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What is Advanced Analytics?

Features

Advanced Analytics are analytical techniques, applied to research data, which go beyond simply describing the data – instead they explore the hidden relationships and patterns within the data. They allow us to infer things that are happening that cannot be seen with simpler cross-tab analysis. That’s why, in statistical circles, advanced analytic techniques are often referred to as ‘inferential statistics’.

There is an extensive range of tools and techniques in Advanced Analytics and they’re continually evolving. Perhaps the most common one that people have heard of is correlation analysis, typically used in key drivers analysis. Others include conjoint analysis, maxdiff, factor and principal components analysis, structural equation modelling, cluster analysis, linear and non-linear regression, Kruskal’s relative importance analysis, chi-square – the list of techniques available to the advanced analyst is extensive.

For me, one of the best things about being an analyst is that there’s always something new to learn. Trusted techniques such as conjoint are continually evolving and new ones are being developed. Every year, new software versions are released that offer significant improvements on previous techniques. Keeping up with (and sometimes leading) these developments is a challenge, but a very interesting and worthwhile one.

"I’ve worked in advanced analytics for ten years now and I am constantly learning new things. I started in the public sector at the Department for Transport, then spent time at global research agency Research International and have spent the last few years at specialist agency Bonamy Finch, based just outside London. Creatively applying statistical techniques to business problems and influencing business decisions is a rewarding career choice."
Giselle Hillman, Bonamy Finch

What do you need to get into Advanced Analytics?

  • A passion for stats – above all, you need to have a head for numbers and enjoy the process of digging deeply into data to extract its secrets. If you’re interested in uncovering insights without the numerical emphasis, then qualitative research might be more up your street. See Tracey Osunde’s article on this site for more information.
  • A relevant discipline – while it’s not a prerequisite to have a maths or stats qualification, a degree with a significant statistical component will be very useful. Members of our analytics team at Bonamy Finch have various different backgrounds – from social psychology to economics via applied statistics – and daily exposure to lots of different perspectives and approaches offers valuable learning opportunities.
  • Curiosity – as with most areas in research, you need to have an enquiring mind and a desire to crack a puzzle. But forget dry and dusty numbers: the challenge in Advanced Analytics is to apply everything you’ve learned to the problem in front of you and produce results that give the client a clear, practical sense of direction.

What has experience taught me?

For the aspiring analyst, my main advice is to make sure you join a company – whether agency or client-side – where you will be part of a valued team. A lot of companies only have one or two analysts, with few opportunities for professional development. In such instances, it’s possible to get pigeonholed as a “backroom number cruncher”, rolled out at the occasional meeting to add credibility to research findings, but with little actual real involvement in, or grasp of, the broader aims and business issues.

To be a good analyst – and to get maximum satisfaction from your job – it’s important to develop an understanding of the wider project, its context and business objectives. Before committing to a prospective employer, look to see whether they have a thriving analytics function already and how the analysts are integrated into the wider project team.

A fulfilling and influential position

The final thing I’d like to say about Advanced Analytics – and this doesn’t always fit with people’s perceptions of what it’s about – is that it’s very creative. As the lead analyst on a research project, the choices you make determine the outcome more directly than any other team member. Whether it’s deciding on a specific data transformation to create a range of segment solutions, or how to group potential drivers in customer satisfaction analysis, these decisions rest with you.

It’s this aspect of the job – creatively applying trusted tools and techniques in innovative ways to new projects and problems, and the ability to influence business decisions with insight – that convinces me that I made a good career choice in becoming an advanced analyst.

    Article provided by Bonamy Finch

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February 2018
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