What is Decision Yield
One of the challenges in adopting Enterprise Decision Management is measuring the ROI. Sometimes organizations find that a focus only on costs saved does not yield enough of a return to justify the investments required for EDM. By comparing organizations that have adopted EDM with those who have not it is possible to identify some clear differences in the way they make investment decisions. In particular there is a much greater focus on revenue improvement and on opportunity costs (those costs implicit in a delayed response to an opportunity). The challenge is how to turn this broader focus into something that can be used to justify EDM investments. Decision Yield is the approach that is recommended for this.
Decision Yield is a broad-based evaluation metric that reveals the quality of your current decisions and decision processes, and helps you plan, justify and measure improvements to these decision processes. It was first described by Frank Rohde in the Harvard Business Review. In it he says:
“We judge leaders by how well they make big, strategic decisions. But corporate success also depends on how well rank-and-file employees make thousands of small decisions. Do I give this customer a special price? How do I handle this customer’s complaint? Should I offer a seat upgrade to this customer? By themselves, such daily calls – increasingly made with the help of enterprise decision management technology - have little impact on business performance. Taken together they influence everything from profitability to reputation.”
What constitutes a good decision? Is it the outcome alone? The cost of executing that decision? The speed? How about the coordination of multiple decisions across different parts of your organization? In reality all of these aspects are likely to be important. As noted, many organizations lack a consistent method for measuring the performance of high-volume, operational decisions. The result is that plans for improvements that are vital to an organization’s growth are often made based on metrics that focus on only one dimension of the decision process, such as cost savings alone. Organizations with such a narrow focus often miss the potential value of an EDM approach.
To determine what constitutes a “good” decision process and to measure the current state of your decision process, you must understand the different facets of an operational decision that contribute to business performance. This holistic way of evaluating decisions is what is known as “Decision Yield”.
While Decision Yield is a fairly general-purpose tool, it is designed specifically to evaluate automated decisions that are typically:
- Customer-facing—from approving loans to pricing insurance to determining cross-sell offers.
- Very frequent—often many thousands of times a day.
- Executed in real-time (credit over-limit approval) or in batch mode (matching an offer with a prospect).
- Delivered through another system such as a website, call center system or letter shop. Often the same decision is delivered through many such systems.
In other words, the kinds of decisions for which an EDM approach is ideal. Decision Yield, then, can be an effective tool for those evaluating EDM and trying to decide where best to apply it.
Decision Yield’s holistic approach involves comparing five different dimensions of decision effectiveness, By considering all these aspects the Decision Yield approach allows you to make a comprehensive assessment of an operational decision. The five areas are:
- Precision or how optimal is the decision?
- Consistency or how consistent across divisions and channels and time is the decision?
- Agility or how quickly can you effectively change the decision when you need to?
- Speed or how quickly can you make the decision?
- Cost or how much does it cost you to take the decision?
Each of these aspects contributes to the overall effectiveness of a decision and the likely yield an organization will get from the decision – the Decision Yield. Let’s consider each of these aspects in turn in a little more detail.
Precision is a measure of the effectiveness of a decision. Different decisions will require different ways to assess precision but whatever is used should be focused on effectiveness, not efficiency, or on targeting. You may need to consider various financial outcomes such as profit, Customer Lifetime Value, revenue or losses as well as the accuracy of predictions, comprehensiveness of factors involved and the level of granularity achieved.
Consistency measures how well integrated and coordinated your decisions are across your enterprise. Do you make the same decision, the same way unless you mean not to? You can measure consistency over time – is today’s price the same as yesterday’s, across channels – is the offer on the website the same as the offer made by the call center, and within and across product lines – do I offer the same interest rate for different unsecured credit products. Highly consistent decisions need not be the same for all customers, all channels or over time but the variations should be deliberate and designed not incidental.
Agility is a measure of how quickly, cheaply and easily you can change the way you take a decision within your systems and organizational infrastructure. For example, if you want to introduce a new cross-sell strategy or a new pricing structure, how easy is it to change the systems specifications that support those decision strategies? How quickly would someone interacting with your organization notice that you had changed the way you wanted to make a decision? Agility should measure the total time and cost from having the data that means you should change your decision process to actually effecting such a change.
Speed is one of the simpler measures – it simply tracks how quickly you can execute a decision. This might be a measure of response time in an interactive system and elapsed time for a batch run of some kind. Speed should include any delays caused by waiting for additional data such as third party reports and should be measured across the distribution curve for a given decision – what is the mean, the median etc.
Cost is a pure efficiency measure for your decision-making, looking at the expenses of executing decisions. These costs include activity-based costs, the cost of data needed to make a decision such as credit scores or motor vehicle reports as well as the cost of system resources and other fixed and variable costs. These costs are separate from the costs of executing the processes in which these decisions are embedded. Cost must likewise be measured across the set of decisions and median, mean, maximum and so on might all be relevant.
Measuring Decision Yield
The first step in measuring Decision Yield for a decision involves finding out the answers to a range of questions about the decision. These will be different for each decision but some typical questions can be identified:
- How finely segmented is your treatment of customers?
- How effective is the decision in realizing your short- and long-term financial goals? (Precision)
- How well are customer- or agent-facing decisions made? (Precision)
- Is the goal behind these decisions to manage Customer Lifetime Value, long-term customer profitability, account profitability, or just current product line performance? (Precision)
- Do your decisions take into account past and future decisions? (Consistency)
- How well do different product lines coordinate customer marketing activities across channels and product lines? (Consistency)
- Are customers or prospects receiving conflicting or confusing communications? (Consistency)
- Are you leaving money on the table by not fully coordinating customer-facing marketing, cross-sell and retention decisions? (Consistency)
- How long does it take a business manager to change the way a decision is made right through to implementation? (Agility)
- How many resources (business and IT) need to be applied to change a decision? (Agility)
- How quickly can new information be brought to bear on a decision? (Agility)
- How much time do you need to return a price quote to a customer or prospect? (Speed)
- How long does it take you to design and execute a marketing campaign? (Speed)
- How many customers do you lose because your decision turnaround times are too long (Speed)?
- Are your decisions made through manual intervention or human review? (Cost)
- What data elements are used in a decision and what do they cost? How many times each is piece of data purchased relative to the number of decisions? (Cost)
To measure Decision Yield effectively you will need to develop a set of questions that are industry and decision-area specific. For instance, if you were working on establishing the Decision Yield for an underwriting decision, in place of the first question you might ask something like “How many tiers do you use in rating risk” or “How accurately do you predict the cost of claims for new customers”. These more specific questions drill into the precision, consistency, agility, speed and cost of the actual decision you are trying to improve.
By gathering answers to these questions you can come up with a measure of the current state of a decision in each of these five dimensions. The most effective way to track the effectiveness of EDM projects is to plot these dimensions for current state, future state and best practice. These are typically plotted on a radar graphic like this one:
The outer boundary shows the current best practice for this decision while the inner one shows the current state. The middle layer shows where each proposed project is expected to “expand” the Decision Yield for this decision. One of the key concepts behind Decision Yield is this measurement against best practice. Decision management is a never ending process – even if you make it to industry best practice on all five dimensions, the reality is that the standard will change and more work will be required to keep pushing the envelope. Deciding how close to best practice your business strategy needs you to be on each dimension for each operational decision is a key element for successful EDM adoption. This also means you will need to constantly re-evaluate your Decision Yield as “best practice” will improve over time. If you improve your Decision Yield out to best practice and assume you are “done” then you may not notice that you are falling behind competitors as they meet and then exceed the standard you set.
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