helping you master customer insight leadership
Another tip for success with your Data Science team is to be more methodical.
By this, I mean to establish and use a consistent methodology, process or workflow. This will enable repeatable results, simpler collaboration & knowledge transfer. If it is a well
A few different influences have had me thinking about this topic recently.
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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 (
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.
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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 (
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.
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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.
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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.
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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).
We’ve shared some conflicting views on the need for business partners, but is data everyone’s job really?
Should transforming a business into a data-led culture be the challenge for Data Science leader or CDO? Or should this cultural transformation be shared by everyone in the C Suite?
To make the contention that data is everyone’s job (or at least every leader’s job), I’m pleased to welcome a new guest blogger. David González is Group Head of Big Data Analytics & AI for Vodafone Business.
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