Creating a framework for designing consultations  

Recently, MHCLG Digital was asked by a policy director to help their team design a better consultation. What do we mean by better? One that is more engaging, wide-reaching and produces more useful insights. We had an MHCLG precedent for this – our Social Housing Green Paper was informed by dozens of co-design events held with social housing tenants up and down the country.

The team were passionate about bringing the voices of those with lived experience into the policy development. This paid off because tenants provided feedback on proposals’ viability as well as putting forward their own ideas.  

In order to explain how to do this to colleagues who are most used to traditional government consultations, we spent some time refining how we present what we know about participatory design and consultation. 

Working backwards from examples 

Participatory design is decades old and we were all familiar with various examples of co-creation. Two of the initiatives that came up as we prepared this piece of work were Better Reykjavik and Martha Lane Fox’s House of Lords Life beyond Covid enquiry. If you attend a conference like Service Design in Government, which a few of us did in March this year, you hear about lots of exciting examples of co-design and consultation.  

The Life beyond Covid enquiry is on the open web 

However, any example is inevitably very specific to that consultation’s aims. We were having to repeatedly extract the general principles behind the example and explain how they could be applied in a different consultation. We didn’t have a good way of showing people the different ways of consulting and allowing them to pick or narrow them down based on their aims. So, using all the examples that sprang to our minds, we developed a structured framework. 

Big data versus rich data 

One of the clearest recent articulations of service design principles is described in Policy Lab’s "thick data" blog post. For our model, we made use of their distinction between big datasets that tell you what is happening at a macro level and thick, rich data from just a few individual cases that tell you what exactly is happening at a micro level – and, crucially, why it is happening. In a consultation you could be interested in either or both of these – and although both types of insight could be delivered, different interventions would be needed to do this.  

Consulting for a particular stage in the design process  

It wasn’t enough to differentiate between big and rich data. We also thought about the types of insight or response that policy teams might want to obtain through consultation, and how they could use them. Consultation takes time and money, so it’s essential to create a process that will provide results that are relevant and usable. If you don’t get this right and the consultation responses are too general or too specific, they could easily be dismissed or ridiculed. Not only is this a waste of effort but, more significantly, it could impact the reputation of the consultation process itself. 

Looking at different types of consultative exercise, they seemed to fall between two extremes. At one end was general context, the landscape which a new policy would enter and interact with, and at the other was specific feedback on and refinement of proposals. This led us to create categories that we view as concurrent with the policy development process: more general information is useful early on; further down the line the parameters are tighter so input needs to fall within them to provide actionable insight.  

Policy Lab is a leader in working at the contextual, exploratory end of the scale, whereas the vast majority of government consultations take place at the other end: a good deal of development has already been done and specific comments on proposals are invited. 

Something we’ve learned from the world of digital services is that if you don’t do that work to understand the problem space before building a “solution”, you risk starting down a path that isn’t the best one for users or beneficiaries of a policy. And if you’re unable to flex and iterate, you might get stuck on that path, storing up risk, only finding out if it works after that big bang launch moment.  

That said, the time for consultation is not only at the start of the development process. There is value in each stage (you may notice that our model also roughly corresponds to the stages of digital delivery: discovery, alpha and beta). Our 4 categories are: 

Example consultation exercises for each type of intervention 

 Identifying these 8 types of consultation means we now have an offer – a menu – that we can use to help us create interventions for future consultations. We won’t stop being inspired by others, but using our model, we can quickly think through the purpose of any initiatives we’re familiar with or hear of in future. Instead of starting a conversation about consultation by describing something someone else has done, which can come across as lazy, we can talk through the objectives and benefits of different models from scratch.  

Having a framework to talk about the aims of consultations will hopefully enable more teams to understand how they can tailor the intervention they design to achieve the specific results they want. Consultations don’t have to be digital versions of an old-school survey – it’s possible to gather serious, relevant insight from methods as varied as videography, up-voting comments, sketching or a simple conversation. 

We can’t wait for the next opportunity to help a team refine a policy idea through consultation.  

Do you want to reuse this model? Feel free! Leave us a comment if you want to discuss it more.