Why use the big engagement survey providers?

I like engagement surveys. But that’s often been because the experience of employers using them has been so bad that they then turn to me. The surveys have taken a long time, a large investment, and it’s just produced data, rather than insight. Or, it’s highlighted an issue, but offered no thoughts on how to explore it more. Or, the provider has failed to analyse any of the free comments, massively devaluing the work.

I’ve been happy to step in and provide something more tailored and more useful.

But recently a number of people have approached me to create their own engagement survey. It seems their clients are not signed up to the necessity of benchmarking. They don’t see value in comparing engagement with other employers. They’d rather create something more bespoke, that will give them actionable results, whilst still being able to compare results longer-term.

And so I’ve stepped in here too.

I’ve given a few options. The first of which is to base it on existing, established engagement questions. But this feels like a bit of an unnecessary compromise. You’re confining yourself to fixed questions, without the benefit of benchmarking. So instead, I’ve proposed:

• Measuring my own set of (up to) 20 engagement factors. First their importance and then how well they are delivered at that employer. These factors are based on my research, what others measure and the work of Engage 4 Success. But they’re infinitely tailorable and addable-to.
• A purely free-text survey – usually asking what’s good/what’s not or what would you start/stop/continue.

Or, ideally, a combination of the two. By doing this, you’re getting your consistently-measurable scores, whilst also allowing more input - even-co-creation of solutions - by employees.

It still might only point at the issues, rather than supply the total solution. But surveys have their place, and for me this is a quick, cheap, bespoke way to approach it. (And I’ll always, always give you insight.)

 

What makes good research?

For a couple of recent pieces of work, I’ve had to think about what makes good research. Not so much how you conduct it, as how you conduct yourself. Not what you do, but how you do it.

And I’ve got it down to two things that research absolutely should be:

1. Simple 
2.  Commercial

Yes, simplicity is about turning complexity into clarity. But it’s about making the process simple too. There’s a temptation when you’re the expert to blind with science. But you can really demonstrate expertise by making a knotty question or a complicated process seem accessible and achievable. Experience of understanding what you can and can’t know will help.
There’s a temptation too to think you must get to all the answers straightaway. Often you just need to report the facts, neutrally. It may take time for the client to get up to speed with what you’ve found, so make it simple for them to access. Other times, once you have established the facts, the recommends start to create themselves.

None of this is to say that I don’t like to slip in a big word*– to have “axiomatic” edited out of my last report was a blow.

The commerciality is about making it a profitable piece of work, naturally; no-one buys a freelancer to lose them money. But it’s about making it actionable, for the client. Even if it can’t tell them “what next?” it must tell them “so what?” And – if having proven yourself, if having given them some strong affirmation, if having given them an unexpected insight– there’s an opportunity to suggest the next project, you should. If they take you up, that’s the best feedback you can get.

But commerciality is about being pragmatic; there are very few clients with infinite budgets and patience. Cost and time shape most projects. I believe you have to challenge that, but be more than prepared to just crack on if you can’t shift them. Otherwise you’re making it more about you than the client – and that’s the antithesis (* see?) of commercial.

What do you think? What qualities make good research?

Detail. Again.

It’s only in starting to write this (and therefore filing it my perfectly constructed and maintained filing system) that I realised I’ve blogged about detail pretty recently. People will start to think that I’m obsessed.

But this time I wanted to talk about the dilemma that I come across often. I’ve just reported on a survey, and in that I had a hundred and fifty odd responses, from various grades in various locations, with both quant/rating and qual/free text questions. That’s a lot of data points. But my summary and recommendations ran to precisely 899 words. If it didn’t put quite so much spacing in it, that’d barely run to two pages.

Is that enough? Is that value for the time I’ve spent and charged on it?

I think so. (I hope so, I’ve not had feedback yet…) And it’s the approach I’ve followed for a long time, with some success. Indeed I remember a distinct stage of my career when the reports/proposals that I wrote changed from being judged on how long/comprehensive/detailed they were to how short/concise/to-the-point that they were. And a sadistic-yet-helpful boss that used to get me to summarise, strictly on one page. And then to do it again, but this time double-spaced.

So, taking this report as an example, I’ve got those many, many data points (about 2,104 since I’m doing precision today) down to two main themes. And in doing so, I can then make 10 recommendations to specifically tackle those two themes.

Now, there’s also another 50 odd pages of charts where I report on and summarise all of that data. At the appropriate time, that’s available to delve into. But if I reported on all of that, in real detail, upfront it’s going to obscure: “what’s happening?” and “what should I do?” And I think that the real value is in summarising and summarising again, until I – and by extension my client – is confident that we’ve hit the nub of the matter.

Data's just numbers, right?

I’ve written about most of the types of research that I’ve done before. In, what some people have described as, exhaustive detail. If you look that up in a dictionary, it’s a compliment. But I’m not 100% sure that they’re using it in that sense…

I’ve talked about enjoying the challenge of qualitative research. Bringing together multiple, divergent, abstract and often contradictory ideas. I like piecing that together into the story that best makes sense of it; that connects most of the ideas, most often. And within that there’s some perception, some inference, some creative license.

Data is different. Data is hard. Data is absolute. And that – tragically – excites me too. You’ve got all the pieces of the jigsaw, and they’re only ever going to fit together one way. You can’t obfuscate, you can’t flim-flam, and you can’t use your intuition. (And sometimes it’s nice to turn off and just chunk through data. Especially, say, if there’s a test match on.)

BUT, there’s still a story to be told. It’s rarely any use just taking data and pumping out some graphs. (Quite apart from anything else, your choice and layout of graphs will determine how easily people can make much sense of them. But that’s another blog….) You need to start to link themes together. What might the low-rating here mean next to the high rating there? What could cause that apparent contradiction? Why are these people scoring higher than these?

Sometimes the outcome is that you need to do further, research to understand fully. But it’ll be precise and targeted – and hence a lot cheaper than it might have been. Other times, you’ll get the answers, and context, that you needed. Data tells a story. And it’s a story I love finding.