Selected work from behavioural strategy consulting engagements across product, e-commerce, marketing, and research.
The challenge
ZIYX had built a new version of their app and needed to know whether it worked for their target users — NEET individuals who hadn’t gone to university. They had assumptions baked into the product about how users would navigate the experience, including a 3D metaverse environment that represented a significant investment. They needed to understand: does the onboarding flow work, does the core experience engage this specific audience, and is the metaverse worth continuing to develop?
The solution
A fully completed survey panel of 1,200 respondents was run, all from the target demographic. Users were sourced through four email campaigns with partners — two of whom were identified and secured as part of the engagement. Alongside the survey, user interviews were conducted with 10 individuals to understand how they experienced the app flow.
The first round of findings led to a complete redesign of the onboarding — moving from a 3D experience to a 2D questionnaire designed as a journey along a road, giving users a sense of progression. The updated version was then tested in a second round to validate the changes.
The results
Users spent three times longer engaging with the 2D version than the 3D version. Engagement times and positive user reviews both confirmed that the simpler, more intuitive 2D experience was significantly more engaging.
The research led directly to the recommendation to drop the metaverse entirely and focus on the core 2D experience. The metaverse was a costly undertaking and the foundational experience hadn’t been validated yet. The data made that case clearly. This ultimately produced two new versions of the app.
You showed us we were building far too much — users couldn’t engage with any of it until we had a really strong core to build on.
Users spent 3× longer in the 2D experience. The metaverse wasn’t the future of the product — it was the distraction.
The challenge
Nordic Life had a strong customer base but their email marketing wasn’t performing. Existing customers weren’t repurchasing and new newsletter subscribers weren’t converting. Open rates and click-through rates were below industry standard, but they didn’t know why. They knew something was wrong with their marketing — they just couldn’t see what.
The solution
The engagement began with an analysis of existing campaign data from Mailchimp to understand where engagement was dropping off. From that analysis, A/B tests were developed grounded in behavioural research.
Two main interventions were applied. First, the email layout was restructured based on visual hierarchy principles — ensuring the most important content and calls to action sat where the eye naturally goes. Second, the picture superiority effect was applied: the founder began including more stories and human faces in emails. Research consistently shows that stories and faces are more memorable and more likely to drive action than product-only content.
The results
The revised emails delivered a 12% increase in click-through rate, alongside improvements in open rates and overall engagement. For a brand with an already loyal but disengaged audience, the shift wasn’t about reaching new people — it was about making the existing relationship more active.
Annabel looked at our email data and showed us exactly why people weren’t buying. Three weeks later our click-through rate was up 12%.
The product wasn’t the problem. The emails just weren’t talking to people the way the brand talks to people.
The challenge
Researchers in the trauma and adversity field were struggling to find work relevant to their own — and in some cases were duplicating research that already existed, simply because they hadn’t found it. The same concepts were indexed under completely different terminology across major academic databases. A researcher searching for “Adverse Childhood Experiences” on PubMed might miss thousands of papers indexed under “Early Life Stress” or “Childhood Maltreatment” on Google Scholar.
There were roughly 20 different terms in active use across the field, and nobody had mapped how they related to each other.
The solution
Nineteen key terms were mapped across four major academic databases — Google Scholar, PubMed, PsycINFO, and ERIC. For each term, the number of results was recorded, then the overlap between term pairs was calculated to understand how much research a scholar would miss by using only one term.
This produced a matrix showing exactly where terminology diverged across databases and how much unique research sat behind each term. The mapping was then built into ANSResearch’s own database as a related terms feature, allowing researchers to see results for connected terminology they might not have thought to search for.
The results
The mapping revealed that some term pairs had zero overlap — meaning a researcher using one term would miss 100% of the work indexed under the other, despite both terms describing closely related phenomena. This demonstrated both the vastness of the research field and how fragmented it had become.
The related terms feature gave a team of around 30 researchers, including collaborators at Harvard, a way to search more comprehensively without needing to know every variant term in advance. It directly reduced the risk of duplicated research effort.
Annabel flagged the terminology problem and mapped the scale of it.
Some search terms had 0% overlap. Researchers were missing entire bodies of relevant work — not because it didn’t exist, but because it was filed under a different name.
The challenge
Moteefe was receiving negative customer feedback but hadn’t systematically analysed what was driving it. The volume of reviews and support emails made it difficult to see patterns without structured analysis. They needed to understand what customers were actually unhappy about and whether the problems were solvable.
The solution
Approximately 2,860 customer reviews and support emails were analysed through keyword analysis to identify recurring themes and cluster complaints. Rather than reading anecdotally, the feedback was treated as a dataset — categorising issues by frequency, severity, and which suppliers they traced back to.
The results
The core finding was that a significant proportion of negative reviews were driven by print quality issues, and these traced back to two specific suppliers. The analysis became part of the business case for dropping those suppliers and redistributing orders to a new supplier with better print quality and faster delivery times.
After the switch, customer feedback improved and delivery times shortened. The work showed that the brand perception problem wasn’t about marketing or customer service — it was a supply chain issue hiding in the review data.
The CEO subsequently attempted to rehire Annabel twice.— Moteefe
2,860 reviews pointed to the same thing: two suppliers were responsible for the majority of negative feedback. The fix wasn’t better marketing — it was a better printer.
Every study above started with one unanswered question. If you've got one of your own, that's plenty to start a conversation.
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