STRAT7 https://strat7.com Mon, 16 Mar 2026 12:56:36 +0000 en-GB hourly 1 https://wordpress.org/?v=6.9.4 https://strat7.com/wp-content/uploads/2024/12/STRAT7_favicon.png STRAT7 https://strat7.com 32 32 Chaos packaging: When breaking the rules drives growth https://strat7.com/blog/chaos-packaging/ Mon, 02 Mar 2026 13:53:28 +0000 https://strat7.com/blogs// Chaos packaging can drive growth, but it can also backfire. Discover when rule-breaking formats create standout, what makes it risky, and why culture matters.

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Stand out or blend in? In today's cluttered FMCG landscape, that's the choice facing brands in mature categories.

Chaos packaging - the use of unconventional formats that defy category norms – is proving to be more than just a visual gimmick. It's a strategic growth lever for brands willing to disrupt.

From cereal in ice cream tubs to detergent in fizzy drink bottles, STRAT7’s latest study tested a range of provocative non-prototypical packaging ideas across different categories in the US, UK, and Argentina. The findings reveal when chaos creates opportunity – and when it backfires.

Our guide explores the key insights from the research and what they mean for brands looking to break through in congested, highly competitive categories. You’ll discover:

  • How chaos packaging creates shelf standout
  • Why it can be risky for brands with established equity
  • The opportunity in borrowing positive associations from other categories
  • Why cultural context matters

Ready to decode the rules of disruption?

Download the Chaos Packaging guide

See the full study findings and implications for market leaders.

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6 shifts redefining the insights function in healthcare https://strat7.com/blog/healthcare-insights-function/ Thu, 26 Feb 2026 14:49:57 +0000 https://strat7.com/blogs// We explore the trends shaping the healthcare sector and how insight teams can help organisations not just adapt, but thrive.

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At our recent STRAT7 Health roundtable in London, we were joined by leaders from across the healthcare sector to discuss the rapidly changing insights landscape.

A number of themes emerged with shared challenges and observations across organisations – but more importantly – opportunities to evolve and turn these shifts into strategic advantage.

The next STRAT7 Health roundtable will be taking place in Boston in April 2026 – if you’d be interested in attending this event, please drop us a note – [email protected]

Shift 1

From data overload to better clarity

We are drowning in more and more data, but more data does not equal an easier decision. It creates more choice – so we need even more clarity and conviction in opinion on which direction to take.

Storytelling is increasingly recognised as being vital for making insights actionable and memorable, especially as the volume and complexity of data grows.

But keeping insights alive is still challenging. Cross-functional collaboration is key to reconciling different perspectives and ensuring the narrative is owned and actionable.

Opportunity: Use storytelling and influence to cut through the noise and refocus on interpretation and bringing forward the insights that truly matter.

Shift 2

From outputs to outcomes

There is a growing pressure to demonstrate not just insight quality, but also insight value.
There is a push for insight teams to move away from legacy ‘comfort blanket’ projects and focus on initiatives that truly deliver measurable business value.

Efforts are being made to track the impact of insights from discovery through to activation, aiming to demonstrate ROI and reinforce the value of the function.

Some organisations are experimenting with behavioural contracts and closer partnerships to ensure insights are implemented and their impact is evaluated.

Opportunity: To build mechanisms and stories that attach insights directly to decisions made.

Shift 3

From AI hype to human-centered intelligence

AI is becoming a powerful accelerator surfacing historical insights, speeding up foundational work and helping teams to focus on more meaningful strategic questions.

Despite these advances, human judgment remains critical, especially when data is conflicting or when nuanced interpretation is required.

There is a need to manage internal expectations as stakeholders can often overestimate what AI can deliver. With more democratisation and access to insights, teams are also worried about outdated inputs and misinterpretation.

Human involvement in decision-making varies across the product lifecycle, with more data-driven approaches in early pipeline stages and greater human input as products near market.

Many organisations are moving towards dedicated brand / project specific agents to ensure they maintain vital context.

Opportunity: AI can do the heavy lifting, enabling more time for insight teams to do the heavy thinking – emphasising value of human judgement.

