True empathy isn’t about guessing what customers might need. It comes from involving neurodivergent individuals directly in research and service design.

Too often, customer experience frameworks are built on assumptions made by neurotypical teams, then retrofitted with “inclusive” language once issues arise. This reactive approach risks perpetuating the very empathy gap organisations are trying to close.

Market research has a critical role to play here. Inclusive research design, such as offering multiple ways to participate, allowing additional processing time and avoiding overly abstract questioning can surface insights that would otherwise be missed.

Neurodivergent participants often provide highly detailed, specific feedback when given the right conditions, offering a depth of understanding that benefits all customers, not just those with diagnosed conditions.

For our team, the future of AI in market research feels both transformative and uncertain. While there’s an expectation that AI might automate some or all of tasks like fieldwork, project design and analysis – potentially shifting the researcher’s role to more of a consultant or manager – it also raises significant concerns about the future of jobs and specialism in our profession.

A major concern about AI’s role in research is the potential for stunted professional growth among junior team members, who might miss out on developing foundational skills if they never need to pore through open-ends or dig through data tables.

What did the research uncover about what’s important to communicate in a campaign such as this? First and foremost, it highlighted the importance of finely balancing emotive and action-oriented messaging – appealing to both the head and the heart. Emotive assets, such as short films that tell personal stories, are highly effective at making people stop and listen – they raise awareness and play an educational role, especially when paired with eye-opening facts and statistics. Perhaps most importantly, by engaging someone emotionally, you’re making them care about the cause. The more people who care, the more potential supporters you have.

As we had briefed our participants that they would be conversing with AI via screening calls, the initial responses rolled in pretty quickly. Many were eager to compare the experience of an AI-moderated interview to a human one. 

Feedback was generally positive: participants appreciated the ability to respond at their own pace, on their device, at a time that suited them. Many were also complimentary of Gary’s listening and comprehension skills – something they were not anticipating to work so well.  

These kind of tools are simple… incrementally yet undoubtedly innovative, just in a subtle rather than screamy and headline-grabbing sense. They meet real needs, improve processes, and make our lives easier.  

So, let’s not forget the pigeons. They’re the AI tools quietly helping us work smarter, not harder. And in a world obsessed with peacocks, that’s worth celebrating. 

A few months back, we collaborated with STRAT7 Audiences on a research experiment to identify the true impact of heightened attention to every single respondent on the ultimate goal of any market research – business decision making. 

Conducting a study in the UK and US, we created three ‘typical’ research modules, rotated to avoid order biases: 

• Concept testing
• Campaign evaluation
• Audience sizing and profiling

Within these, we created two fake concepts for a new toothpaste brand and a fake OOH creative for this new brand to remove any biases derived from an existing awareness of a brand or campaign.  

Pare back your offering and bring emotional elements into your messaging.

Today’s world is overflowing with options and change for consumers, so understanding your brand and what it stands for is essential. Reduce the clutter and noise for consumers by paring back your offering to its core – focus on what sets you apart, and then communicate that clearly through emotive techniques to increase resonance.

Build rapport and trust through humour and sarcasm rather than matter-of-factness.

Humour and sarcasm are more effective in grabbing attention, breaking walls and building empathy – which lead to stronger resonance and trust.  As a result, brand messaging is focusing less on functional benefits and matter-of-factness, and stressing emotional elements through lighter, more humorous tones instead.

One example of this is Nescafe’s promotion of its pink creamer through a ‘Mean Girls Limited Edition’ post done on social media. The post taps into the nostalgia surrounding the film by alluding to Regina George’s memorable line and adapting a humorous “burn book” tweet. It not only emphasises the silliness of judging coffee choices but also subtly implies that embracing the pink creamer aligns with the movie’s theme of embracing one’s uniqueness.

Drumroll, please… Our survey findings showcase very positive results towards AI and this new tool. It seems that integrating AI probing into our survey process has sparked a surge in both the length and depth of open-ended responses, cutting across all key demographics. Let’s delve into the specifics:

1 – Character length increase

Respondents exposed to AI follow-ups witnessed a staggering 79% increase in character length, while those probed without targeting experienced an even more impressive 86% surge. Talk about boosting engagement and depth of insight!

2 – Quality score boost

We saw a big lift in the quality of the AI-probed ends too, with the quality score almost doubling. When informed they had encountered an AI-generated open-ended question, 58% of respondents admitted to investing more thought into their responses when encountering the AI probing.

This shows this new tool is not only capturing their attention but also encouraging deeper reflection and engagement.

3 – Positive respondent experience

An overwhelming 85% of respondents sailed through the survey experience seamlessly, reporting a smooth journey from start to finish, with 75% of respondents saying the AI question that was asked felt natural. We also saw a slight positive uplift in terms of those who said they like the open-ended question “a lot” for those who were asked an AI probe.

There are natural concerns to have when looking to migrate a tracker, so to ease your mind, here are some of the things we can commit to with every migration we carry out:

• Migrating and onboarding pre-existing trackers at a global level.
• Managing this process from start to end, including liaising with the current data providers.
• Merging previous data sets, so future waves merge seamlessly into the data deliverables.
• Matching current sampling methodologies or looking to improve the process.
• Ensuring quotas are matched.
• Matching timelines/SLA’s.
• Price matching!

So, how do we do it, and what do we keep in mind during the process?

1. Outputs

‘Why have we started at the end?’, you might ask. The answer is that outputs can often lead the first stage of the porting process. There might be redundant historic data, in which case it’s best practice to only bring across what you need and archive the rest. Either way, taking an outputs-first approach can help streamline the entire process.

We can use back data to ensure the output from the very first wave we run can contain all data from previous waves, whether in files or tables. This is a holistic process, and in line with the data-mapping stage at the beginning. It is also a consultative process to identify redundancies or reports/outputs that would be made more relevant following a review.

2. Data mapping

When we start to onboard a pre-existing tracker, we make sure to acquire the latest questionnaire and historic raw data files. Using this, we programme the survey so the data map matches the pre-existing file, and ensure we keep labelling and numbering across all files as cohesive as possible. This helps with mapping questions and slotting our new output seamlessly into the pre-existing data structure. In effect and outcome, it’s a newly programmed survey, matching the data map and copying the variables of the previous survey.

3. Survey style

To ensure consistency in the survey data, we’ll look to design and style the survey to match its predecessor, from both an aesthetic and a UX perspective. The survey should mirror the current survey to ensure that new look, feel & function do not create any artificial lifts in the data. This is especially worth considering if the existing tracker is particularly old/stale. Similarly, we can look to update this and modernise the aesthetics of the survey, so the data collected is more reflective of the current respondent engagement levels. Some surveys benefit from changes, some don’t, so we make sure we can do both.

4. Sampling

We can match feasibility and price to ensure quotas are consistent, wave-on-wave. We will dynamically sample this and keep an eye on any pre-existing, trended scores to ensure that new sample is in line with previous data.