A Simple Convergence Model

A Simple Convergence Model

Early in the design stage, we expend considerable time and effort and learning about the desired outcomes of the entire design process. While we may not know the specifics of the destination, we do know a bit about where we think we would like to end up. One of the outputs of the design stage should be a thoughtful set of initial criteria to help guide the process and develop an image of what the overall outcomes may be like. Through successive discovery and divergence, the world of possibilities opens up, and as intended they should provide far more opportunities than we could or should pursue. Convergence allows us to make the best choices moving forward as resources are added to the refined design moving toward implementation.

Step 1: prepare. The pattern of convergence is reducing and combining ideas to build progressively valuable and implementable solutions. Early in convergence, the choices are easy. During the preparation stage, focus should be given initially to developing processes to help make easy early choices. It’s time to revise the initial criteria from the design brief or plan. Do these initial criteria make sense at this point? How should they be adjusted? Also, consider what has changed in the design process. What critical assumptions must be made going forward? How do they help refine choices? At least one decision pass or phase through criteria-testing-selection must be made. On the most simple end, it can be done in a group discussion; on the more complicated, a test could involve a large number of participants, data collection, and sophisticated analyses with statistical decision models. At either end of the spectrum, be intentional about each phase. In most case, I’ve found that multiple decision passes or phases need to be made to narrow options down far enough. When establishing these processes, ask questions about the number of stages, the rigor of each (i.e. data collection, metrics, inputs, decision making), and the inputs and outputs (from how many to how many). Sketching this out during preparation is helpful, if not required activity. One sketch might look like this.

Steps 2-4: criteria-test-filter-select-repeat. It can be hard to know exactly how much filtering and reduction can be done at any one pass. There are many standards and metrics for innovation and product development pipelines and several rules of thumb. My research suggests that these metrics and average rates vary considerably by industry with some convergence funnels or pipelines beginning with thousands of ideas and opportunities being narrowed down only a handful for implementation with a rate far less than 1%. At the less selective end of an innovation process we may have 100 or so good ideas that lead to 15 that could be implemented representing a 15% success rate for ideas.

When convergence process are repeated often or use precise tests, you can develop expectations for how the process will go. The following graphic shows where metrics or expected counts can be applied to a three pass convergence process:

1. How large is the entire convergence process? Count the ideas that resulted from the divergence process and exist at the beginning of convergence. This can help you understand the resource required for testing relative to the benefits you get through precision in the process.
   
2. How selective will the first filter be? Determine the percent reduction of the number of ideas or as a success rate. You can change the criteria for testing to move the rates up or down.
   
3. How many ideas or opportunities should go through the second set of tests and filters? In some cases you may want to really limit the number of ideas that pass through and keep the success rate low. For example, if testing is costly or may take a long time. In other cases, you may want more ideas to pass through to be sure too many early limitations don’t result in too few at the end.
   
4. How selective will the second filter be? Depending on the nature of the tests and processes used to understand the viability of an idea, the final test and filter may need to yield a polished idea ready for implementation. If the convergence process is focused on a product or service designed to go to market, some form of customer feedback should be included.
   
5. How many opportunities need to emerge from the entire convergence process? If you know that you need the three best options for final comparison prior to implementation, you can design the last stages of testing to deliver a specific number by changing criteria or the testing and filtering process.
   
6. Finally, a rate of reduction or success for the entire process can be determined by comparing the number of ideas at the end to the number at the beginning.

1 # of opportunities at the beginning of convergence

2 % reduction by the first filter

3 # of opportunities in the second pass

4 % reduction by the second filter

5 # of opportunities at the end

6 % reduction by the entire process

Step 5: conclude. Whether a science or art, understanding how ideas flow through the convergence funnel is important. The convergence stage ends when you have either viable early-stage prototypes or a design even more evolved and ready to implement. Sometimes things can go awry and convergence can yield nothing viable or ideas could be reduced too far. In this case, we may go back and forth between divergence and convergence if none of the choices appear to be working or back to the tests and filters and modify the criteria to produce more at the end. When convergence is followed by prototyping, as I suggest in my 6-stage design model, the final filter should be tuned to ensure an early stage prototype emerges from the process, which can be further refined through prototyping (more on this in the next article).