A group of hikers walking up a snowy hill as the sun sets

Shift 4

From supplier relationships to strategic partners

As internal data access continues to grow, organisations want and need more from their external partners. Agencies that can integrate multiple data sources (syndicated, primary, CI) and offer strategic synthesis are increasingly valued.

There is a growing need for external agencies to support innovation and future scanning, bringing in outside-in perspectives and expertise from other industries.

Opportunity: Amplified insights from partners who can stitch together multiple evidence streams and bring outside-industry intelligence and perspectives.

Shift 5

From specialist silos to hybrid insight teams

There is a shift toward hybrid roles that combine data science, analytics, and traditional market research skills, especially in larger organisations.

Integration of insights with forecasting and other data streams is becoming more common, requiring fluency in multiple ‘languages’ of data and business.

Team structures are evolving, with some organisations piloting product operating models that bring together insights, data science, and product teams from the outset.

The value and influence of insights teams have increased, with greater partnership roles in commercial and strategic decision-making.

Opportunity: To build unified, multidisciplinary insight engines that connect data science, analytics, forecasting, and market research

Shift 6

From a pharma only to a consumer mindset

The industry is steadily shifting toward more consumer-centric health experiences.
Patient journeys are becoming the backbone of strategy, and private/direct-to-consumer models are accelerating this change – especially in the UK.

This shift is prompting teams to invest more in future scanning and prediction to keep pace with rapidly evolving expectations.

Opportunity: Look outward, anticipate customer behaviour earlier, and adapt at speed.

Conclusion

These six shifts aren’t isolated trends – they’re interconnected signals of a function in transformation. The organisations that will thrive aren’t necessarily those with the most data or the most advanced tools, but those that use insight to drive decisions, build the right partnerships, and keep human judgement at the centre of an increasingly automated process. 

If you’re navigating any of these shifts, we’d love to continue the conversation. Get in touch with the STRAT7 Health team.

About the team

Lizzie Eckardt is UK Managing Director at STRAT7 Incite. She has worked in pharmaceutical market research and brand, marketing and strategy consulting for 20 years. Lizzie is passionate about the intersection of culture and healthcare and specialises in getting closer to lived experience of HCPs and patients. She is also an expert at making those experiences actionable and meaningful for her client’s brands. She has worked with a range of methodologies and loves to innovate and bring consumer approaches into the healthcare world.

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Navigating change in biotechnology: Q&A with STRAT7 Health team https://strat7.com/blogs/navigating-change-in-biotechnology/ Thu, 19 Feb 2026 10:48:28 +0000 https://strat7.com/blogs// We explore the trends shaping the healthcare sector and how insight teams can help organisations not just adapt, but thrive.

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The biotechnology industry is moving fast. New therapies, new players entering the landscape, new regulatory expectations and a more uncertain global environment are reshaping how organisations operate.

We sat down with Andrew Way and Marion Gannon who lead on our biotechnology work in the STRAT7 Health team and specialise in working with organisations developing products and services to support the research, development and manufacturing of biotherapeutics. We were also joined by Maria Colarusso, Head of Digital, to discuss the trends they are seeing and what it takes to stay ahead.

Andrew Way portrait

Andrew Way

Joint Head of Life Sciences Research

Marion Gannon portrait

Marion Gannon

Joint Head of Life Sciences Research

Maria Colarusso

Maria Colarusso

Head of Digital

Q: How are biotechnology organisations adapting to the current wave of geopolitical and economic uncertainty?

Andrew: It has been a turbulent few years. First COVID shut down supply chains and forced pharma and biotechs to find entirely new ways of sourcing products and equipment that would help get their products through R&D and to market. Just as things stabilised, the sector was hit by fresh geopolitical and economic pressures.

Marion: Also, drug developers and manufacturers have become far more cautious and have needed to be more adaptable. Where they once relied on a single supplier, they are now establishing expanded networks of primary and secondary suppliers across different regions and often moving them closer to manufacturing sites to reduce risk. It is challenging in the short term, but the sector has learned a lot and is better prepared than it was pre-COVID.

Andrew: Finally, as a result of all of this, budgets for market research are becoming increasingly tight, so we’re seeing that clients are asking us to help them do more with less, which means we have to look at more innovative approaches to maximising budgets and maintaining value in our offer to them.

