Webinar series
on hybrid research use cases

Discovery for innovation needs both qual and quant. But the usual qual-then-quant sequence breaks: meanings get lost in handoffs, and numbers arrive too late to steer the exploration. We'll show a different design: a single hybrid dataset built in rounds, where quant points qual to the next dig. All in one compact study with no handoffs. The result: opportunity spaces that are validated enough to invest in and rich enough to hand straight to ideation.

Testing raw ideas is tricky. Quant scores them, but people react to unfinished ideas unpredictably — the numbers won't tell you whether an idea truly resonates, or why. Qual gives you a rich list of hypotheses but says nothing about potential. We'll show a hybrid design that runs qual and quant in a single iterative flow — so you get a reliable read on potential and a clear picture of what works in the idea and how. Enough to confidently pick what to develop further, and to know exactly where to take it next.

Brands need personas to focus their communication and to build the right products. Good personas take both qual and quant: you need the details that make each segment distinct, and you need confidence that the segment is big enough to matter. But segmentation usually means a long, expensive quant study, sometimes with no qual stage at all. Nuances go unexplored or get lost in translation. We'll show a different design: a single-source hybrid study built in rounds. Round one captures spontaneous drivers, barriers, attributes and behaviour patterns, and forms draft segments. Round two enriches and validates them. The result: segments that are sharper, richer and more vivid, delivered in a fraction of the usual time.

Testing in overclaiming markets is tricky. Whatever quant metrics you use, the concepts end up scoring about equally well, so you need a more sensitive thermometer. Qual can tell you whether an idea connects with a person, and why. But it can't be scaled to the population, so it can't support a go/no-go call or a choice between ideas. We'll show a hybrid design that runs qual and quant in a single iterative flow. First we collect a long list of drivers and barriers. Then we test each one under the microscope: qual checks whether it's genuine (spontaneity, emotion, personal relevance, reasoning, depth of detail), and quant measures its strength (likeness, uniqueness). The result: a confident answer on whether an idea really lands, and a clear winner when you have several.

Post-launch is a quick check of whether a launch went to plan. It's usually done in quant. But a quant questionnaire is built on our own hypotheses, and the real story often sits where we didn't expect it or never thought to look. Spontaneous answers are where those insights live, and a pre-programmed survey can miss them. We'll show a single-source hybrid design built in rounds. It gives you the metrics and KPIs, and alongside them the spontaneous drivers and barriers, in respondents' own words, and validated. The result: no hidden reason for success or failure slips through, and you have every number the business needs to decide with confidence.

When a category feels fully explored, the next big idea is often hiding in a weak signal. These signals don't show up on their own. Finding them takes spontaneous answers and deep probing into motives, barriers, pain points and unmet needs. You also need to know how widely each signal is shared. The usual qual-then-quant sequence struggles here: meanings and nuances get lost in the handoff between stages, and once the quant is done, the numbers can't be unpacked any further. We'll show a design that wasn't possible before: a single-source hybrid study built in rounds. It combines deep qual digging, reinforced probing (where emerging ideas are tested with other participants in the study) and validation. The result: fresh signals for product and communication development, with clear priorities on which ones are worth building on.