When all you have is a hammer, everything looks like a nailOnly thinking about process can be dangerous- The systems you are integrating are pretty dumb and hard to change- Many processes rely on people to provide intelligence The rules that help control a process are not the same as decision rules The rate of change of decisions is often different, and higher, than that of processes Decisions can be shared across processes and systems and so require different management
Process management and decision management are different – they have different intentions, different outcomes, different techniques and different technology
Just to bring this home, here’s an example of an underwriting process to show where the decisions might be.
As we are talking about decisions it is worth remembering that all decisions matter, as Peter Drucker noted. Not just the big, strategic decisions of your executives but the day to day decisions that drive your business.After all, people react to your organization’s decisions, especially decisions made by front-line systems and staff, as though they are both personal and deliberateYou should make sure they are
First, decisions. When we wrote the book this was an area where we wrote a lot more than we had expected – when you think about decisions all the time you get used to identifying them and focusing on them but it is challenging initially.We are not talking about strategic decisions here – should we do business in Alaska, say – but on the decisions that relate to specific individuals, customers, members. These customer treatment decisions can and should be identified, externalized and managed.There are a number of different ways to find these decisions and the four most common are:Micro decisionsOrganizations do not realize how many they makeEvery strategic decision has many operational consequencesEach customer interaction can be many decisionsManual decisionsHidden under manual processesDecisions that are being taken every day by front-line staffConflicting decisionsDifferent parts of your organization treat customers differentlyA longer time horizon might drive a different decisionDecisions are made inconsistentlyMissing or default decisionsDecisions that you do not think you can take and so you do the same thing every timeThe policy was set a long time ago and was never updatedFred has a great example of this with the AmEx website which you will see later
Here’s an example from a retailer to illustrate my point. They wanted to get members who bought only within a single category to start buying in multiple categories.They created a number of test groups and then used their traditional approach to target some in each group. These mailings were designed in the traditional way – find a segment, figure out a message and a compelling cross-sell, send a letter. They got a pretty typical result – just under 1%In parallel they conducted a separate campaign designed by focusing in on the operational decision of what to send THIS customer. The cross-sell offer was targeted to the micro-segment that a particular customer was in, the messaging was targeted using rules, prices and location information was selected for that customer etc. The results for this personalized campaign are better This represents a 2000% improvement in response rates. The customers in the personalized campaigns also had larger baskets too so the results were better by even more than 2000%By drilling down from their strategic objective – increasing basket size by expanding the range of products purchases – to the specific operational decisions that impact customers they got tremendous results.
In this case a manufacturer wanted to provide an automated diagnostic system for its products.All business rules are entered and managed directly by engineers who understand the symptoms, problems, and products. No IT resources are required to make updates, so changes and improvements are made quickly and inexpensively.Five months to build rather than 30 monthsROI 10 to 20x hard-coding rules
Analytics simplify data to amplify its valuePredictive Analytics turn uncertainty into usable probability
The challenge often faced is that the analytic insight must be delivered to systems and manual processes, people with policy and procedural manuals, before it can be truly put to work. All too often the process for doing that is broken, because it passes through PowerPoint or Word or something similar or simply painfully slow. I remember talking to an online travel site’s IT team once and asking them about modeling. Oh yes, they said, our business users paid for a response model. When we asked to see it they showed us a PowerPoint presentation. That’s nice we said, where’s the model? They said “It’s described in the PowerPoint”. That meant the IT department had to find where to apply it, and there were several spots, figure out how to code the model and link it to the data actually available in the operational environment and then re-test and confirm everything. Months later the model was in production. Somehow I doubt the business got the return it was expecting on that modeling investment….To put analytics to work we need a new approach.
In this example an insurance company implemented a risk-based underwriting decision service for use across its systems.In the first year an eight-point reduction in combined ratio Resources are applied more effectively, because manual review of clearly unacceptable and clearly acceptable risks eliminatedUnderwriters focus more on book management and agent performance.People in the underwriting department manage the rules and don’t compete for IT resources.Risk management is more effective and pricing is more accurate, because the number of pricing tiers increased four to eight times.
One of the challenges with decisions is that they take time to play out – the results may not become obvious for some time. So, if we take a population and take a decision about how to treat them we will only see the results that generates if we wait for some period of time – perhaps until they are next due for a check up or a renewal, perhaps sooner. By the time the results are clear, however, it is way too late to gather any new data about what might have worked better.To address this we do not treat everyone exactly the same. We take our main approach – called the champion – and apply it to most of our population. We also create a number of challengers – alternatives – and apply them to a small percentage of the population. Now as time passes we are also collecting information about how these alternatives would have worked. If one works better, we can make that the new champion and then apply it to all subsequent decisions.Better yet, we can make it the champion and develop new challengers and repeat the process. Ensuring that we always have challengers running allows us to constantly verify our approach and continuously improve it.
It is particularly easy to see how failing to identify explicit decisions over complicates a business processThis example is from IDS-Scheer and shows how using the usual branching and related structures of a process design can become terribly complex. Not only is this hard to read and use but any change to the decision making logic impacts the process design and adds complexityIn contrast the same process with the complexity of the decision encapsulated in a decision service is simpler, easy to manage and can now be changed independently of the decision logic.
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