Q: What regulatory changes are having the biggest impact on clients today?

Andrew: Regulation is the backbone of this industry, but right now the system is struggling to keep up with the science. Long established modalities such as mAbs present fewer issues but newer and emerging technologies such as Cell and Gene Therapies, mRNA, etc. are more challenging. Drug manufacturers are developing processes and protocols with greater degrees of uncertainty as to whether they will actually be approved by the regulatory bodies.

Marion: For our clients – primarily providers of equipment, consumables and systems that biopharma/biotech companies use to develop and manufacture therapeutics – they are having to adapt and become regulatory experts themselves by demonstrating their ability to work with the regulatory bodies and provide proof that the solutions they offer will pass muster. Their clients are often looking for pre-validated equipment and data that will help speed up the approval process, rather than having to evolve compliance and guidelines at the same pace as, or even faster than, the regulators themselves!

Q: How are your clients adjusting to the rise of new entrants or start-ups?

Marion: The landscape is changing, but clients are not necessarily treating newer and smaller players as threats. Instead, they are looking at how to help them flourish. Growth in this space depends on a healthy pipeline of new technologies, so we are seeing more collaborations, more partnerships and more creative funding approaches.

Andrew: We’ve also seen increased interest in outsourcing elements, particularly for smaller organisations. With the current economic climate, few are in a position to invest in GMP facilities, so science parks or flexible facilities that early-stage companies can rent rather than build and/or delay investment, hold great attraction.

Marion: We’ve also found that our clients are looking at innovative ways to support smaller organisations. Some are offering loans or designing novel ways to support biotechs and academic spin outs in the development and commercialisation of new products. Strategic partnerships and M&A activity are also more common as equipment and solutions manufacturers look to acquire or align with emerging technologies early.

Colleagues in discussion in an office

Q: How is AI changing the way insights, analytics and research are delivered?

Andrew: AI is absolutely reshaping all of the work we do in health, and we have adapted in response to this. For example, we’ve established Nucleus, our AI hub at STRAT7, which serves as a centre of excellence embedded across everything we do. By integrating advanced AI capabilities with expert human insight at every stage, we’re able to deliver projects faster – without ever compromising on the quality and strategic thinking our clients expect.

Maria: A specific example is using our online platform, Whycatcher, which allows us to analyse confidential/client data according to current data protection regulations and can handle labour-intensive tasks like reviewing large volumes of unstructured data, identifying quotes or themes and highlighting patterns with much greater efficiency than before.

Andrew: Which means that we can then focus more time on interpretation of the data and bringing valuable insights to our clients, rather than on manual processing.

Maria: We also use Maya, our AI moderator, for natural conversations in digital research where respondents are capturing and reporting on their own experiences – and Maya can ask questions to get a deeper understanding of those moments. This works great in ethnographic contexts or where we’re trying to understand complex decision-making journeys.

Andrew: We are also applying AI at quantitative scale, where we can also analyse emotional tone, sentiment and behavioural drivers across large samples, which until recently has typically been the preserve of qualitative projects.

Marion: And just to add, we’ve been hearing that those organisations working in early stage R&D are using AI to refine processes, compare molecule performance and model which of their prototypes offer the greatest hit to lead opportunities, and therefore help guide allocation of their already depleted R&D funds. There is some worry that this approach may mean loss of opportunity as AI may reject some potentially good ideas. Engineers and systems people are excited by the prospect of AI, ML and to a lesser extent Quantum in the future, and it’s still early days for much of this, but overall, assuming tools are used responsibly, they present greater opportunities than threats.

Q: What are the key ingredients to delivering actionable insights, not just data?

Andrew: Actionable insight now relies on triangulation; one data source is almost never enough, especially for high-stakes decisions. Where possible, we bring together client CRM data, sales data, market size, primary research and other sources to create a clearer, more robust picture.

Marion: Furthermore, what gives us a major advantage, is that we can bring in the right experts from across the group, combine methodologies and build integrated solutions that draw on multiple perspectives. Our clients appreciate having a single point of contact while benefiting from the depth of an entire ecosystem.

Q: What motivates you personally about working in the biotechnology sector?

