Business and production systems have become much more capable at collecting data. Equipment collects a variety of sensor and parametric data, and today all kinds of information on buying habits and consumer preferences is available. This level of detail cannot be analyzed and comprehended with static, conventional reporting. Instead, business analysts, engineers and scientists can unlock insights and make discoveries with leverage provided by interactive, visual analytical software.
Analytical software has surfaced a new world of analytics that is characterized by these important traits:
An 'aha' moment
Consider this insurance example. Here demographic information from many thousands of current and potential clients was collected and maintained in a database. The insurance company was able to download the data into a spreadsheet and summarize the data but did they get the best exploitable insights? Answering even the simplest questions took days to acquire, splice and arrange the data.
Today, with integrated, interactive and visual analytics insights are revealed in seconds. The big question when it comes to prospective clients is how many of them were converted to new business and what are the factors that drive the conversion? By knowing this, focus can be brought to business practices that lead to higher rates of success.
We started by loading the data in JMP. With only a few clicks, tens of thousands prospective client encounters, including demographic information such as income, education, age, martial status, etc., were loaded. You can see from the image above that overall about 12.5% (the blue area) of these prospects were converted into paying customers.
Now to the question at hand: What factors determine success in winning new business? One more click (on the Split button in the lower-left) and an “aha” moment ensued.
The JMP chart above shows that a particular factor (which, due to confidentiality I can’t disclose so we’ll call it ... ), “factor Xn,” leads to an incredibly high conversion rate (about 90% as seen in the blue bar on the right) for a good number of prospects and that the remaining prospects had little chance of succeeding.
The analysts were stunned at seeing this. This insight had eluded them because the overall conversion rate was masking a major distinction, identified by factor Xn, among the prospects. Keep in mind that these analysts spend day-in and day-out poring over data, but this important insight and others that were to follow remained locked within.
This insight spawned a bunch of questions. First, it appears changing sales representative instructions were in order. Second, why was it that the conversion rate for other customers was so incredibly low? This led to questions about pricing, packaging and the like in combination with demographics that would be investigated with designed experiments.
Why it worked
Looking back at the six traits above, we can see that in this case:
Not only were the analysts impressed with the insight, but they were also excited about how readily it was derived.
Build your own culture of analytics
What does it take to bring the new world of analytics into your organization and support a culture of analytics?
This is where IT comes in -- obviously, they have a major role to play. IT no longer needs to worry about conducting analytics. It’s best left to the analysts. Instead, IT are now enablers of analytics. They can do this by:
Securing systems is a rapidly growing and increasingly demanding responsibility for IT -- so much so that we find that IT folks are usually very happy to be relieved of the burden of conducting analytics or involving themselves with analytics that analysts can better support themselves. Their enabling role is much more consistent with their other activities and responsibilities. For example, IT supports order/shipping/billing systems, but they do not order, ship or bill themselves -- so why should they conduct business, science or engineering analytics?
With the Internet of Things, new more capable equipment and the internet’s expanding reach, we can expect an exponential increase in the amount and quality of data well into the future. It’s best to prepare for the opportunities presented by building a culture of analytics now. That involves designing the right data architecture, providing JMP and enabling business analysts, scientists and engineers to advance their subject matter expertise with analytics.
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