I often tell folks that one of the benefits of decision management is that it enables analytic decision making – that is decisions based on accurate analysis of data about what works and what does not – even by people who don’t have any analytic skill. For instance, using analytics to assess the credit risk of a customer allows a call center representative (even one who started today and has zero experience) to quote a loan rate accurately. The operational decision “what is the right loan rate for this customer for this size of loan today” is made using a combination of rules and analytics and the answer is passed on to the call center rep.
He or she needs to know nothing about risk assessment, nothing about trends or forecasting. No math skills or statistical awareness are required. And this is important because most people don’t have these skills! Presenting them with data and expecting them to accurately use it is just not reasonable (which is why just giving call center people query tools and dashboards is not a solution).
So why, I hear you ask, is James talking about this today?
Well I got an offer from my local city utilities company (Palo Alto has its own utility …
I often tell folks that one of the benefits of decision management is that it enables analytic decision making – that is decisions based on accurate analysis of data about what works and what does not – even by people who don’t have any analytic skill. For instance, using analytics to assess the credit risk of a customer allows a call center representative (even one who started today and has zero experience) to quote a loan rate accurately. The operational decision “what is the right loan rate for this customer for this size of loan today” is made using a combination of rules and analytics and the answer is passed on to the call center rep.
He or she needs to know nothing about risk assessment, nothing about trends or forecasting. No math skills or statistical awareness are required. And this is important because most people don’t have these skills! Presenting them with data and expecting them to accurately use it is just not reasonable (which is why just giving call center people query tools and dashboards is not a solution).
So why, I hear you ask, is James talking about this today?
Well I got an offer from my local city utilities company (Palo Alto has its own utility company) today. They wanted me to try some LED light bulbs and had an incentive offer – I can get 2 bulbs for just $8 instead of the usual $75 as a way to see how much better LED bulbs are. The key benefits were listed and the first one went like this:
LONG-LASTING LED bulbs can last up to 35,000 hours. That is approximately 97% longer than an incandescent and 77% longer than a CFL bulb.
Hmmm. So these bulbs usually cost nearly $40 and only last about twice as long as an incandescent? That’s what 97% longer means after all – the life of an incandescent bulb plus 97%. Twice as long for 30 or 40x the price? No way.
But what, in fact, is the lifetime of an incandescent? Well the ones in my cupboard say 1,200 hours. Wait, that’s just 3% of the lifetime of one of these LED bulbs. And the flipside of a lifetime of 3% is not 97% longer but 3,300% longer! Now this begins to look better. And, of course, the math works better for the CFLs too – mine say 8,000 hours or about 27% of the LED ones making the LED ones about 400% better.
This was a glossy, expensive mailer sent to about 25,000 households. Presumably it was checked multiple times and still a basic math mistake was made that grossly undervalued the offer – by more than 30x. Imagine how many out of 10,000 call center representatives will make similar mistakes if you rely on their math skills…
Please, embed the analytics, don’t rely on your staff’s ability to do math.