Marion: I think everyone in the team would say the same thing – impact! You talk with people who are developing treatments that genuinely improve or save lives. You talk to scientists who are pushing the boundaries of what is medically possible. You see things in the news and think, “I helped with that.”

Andrew: There is also something special about the people in this sector. They are mission-driven, collaborative and genuinely passionate about improving health. The job we do as researchers does not have the global impact of these people, but I think we share their passion and drive to learn about the sector and the work that is being done and want to play a part in that. The work is demanding at times, but incredibly rewarding.

STRAT7 Health is a specialist team consolidated from across the group with expertise in the Pharma and Healthcare industries.

We bring the foresight, strategic clarity and bold thinking that turn complexity into competitive advantage.

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AI anxiety paradox: Why insight teams are right to be cautious https://strat7.com/blog/ai-anxiety-paradox-insight-teams-cautious/ Wed, 18 Feb 2026 11:59:33 +0000 https://strat7.com/blogs// We explore the trends shaping the healthcare sector and how insight teams can help organisations not just adapt, but thrive.

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Insight teams are under more pressure than ever. The board wants AI to accelerate speed to insight. Finance wants it to cut costs. Everyone wants more impact, and they want it now.

Under pressure

I’ve spoken with a lot of insight leaders, and most of them will tell you the same thing – they feel squeezed. They’re being handed tools they don’t fully understand and asked to stake their professional credibility on the outputs.

While AI vendors promise faster, cheaper, better research, the people who actually understand research methodology are asking questions. What data trained these tools? How do we know the outputs are accurate? What happens when we get it wrong?

That anxiety is well-founded. Insight leaders who feel it aren’t resisting change. They’re being cautious about tools that could fundamentally undermine the decisions they’re paid to inform.

The 5% problem

Take synthetic respondents. AI-generated survey responses can simulate thousands of consumers every hour, at a fraction of the cost of recruiting real people. Some providers claim 95% accuracy compared to traditional research.

Here’s what that pitch misses… all you need is 5% inaccurate data to make a wrong decision. If you’re building a global segmentation that will shape your marketing strategy, product development, and commercial priorities for the next three years, 95% accuracy isn’t good enough. The 5% you got wrong might be the segment that matters most. Miss a critical insight. Misread a behavioural driver. Launch the wrong product into the wrong market. The cost of that mistake isn’t measured in research budgets. It’s measured in millions of pounds of wasted investment.

Opaque tools

Synthetic data is the most visible example of AI risk in research, but it’s not the only one. Many AI tools operate as black boxes. They take inputs, produce outputs, and what happens in between stays unclear. What data sources are being used? How are models trained? What biases are baked in? For most tools, the honest answer is: we don’t really know.

This puts insight leaders in a bind. They’re being asked to champion AI adoption while unable to explain how the tools actually work. They’re being asked to stake their credibility on outputs they can’t fully interrogate. Feedback we’ve heard consistently is that this opacity creates genuine anxiety, not because people resist technology, but because their job is to provide reliable intelligence. And you can’t do that if you can’t trust your tools.

Looking up a circular staircase

The human element

For years, the best insight work has been built on trusted advisor relationships. Researchers who understand the business context, know the history, and can interpret findings through the lens of what the organisation actually needs.

Many AI tools promise to skip that step. Plug in your brief, get your insights, skip the expensive consultants. But in doing so, they skip the judgment, context, and pattern recognition that makes insight actually useful. Businesses get outputs faster, but lose the human intelligence that turns those outputs into decisions. They save money on the research, then waste it on the wrong strategy.

A principles-based response

So what’s the alternative? Reject AI entirely and stick with legacy methods? That’s not realistic, and it’s not smart. The efficiency gains are real, and the possibilities are genuine. Ignoring AI isn’t caution, it’s denial.

The better path is a principles-based approach that captures AI’s benefits while maintaining the rigour and human oversight that good research requires.

At STRAT7, we’ve developed six principles that guide how we build and deploy AI across our work. These emerged from extensive conversations with insight leaders facing exactly these pressures, and they shape everything we do.

Humans in control

AI handles the heavy lifting. Our people handle the heavy thinking. Every output gets human oversight before it shapes a recommendation.

