customer generating low (negative) margins, ensuring this is not repeated in future.
Depending on the raw data available, we are also able to map the data by salesperson and location (including geospatially) in order to slice and dice the above data in different ways. While it is possible to replicate this sort of analysis using a traditional spreadsheet approach, the level of insight and speed possible using analytical tools is vastly higher.
For more information on how to transform data into information or for more information about revenue analytics, please do not hesitate to contact either contact Jason Weir or Lisa Tai.