If a company offers products and services that have personal relevance, well, the cat’s half in the bag.

If they can work out how to tell me about those relevant products and services, in a relevant way, at a relevant time, then they’ve got themselves the full cat deal.

But what is relevant?

Hooray for qualitative research!

Maybe I should stick to knitting, but despite my obvious leaning towards qualitative (vs quantitative) research, I feel compelled to write about a quantitative research issue that isn’t quite getting the consideration it should; sample representativeness. This, I might add, seems to be a problem particularly – although by no means exclusively – for quantitative research conducted in the online environment.

Sample representativeness

When we (when I say ‘we’, I don’t mean you or me of course, I mean them, but let’s go with ‘we’) undertake a quantitative market research study, it’s rare that we have either the time, or a budget, that would allow us talk to each and every customer/potential customer of interest.

Instead, we choose a selection of those customers/potential customers to represent the greater population of interest to us. In research-speak, this selection is called the sample.

In a quantitative research context, the way you choose your sample, and the structure of that sample, is everything. These two factors will pretty much define the extent to which you can extrapolate your research findings to the population of interest. In non-research speak, that means the extent to which you can have any confidence in the research results.

Making sure that the sample you want to use to generalise to the greater population of interest is representative of that greater population is;

1. Of vital importance

2. Not always easy

3. Of vital importance

Number 3 isn’t a typo. Issues arising from number 2 often mean that the sample may be seriously compromised. I put number 3 there as a reminder.

Raise your hand if you can think of some brands that need to work on customer engagement.

And/or raise your hand if you can think of some brands that should be (more) transparent.

And/or lastly, raise your hand if you can think of some brands who should join the ‘conversation’.

If you’ve got your hand up, maybe you can answer this;

What, exactly, do you mean?

Go on. Define ‘customer engagement’, ‘transparency’ and the ‘conversation’.

No wait. I mean in a useful way; a way that can be operationalised and measured.

It’s almost 5 months to the day since the first Side of Porcupines * post; definitely time for another one!

The criteria is the same as before, but just to highlight the 3 most important things;

1. You have to have written it,
2. The post doesn’t have to have attracted hits/comments (but it’s ok if it did),
3. You just have to like it and/or want to share it.

Link away!

: )

* An overview of Porcupine reads

There’s lots of talk about the future of the market research industry and its move (was slow, now fast) to online. And exciting as it all is, something about it bothers me.

I can see two key factors that would logically spur the growth of online qualitative* market research;

1. The perception that online qualitative research can deliver better insights than traditional offline methods (for the purists)

2. The perception that online qualitative research is more efficient than offline methods. Specifically, the perception that you can do more, faster, and for less (for the realists)

And, for each project we undertake, my three key questions;

1. Can it?

2. Is it?

3. Are we thinking about questions 1 and 2 hard enough?

When people consume a piece (?) of advertising for the first time, they don’t typically sit down and analyse it 5 minutes later.

Thus, when you test a creative concept in focus groups or in-depth interviews, the last thing you want respondents to do is analyse it. In fact, one of the most important things to do in concept testing research is to make sure your research participants don’t think.

Instead, it’s initial impressions you want; that very first response.

Don’t want helpful; don’t want clever

Concept testing presents two interesting challenges for market researchers:

1. People are helpful

2. They don’t like to look stupid

People are helpful: When you ask people to take part in a research project, the basic understanding is that their ‘job’ is to help you. ‘Helping’ often (and in most cases, logically) takes the form of concentrating and thinking.

They don’t like to look stupid: Respondents (aka humans) also want to look clever, or at least not stupid, in front of the rest of the group or moderator.

These two factors can easily get in the way of capturing a valuable first response vs a ‘tainted’ intellectual one.

I’d almost forgotten about this. It’s from my earlier days as a researcher, a project I helped my mum with.

And here’s a lovely overview of her work from when she won the University of Sydney’s Vice Chancellors Award for Outstanding Teaching 2001.

Now she’s retired, she’s taken up botanical painting (as you do); that’s her work above.

Quite a woman indeed.

: )