Purpose-led adoption

We don’t use AI for everything. We use it where it delivers tangible, high-ROI value. That means being selective, not performative.

Governance and trust

We’re transparent about what data sources we use, how we test our AI agents, and what they can and can’t do. No black boxes.

Bias awareness

Large language models are trained on data that carries cultural biases and assumptions. We actively work to understand and counteract those biases in our outputs.

Continuous improvement

Our AI tools aren’t finished products. We constantly refine them based on client feedback and evolving best practice.

Transparency over salesmanship

If AI won’t work for a particular brief, we say so. If there are trade-offs, we name them. The trusted advisor relationship matters more than the technology sale.

These principles aren’t just internal guidelines. They’re commitments we make to clients who need to trust that the intelligence they’re receiving is reliable.

The real question

The conversation about AI in research has focused on efficiency metrics. How much faster? How much cheaper? How many more data points?

Those questions matter, but they’re not the most important ones.

The most important question is: can you trust the output enough to bet your strategy on it?

For synthetic data and black-box tools, the honest answer is often no. The accuracy isn’t proven, the methods aren’t transparent, and the biases aren’t understood.

For AI that’s built on sound principles, with human oversight, transparent methods, and a clear understanding of its limitations, the answer can be yes. Not blind trust, but earned confidence based on rigorous process.

Insight teams feeling anxious about AI aren’t being difficult. They’re doing their job. The challenge now is to channel that healthy scepticism into demanding better. Better tools, better transparency, better integration of human judgment.

Because the goal was never to do research faster. It was to make better decisions. And that requires intelligence you can actually trust.

Accelerate your insights using Nucleus, our AI hub of proprietary agents working alongside our consultants to speed up insight delivery and unlock new possibilities.

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Elevating your healthcare strategy through cultural intelligence https://strat7.com/blog/healthcare-cultural-intelligence/ Wed, 11 Feb 2026 08:44:33 +0000 https://strat7.com/blogs// STRAT7 explores the trends shaping the healthcare sector and how insight teams can help organisations not just adapt, but thrive.

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Culture isn’t optional. It frames how people see and experience the world – and how they see and experience brands.

Businesses and organisations exist within it by default, not by choice. In this environment, cultural relevance has become a defining marker of brand strength and long-term potential.

Life sciences companies spend an average of 12-14 years developing assets. Investing a lot of time entirely in the asset itself supported by a rigorous and necessary development process. This rigor has historically meant that the life sciences industry has leaned into being ‘product first’.

However, this is changing! What has been missing is an understanding of the cultural context your product is launching into or exists within. How are patient expectations evolving? What external forces are shaping HCP prescribing behaviour? Which therapeutic categories face disruption?

The GLP-1 market explosion looked sudden. But the cultural signals were already there. Proactive health optimisation, biohacking curiosity, and bottom-up weight management communities were all taking shape and evolving for years. The companies that attuned early to these signals, such as Hims & Hers, were already positioned when the market broke open.

Formation, Skydiving

Why the life sciences industry isn’t doing enough

It’s not that life sciences businesses are unaware of cultural shifts. Most teams recognise that customers and consumers behave differently, acknowledging that the dynamics and expectations are always evolving. However, this awareness rarely travels beyond the brand.

Segmentations and personas are still largely treatment- or disease-led, leaving cultural context underexamined.
The challenge is translating cultural awareness into action while also looking ahead to the future. To increase their cultural relevance, life sciences businesses need to take more proactive steps to adapt rather than just reacting to situations as they evolve. This is especially important given how stringent this sector is and how long things take to develop.

The question isn’t whether cultural shifts are happening. It’s whether your organisation has a way to identify which shifts matter for your therapeutic areas and customers you are engaging with, when they’ll impact your brands, and what strategic decisions should change as a result.

What this looks like in practice

We recently ran a culture workshop with a top 10 global pharmaceutical company, bringing together teams from different areas within immunology to explore how cultural trends could be reshaping their markets.

We spent half a day exploring fast and slow culture and how some of the dominant and emergent trends have the potential to impact their strategy.

During the session we helped the team:

  • Develop a deeper understanding of their evolving customer needs
  • Be inspired by examples from different sectors outside of healthcare
  • Identify cultural opportunities within immunology.

