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Presentation about combining analytics and user research, talk at UX Brighton event, 13 July 2010.
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Combining Analytics and User Research
Alex TarlingUser Experience Consultant
About the session:
Why is it good to combine methodologies?
Why doesn’t this commonly happen already?
Some opportunities to combine analytics and user research…
… some case studies and some hints and tips to get started!
Who am I?
Freelance user experience consultant
12 years experience of design research, UX, information architecture
Projects for Intel, BBC, Nokia, Orange, New Look, Blacks, Millets etc
http://linkedin.com/in/alextarling
User Experience Research
“User experience research is a collection of tools designed to help you find the boundaries of peoples needs and abilities“- Mike Kuniavsky
User Experience Research
“User experience research is a collection of tools designed to help you find the boundaries of peoples needs and abilities“- Mike Kuniavsky
”The field of user experience, is blessed (or cursed) with a very wide range of research methods“- Christian Rohrer
Web Analytics
"Web Analytics is the measurement, collection, analysis and reporting of Internet data for the purposes of understanding and optimizing Web usage.“- The Official WAA Definition of Web Analytics
Why use a combination of methods?
”When all you have is a hammer, everything looks like a nail”- Abraham Maslow
Why use a combination of methods?
All methods have strengths and weaknesses
Combining methods with different attributes allows us to:
Triangulate between the strengths of individual methods
Mitigate the weaknesses and risks of each
Why are user research and analytics methods not routinely combined?
Because user research and analytics are often commissioned and actioned by very different functions in the organisation.
Because user research and analytics often happen at different stages in the product cycle.
Analytics developed in a context to measure against business-goals, whereas user research is deployed as an aspect of the customer experience.
Attributes of different methods:
Observational User Research
Primarily qualitative: provides insights about users’ goals, motivations and attitudes
We can explore context and opportunities, and what doesn’t happen as well as what does happen
But, small sample sizes and high cost means we end up with a snapshot in time, and specific demographics. Lab settings can also be problematic
So, observational findings open to challenge / multiple interpretation
Web Analytics
Quantitative and based in real-world data – talks about what is happening.
Large sample sizes mean high degrees of confidence
But, interpretation of behavioural aspects is hard without additional customer insight
Where we predefine measures, prior assumptions about meaning and significance can become entrenched
Design of Terminal 5 - Risks of insight research…
Quantitative methods also have down sides...
”Not everything that can be counted counts; and not everything that counts can be counted ”- Albert Einstein
We have ‘abandonment’ issues…
www.shopsafe.co.uk/news/online-shopping-cart-abandonment-analysed-by-royal-mail/10098
We have ‘abandonment’ issues…
www.shopsafe.co.uk/news/online-shopping-cart-abandonment-analysed-by-royal-mail/10098
… assumptions about the implications of ‘abandonment’ don’t talk to real-world customer experience…
We have ‘abandonment’ issues…
(econsultancy.com/blog)
We have ‘abandonment’ issues…
= Still missing the opportunity to genuinely explore and design for customer experience!
Opportunity 1:
Use customer insight data to inform analytics measures So that analytics reporting genuinely
reflects and supports the customer experience, not just the business goals
Opportunity 1: use customer insight data to inform analytics measures
Deliver actionable analytics metrics from your customer research insights
(because user research and analytics often happen at different stages in the product cycle)
Broker communication and collaboration across informational silos, and between phases in the product lifecycle
(because user research and analytics are often commissioned and actioned by very different depts.)
How to get started:
Opportunity 2:
Use analytics data to drive the user research programme
E-commerce redesign project:
Driving the user research programme from analytics data
Opportunity 2: use analytics data to drive the user research programme
Just do it! You can do a lot with even basic
levels of analysis Think laterally about the sources of
data that are available Commission specific analysis of
existing data sources
How to get started:
Opportunity 3:
Integrate analytics and user research to optimise the user experience throughout the product lifecycle
The Open University
support for new distance learners:
Opportunity 3: Integrate analytics and user research to optimise user experience throughout the product lifecycle
Ongoing use of analytics to discover and target behaviors or
interactions for the research programme to ‘evidence’ user research insights
Rapid iterations of ‘fast’ user research interventions Targeted against specific analytics findings to generate insights and use-cases for
development
Integration means adapting the product lifecycle to support continual innovation ‘Launch early and listen’ and agile
strategies
How to get started:
In summary
Opportunity 1: use customer insight data to inform analytics measures
Opportunity 2: use analytics data to drive the user research programme
Opportunity 3: integrate analytics and user research to optimise user experience throughout the product lifecycle
Thanks!
