helping you master customer insight leadership

How can you make your customer research faster? Should you?

research faster

Why can’t you get me that research faster?” That is perhaps the most common challenge I hear is faced by research leaders.

I pointed out, in a post of ‘research as fashion‘, that a common theme in case studies, was the challenge to get research faster. So, as promised, this post will share a number of relevant posts and resources on that topic.

Let me first state that faster is not always better. Aside from the wider “In Praise of Slow” movement, our previous posts on “Thinking Fast & Slow” & “Deep Work” make this point.

Faster work can mean lower quality thinking & be more susceptible to the biases of brains on auto-pilot. So, it can often be worth pushing back, when more robust data & careful interpretation are needed.

But, we like to be pragmatic here. In real businesses, whatever the pros & cons, customer insight leaders need to deliver quicker. What are some of the options to achieve this with research work? (more…)

Why consumer research is like fashion, and the role of your staples

research is like fashion

I didn’t realise consumer research is like fashion, but a webinar helped me see it & value “old staples“.

Given topical concerns, about gender balances in work, it was interesting to experience a female bias. Joining a useful webinar, hosted by Quirks and with speakers from 2020 Research, I began to feel out-of-place.

Our hosts were women (I’d say younger, but most people are beginning to feel younger to me) & audience was mainly female.

What helped me get a feel (for what it must be like for women in male dominated events), were the analogies. Focussed on the topic of why old research methods are still relevant, the analogy was the world of fashion. I lost track of the number of times older research methods were compared to the staple of “the little black dress“. The analogy worked well, but much of the language used reminded me how unaware I can be of gender bias in content. A useful reminder. (more…)

Seven characteristics, of an ideal B2B Influencer, for researchers

B2B influencer

On this blog, we don’t want to neglect B2B insight challenges, including finding a B2B influencer. Much of our content focusses on B2C customer insight work. So, in this post we look at one for B2B businesses.

This builds on our past posts, sharing tips for effective B2B Customer Management. One of our familiar guest bloggers, who is also a B2B influencer, shares 7 characteristics to help you spot ideal ones.

There’s more of a focus, on the role of Big Data & Data Science, in spotting key influencers within social networks. Numerous case studies, focussed on B2C mass market, demonstrate how analytics can identify them. However, there is also a key role for market research & judgement. (more…)

Take your own medicine, two surveys for research leaders


An opportunity to take your own medicine, as a leader, is nearly always helpful.

Whilst sending out GDPR communications, to my clients & contacts, a number have praised seeing someone “take their own medicine“.

Similarly, we have previously recommended that analytics & database marketing teams visit customer touch-points, or observe the impact of their leads.

So, during this month focusing on research, here is some ‘own medicine’ for research leaders: A quick post, recommending a couple of surveys. (more…)

Implicit Research Methods, resources on those you should consider using

implicit research

Following Katie’s useful introduction to behavioural research, this post surveys implicit research techniques.

Given the relevance of implicit research techniques, to monitoring behaviour, it makes sense to dig deeper.

In this post, I will share the breadth of these technique, current usage & academic basis. Plus, to finish, I’ll share details of an upcoming event in London.

As we’ve shared before, learning in the field of Behavioural Economics (and related fields) has raised the bar for researchers.

The annual Behavioural Economic Guide, reveals how much work is happening. How can Customer Insight leaders best ensure, they have behavioural evidence, as well as just self-report? Due consideration needs to be given, to permission for data capture, as outlined in GDPR. Once that is addressed, implicit methods can help. (more…)

Are you ready for Behavioural Research? Ask yourself these questions.

behavioural research

This month, we will return to the topic of research, starting with behavioural research.

As we discussed, when sharing posts on Behavioural Economics, behavioural biases cast doubt on self report. If people aren’t aware of their behavioural biases, can you trust survey results or focus groups? The answer is more nuanced, due to other advances in research design.

But, such concerns have caused some to refocus research effort on behavioural research or experiments.

So, to help us start to explore this area further, I’m delighted to welcome Katie Hagan, as our latest guest blogger. Katie works for Netquest, a leading global data & research agency. Talking with their UK MD, Johnny Caldwell, he put me on to this interesting article from Katie; together with a free e-book for readers. (more…)

The average week of a Data Scientist, hearing from one on what they do

average week of a data scientist

As we come to the end of a month, focussed on Data Science, lets hear about the average week of a Data Scientist.

On this blog, we seek to get past the spin & platitudes of much advertising on data science. So, I’m delighted to bring you a new guest blogger, to share his reality.

Chris Bose is a Data Scientist, running a small technical PR company, In Press PR. As you’ll read, his focus is on smaller datasets & textual data. However, I believe his experience helps illuminate the reality for many data scientists.

So, prepare to look beyond the infographics & theoretical articles, here is what a real data scientist spends their time doing. Over to Chris, to share his answer to my question: “What do you spend your time doing? What is the average week of a Data Scientist?(more…)