One participant called it ‘a chance to blow things up, talk blue sky.’ Another noted ‘Pharma is so regulated, it’s different from consumer – so it’s really useful to challenge our thinking this way.

We covered a broad spectrum of ideas, including packaging design changes, patient education material revisions, HCP engagement approaches – and even created a pitch document to sell in these ideas to their CEO.

“The workshop was a pivotal moment for our team—it got us thinking differently and considering cultural shifts that we simply hadn’t thought about before. In pharma, we’re often so focused on the brand level that we can miss the broader societal currents shaping how patients and healthcare providers engage with the world. This session challenged us to step back, look at the bigger picture, and ask ourselves: how do these cultural trends influence not just what we communicate, but how we connect? It’s a mindset shift that will inform our strategy for years to come.”

Nicola Bailey, Director Market Research Immunology, Sanofi

The trends shaping the healthcare sector

Here’s a snapshot of the cultural trends that we focused on:

Health is no longer episodic

Health is becoming something to work towards vs thinking of it episodically. Health is a broader holistic concern with consumers and HCPs incorporating their own personal data into their health routines and even consultations.

What could this mean? Treatment benefits need to show up in how consumers measure and manage their daily health and align with more holistic approaches.

Consumers are increasingly more curious

With so much access to information, consumers are becoming more curious, creating a need for more information including the more complex scientific aspects.

What could this mean? Shared decision-making isn’t optional anymore – it’s what everyone expects. Are we developing materials and content that consumers can more easily engage with and supporting HCPs with the dialogue?

Trust comes from the crowd now

Healthcare professionals and consumers alike are relying increasingly on crowdsourced information. We find reliability in the masses and are being presented daily with social health cures that are harder for healthcare businesses to control.

What could this mean? Horizontal trust networks mean your messaging competes with peer experiences and crowdsourced information – including misinformation. Your communication strategy needs to account for how consumers and HCPs discover, validate, and share treatment information, not just how you wish they would.

Personalisation is now the baseline

Consumers expect personalisation as standard. They learn from others with the same condition, age, and lifestyle. HCPs also demand and expect more nuanced data grounded in patient types.

What could this mean? Generic education materials and one-size-fits-all treatment approaches feel outdated, especially when personalisation is part of everyday life and consumers are taking control of this with or without permission..

What becomes possible with cultural foresight?

Culture-aware healthcare companies can make better strategic choices by design and thus get ahead of the curve.

Examples include:

Portfolio strategy: Aligning assets to where customer expectations are moving

Launch positioning: Positioning strategies that reflect how beliefs are shifting and don’t feel outdated or too inward looking

Commercial execution: Content, messages, packaging, support materials are all designed around emerging expectations.

The companies paying attention now are positioning for advantage.

Work with us

We run culture workshops for pharmaceutical, biotech, and medtech companies. Cross-functional teams explore how cultural trends are reshaping their therapeutic areas and leave with implementation plans – not just insights reports.

Curious what cultural trends are affecting your therapy area? Get in touch to discuss a workshop for your teams.

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What insight leaders really need from AI: Q&A with our COO https://strat7.com/blog/what-insight-leaders-really-need-from-ai/ Tue, 10 Feb 2026 07:47:00 +0000 https://strat7.com/blogs// A conversation with our COO Jonathan Clough on why we built STRAT7 Nucleus, what makes our AI approach different, and what clients are saying about AI-augmented research.

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Insight teams are under intense pressure to adopt AI - but figuring out where it genuinely adds value (and where it doesn't) is the real challenge.

We sat down with our COO, Jonathan Clough, to get his take. As one of the architects behind STRAT7 Nucleus, he’s spent months working with clients and insight leaders, helping teams put AI into practice.

The conversation covers synthetic data risks, why human oversight still matters and entirely new ways of approaching research.

Q. The pressure on insight teams to demonstrate AI value is intense right now. Why did STRAT7 decide to build Nucleus?

Insight teams are under real pressure to show how AI can accelerate speed to insight, reduce costs, and increase impact. But a lot of tools entering the market research space are opaque and unproven. They create anxiety rather than confidence.

