Paul Watts

paul_264x264Associate Director

Paul is the stalwart of Redshift, a loyal team member that can be relied upon time and time again. With years of quantitative research experience under his belt, he uses his data modelling skills to provide clients with a variety of analytical services.

Paul’s specialisms include regression, pricing and KANO analysis and through his methodical thinking is able to provide clients with clear evidence for product development. Paul has also pioneered statistical development enabling the company to develop customer archetypes and personas. Paul has an excellent eye for questionnaire design and uses his analytical mind to estimate the implications certain results can have before a project has even been launched into field.


When not at work: Paul enjoys passing the time by sampling real ale in sunny beer gardens, genning up on his historical knowledge, and working towards his Chartered Statistician qualification.


Most memorable/favourite marketing campaign: Compare the Meerkats.  Silly but it has impact and is inventive, with an on-going story.



We are ready to help you

After conducting our second annual customer survey with Redshift they were instrumental in delivering the feedback workshops, leading discussions which enabled us to build on the findings and tease out the information necessary to inform the development of both the product itself, along with evidence to help identify best (and worst) practices to using the system.

We came away armed with plenty of ideas on case studies and the kind of messages need for internal publicity campaigns, that could both highlight the benefits of the system and encourage local improvements in approaches to using the system.
Redshift’s facilitation of the session was both invaluable on the day and for reaching agreement on key points to take forward.

Stakeholder, Engagement and Communications Manager  Home Office Technology 

Redshift’s Red Hot Tip

Questionnaire Design:

Keep the number of questions to a minimum to ensure highest possible data quality