helping you master customer insight leadership

The Hidden Blocker in Delivering Great Analytics Projects: Empathy


Continuing our posts on how to achieve success at work, let’s turn to the power of empathy.

This might sound far removed from the worlds of Analytics & Data Science, but as we saw with Hackathons, it can be a vital step.

To help bring to life how greater empathy could breakthrough barriers for your Data Science projects, I am delighted to welcome back Ryan. Guest blogger Ryan Den Rooijen has tremendous experience from his work leading Data Science teams in Google & Dyson.


Are you a Superhero at work? Are you a cultural fit?

cultural fit

Continuing our series on helping you succeed at work, let’s turn to the question of cultural fit.

So far this month we have covered topics including the benefits of fuzzy Analytics team roles, avoiding VoC program pitfalls and Data Science methodologies. Now let’s take a step back from role specific advice.

In her latest guest blog post, Hanne Sorteberg returns to share her advice on the importance of cultural fit. I’ll leave her to explain how this relates to being a superhero (whether or not you wear a costume).


10 more all too common mistakes and how to improve your VoC program

improve your VoC program

Continuing our series to help improve your Voc program, here are 10 more mistakes to avoid.

After the popularity of her first post in this series, I’m delighted to welcome guest blogger Annette Franz to share 10 more tips. As I mentioned before, Annette has many years of client-side experience in getting Voice of the Customer (VoC) programs to work.

As is the case for Customer Insight leaders, most of the best learning comes from getting things wrong. So, here is another list of 10 pitfalls which Annette has discovered you need to avoid.


10 all too common VoC program mistakes and how to avoid them

VoC program

Our focus this month is how to achieve success, including with VoC programs.

Previously, Alan Murray shared his advice on keeping analytics team roles fuzzy, especially for smaller teams. In this post, guest blogger Annette Franz returns to talk about Voice of the Customer (VoC) programs.

Like Annette, I have all too often seen such programs lose their way or just be badly designed from the beginning. Whether using NPS, CES or CSat scores, the focus can become on the metric not action from insights.


Setting up an Analytics team for success = Get Fuzzy!


Building on our month focussed on controversial topics, let’s turn to what will set your team up for success.

Different contexts can require different types of analytics team. A lot of the advice that I offer within the Opinion section of this blog is based on a lifetime leading teams in large corporates. So, I’m pleased to partner with guest bloggers from other settings.

On that basis, I am delighted to welcome a new guest blogger. Alan Murray. After a career in the worlds of consultancy & large corporate, Alan has spent plenty of years in both medium-sized firms & lately startups.


Unicorn Farming: Building Capabilities Against the Odds (Part 2 of 2)

Unicorn Farming 2

Guest blogger Ryan Den Rooijen returns to complete his two-part series on Unicorn Farming.

It’s been great to see the conversations that part 1 has sparked on social media. It’s always encouraging to hear other experienced analytics leaders, like Martin Squires, agree with many of the key points & share their insights.

Hopefully you find part 2 as interesting and useful. I’m tempted to make a “neigh” joke, but I’ll resist. Here is part two, with a focus on how to assess candidates background & experience.


Unicorn Farming: Building Capabilities Against the Odds (Part I)

unicorn farming

Continuing our focus on controversial issues, let’s talk about unicorn farming.

I’ve previously used the term recruitment & retention, but Ryan den Rooijen has the more poetic term of Unicorn Farming. How to successfully recruit for your Data Science team.

Ryan is Global Director of Data Services at Dyson and has previously blogged for us on data artists. We also interviewed Ryan on his role, so you can find out why he know what he’s talking about (including his background at Google).