Jonathan Clough

Jonathan Clough,
COO, STRAT7

Take synthetic respondents. The pitch sounds great: cost-free AI-generated survey responses that can run thousands of surveys every hour. But when you look at the methods, even providers claiming 95% accuracy are admitting that 5% might be wrong. If you’re building a global segmentation or making a major product decision, 5% wrong means millions in wasted investment.

The feedback from insight leaders was clear: they need to use AI where it makes sense, but they also need human oversight and a trusted advisor to help them work out what works and what doesn’t. That’s what Nucleus does – it’s our proprietary AI hub, used exclusively by STRAT7 consultants giving our clients access to our capabilities with greater speed, precision, and impact.

woman interviewing man

Q. There are a lot of AI tools in the market. What makes STRAT7’s approach different?

We started by agreeing principles rather than jumping straight to building tools. We worked extensively with clients to understand what they actually needed, and that shaped six principles guiding everything we do.

One of those principles is ‘humans stay in control’. AI does the heavy lifting, our people do the heavy thinking. Every output gets human oversight.

We’re deliberate about purpose-led adoption. We don’t use AI for everything. We use it where it delivers genuine value and we’re transparent about what we’re using, how it works, and what its limitations are. If AI won’t work for a brief, we say so. The trusted advisor relationship matters more than selling AI.

That’s fundamentally different from vendors pushing tools. We’re integrating AI into consultancy to make the consultancy better, not replace it.

Q. This is the question everyone asks: will AI replace research teams?

No. And we don’t want it to.

Our philosophy is human intelligence augmented by AI. We’re investing in training across the company to develop what we’re calling AI-certified consultants. People who understand how to prompt agents effectively, how to evaluate outputs critically, and how to get real value from the tools.

The shift is that consultants become orchestrators. They control what the AI does, interpret what it produces, and add the context only a human can provide. The client relationship, the business strategy, what’s been tried before. AI can’t know any of that. Only a consultant who’s worked with that client can bring that perspective.

So AI changes what people do, but it doesn’t replace them. It frees them to focus on the thinking and the impact rather than the processing.

Q. What can you do now with Nucleus that wasn’t possible before?

The efficiency gains are real, but perhaps the more interesting piece is innovation.

When you remove the constraints of manual processing, you can challenge methodologies that weren’t feasible before. If you can have an agent generate descriptions of 40,000 products in real time and layer them against consumer need states, you approach product optimisation completely differently. That analysis would have been prohibitively time-consuming six months ago.

We’re developing new approaches to briefs that simply couldn’t have been done before. That’s genuinely exciting because we’re not just doing the same work faster. We’re doing different work.

Q. What’s surprised you most about client reactions to Nucleus?

A mixture of relief and excitement!

There’s been so much noise about AI and so many vendors making big claims that clients have been understandably sceptical. What we’re hearing is that our approach feels reassuring because it’s not about a single tool promising to solve everything. It’s the trusted relationship they already have with us, plus confidence that we’re using AI thoughtfully.

We’ve had clients come back with completely different kinds of briefs because they now see what’s possible. We’re also seeing growth in new clients specifically coming to us because of how we embed AI into consultancy rather than treating it as a bolt-on.

The reaction has been more positive than I expected. People are ready for this. They just needed a partner they could trust to do it properly.

Want to find out how STRAT7’s AI capabilities could support your insight and research needs? Get in touch to start a conversation.

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Navigating change in healthcare: Q&A with STRAT7 Health leads https://strat7.com/blog/strat7-health-leads-qa/ Mon, 08 Dec 2025 10:46:51 +0000 https://strat7.com/blogs// We explore the trends shaping the healthcare sector and how insight teams can help organisations not just adapt, but thrive.

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Healthcare and pharma are undergoing profound transformation - from the consumerisation of healthcare to rapid advances in data, technology, and AI.

With the recent launch of STRAT7 Health – our dedicated life sciences team – we sat down with healthcare leads, Lizzie Eckardt and Hannah Potter, to explore the trends shaping the sector and how insight teams can help organisations not just adapt, but thrive. 

Portrait of Lizzie Eckardt

Lizzie Eckardt

STRAT7 Health Lead

Portrait of Hannah Potter

Hannah Potter

STRAT7 Health Lead

Q: Change feels especially intense in healthcare right now. What’s driving that, and how are clients responding?

