Empathy maps, customer journey maps, experience maps, and service blueprints depict different processes and have different goals, yet they all build common ground within an organization.

Until the definitive answer is provided, perhaps an N of 30 respondents is a reasonable starting point fordeciding the qualitative sample size that can reveal the full range (or nearly the full range) of potentially important customer perceptions. An N of 30 reduces the probability of missing a perception with a 10percent-incidence to less than 5 percent (assuming random sampling), and it is the upper end of the rangefound by Griffin and Hauser. If the budget is limited, we might reduce the N below 30, but the client mustunderstand the increased risks of missing perceptions that may be worth knowing. If the stakes and budgetare high enough, we might go with a larger sample in order to ensure that smaller (or harder to reach)subgroups are still likely to be represented.

7 customer research sources: 1/ Media Kits 2/ Google Scholar 3/ Amazon Reviews 4/ The New Forums 5/ Comment Sections 6/ Customer Data 7/ Interviews

The Data Playbook is 120 exercises, games, scenarios, slides and checklists to assist you and your teams on your data journey. The social learning content is designed for teams to have discussions and activities across the data lifecycle in short 30 minute to 1 hour sessions.

We want to take the shortcut and ask the why question, but please, resist the urge. Reframe it and you’ll find you are getting a more honest answer that is closer to authentic truth.

A cognitive walkthrough is a technique used to evaluate the learnability of a system. Unlike user testing, it does not involve users (and, thus, it can be relatively cheap to implement). Like heuristic evaluations, expert reviews, and PURE evaluations, it relies on the expertise of a set of reviewers to assess the interface. Although cognitive walkthroughs can be conducted by an individual, they are designed to be done as part of a group in a workshop setting where evaluators walk through a task in a highly structured manner from a new user’s point of view.

Whilst you’re shaping the problem space and then during the first diamond of understanding and defining which user needs to focus on, you should ideally get out of the lab or the office. When you have defined your solution and are iterating on it, that’s the best time to use your go to method — lab usability testing in a lot of cases, remote interviewing is mine. This is because you are likely needing cycles of quick feedback and iteration so you need a tried and trusted method so you can spin up a sprint of research quickly and efficiently. So how about when time and efficiency isn’t quite so important and the quality and depth of understanding or engagement of stakeholders are the key drivers? Here are some examples from my toolkit:

Broadly, these feedback surveys can be categorised into five groups: the pointless; the self-important; the immoral; the demanding; and the downright weird:

Method:Three participatory workshops were held with the independent Welsh residential decarbonisation advisory group(‘the Advisory Group’)to (1)maprelationships betweenactors, behavioursand influences onbehaviourwithin thehome retrofitsystem,(2)provide training in the Behaviour Change Wheel framework(3)use these to developpolicy recommendationsfor interventions. Recommendations were analysed usingthe COM-B (capability, opportunity, motivation) model of behaviourtoassesswhether they addressed these factors. Results:Twobehavioural systems mapswere produced,representing privately rented and owner-occupied housing tenures. The main causal pathways and feedback loops in each map are described.

EMERGE (Evidence-based Measures of Empowerment for Research on Gender Equality) is a project focused on gender equality and empowerment measures to monitor and evaluate health programs and to track progress on UN Sustainable Development Goal (SDG) 5: To Achieve Gender Equality and Empower All Girls. As reported by UN Women (2018), only 2 of the 14 SDG 5 indicators have accepted methodologies for measurement and data widely available. Of the remaining 12, 9 are indicators for which data are collected and available in only a limited number of countries. This assessment suggests notable measurement gaps in the state of gender equality and empowerment worldwide. EMERGE aims to improve the science of gender equality and empowerment measurement by identifying these gaps through the compilation and psychometric evaluation of available measures and supporting scientifically rigorous measure development research in India.

People regularly ask me how to perform a systematic *web* search. Finally found some time to organize my ad hoc tips and relate these to steps in a systematic scholarly search. Despite the options, web search will remain less controlled and a fuzzy patchwork.

The issue is: We try to solve every single box in the problem tree. If people don’t know about something, then we solve it by raising awareness. If people don’t care about something, then we solve it by getting them to care more. If people are doing illegal behaviors because of a lack of enforcement, then we solve it by increasing enforcement. We go through the whole set of problem tree causes in this manner, writing objectives with a one-to-one match per problem. Not only does this result in a long list of objectives, which will quickly overwhelm us, it also traps us into solving behavioral problems using logic-based approaches.

The higher the sensitivity of data breached, the more significant was the impact on stock prices

Social engineering is free for hackers but fraught with danger for regular users

In March 2020, more than one-third of Dark web websites data was stolen

That’s why we’ve developed an evidence-based approach to identifying and prioritising the most suitable behaviour(s) to address a problem: The Impact-Likelihood Matrix (ILM), developed by our very own Sarah Kneebone. By undertaking a rigorous investigation of the literature and audience research, our technique ensures that the behaviour(s) you choose to target for your intervention or policy will have the highest likelihood of driving the change you are seeking.