Hannah: “Change has always been part of life, but right now it feels particularly tumultuous. There are geopolitical pressures, cultural shifts, and rapid advances in healthcare innovation — all happening at once. 

What we’re seeing is that pharma clients need partners who understand their broader journey, not just the next project. That’s why we focus on relationships rather than transactions. Client needs are evolving quickly, and we have to flex with them.”

Q: With everything moving so fast, is there a risk of becoming too short-term in our thinking?

Lizzie: “Yes – and it’s a real concern. Across industries, decisions are becoming more reactive. Healthcare traditionally hasn’t worked that way, so it’s a big adjustment. 

The challenge now is balancing the short-term need to respond quickly with long-term strategic thinking. Trends matter. Future scenarios matter. And we have to help clients do both.”

Q: Pharma has traditionally been inward-facing. Are you seeing that shift?

Lizzie:Definitely. Historically, because drug development is long and complex, pharma companies focused heavily on their brand and science. But expectations have changed. 

Patients, caregivers, and HCPs are behaving more like consumers. They expect better experiences, more information, and more transparency. And more mainstream conversations — like women’s health, migraine, mental health — are shaping what people expect from their broader healthcare needs. 

Pharma companies are having to look outward at what’s happening culturally, not just clinically. That’s a big shift.”

middle aged couple exercising chatting

Q: You mentioned that patients, caregivers and HCPs are behaving more like consumers. Can you elaborate on how that’s influencing pharma today?

Hannah: “Consumerisation is huge. We’re seeing services advertise directly to patients — things like digital weight-loss programmes offering GLP-1 treatments. People now come to their doctors informed (and sometimes misinformed) and asking for specific solutions. 

Tools like online medical records mean patients often see their results before their clinicians do. They’re more engaged, which is great, but it creates new challenges around communication, misinformation, and expectations. 

Pharma now has to speak to HCPs, consumers, and caregivers — all at once — and create a clear, coherent and complementary narratives for each.”

Q: How important is it for pharma to understand the full ecosystem of people influencing the healthcare journey?

Hannah: When we started our careers, it wasn’t unusual for projects to adopt a narrow lens — one client, one audience, one perspective. That limits the impact of research. 

Now, our best work comes from involving the full landscape: patients, specialists, GPs, nurses, pharmacists, carers — anyone who influences or experiences the journey. 

Including an integrated point of view based on stakeholders from across the ecosystem massively improves outcomes. And clients increasingly expect this.”

Q: Data and insights seems to be playing a bigger role than ever in healthcare decision-making. How is that changing insight work?

Lizzie: Robust, accessible data has become absolutely central to decision-making. It’s always mattered, but today stakeholders expect immediacy and insights at their fingertips, not months down the line.

Data also no longer sits in silos. Competitor intelligence, primary research, secondary sources, and market access workstreams increasingly need to operate in harmony. When these elements are integrated, they unlock richer insights and more holistic growth strategies.”

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Q: AI and technology is impacting all industries. How are you ensuring you are staying ahead and leveraging them in the right way in healthcare?

Lizzie: “Our approach is focused on ensuring we respond to the disruption from AI in a considered and sustainable way. 

AI has the potential to address many of the challenges our clients are facingExamples include getting to insights at speed, being able to quickly summarise all of the data held, but more recently we have been using it as a way to drive creativity and innovation (often an area that is underplayed with AI). 

Our agentic hub and technology toolkit has been a real game changer for us and helps automates workflows, drive innovation and reveal hidden insights. This is woven in to everything we do and not just something we add on to projects, but we are very mindful of what it can do and are honest to call out what it can’t.  

Q: Can you give some examples of the types of AI tools you are using?

Hannah: We are using WhatsApp-based approaches which allow respondents to capture real, in-the-moment experiences — symptoms, treatment administration, HCP interactions, day-to-day realities. It brings the human side of healthcare to life in a way that feels natural and integrated into daily routines, but can leverage AI to get the most our of responses and ensure we have the depth we need.”

Lizzie: “We’re also excited about our segmentation chatbot. It takes all the great work from segmentation studies and turns it into something teams can use every day. Stakeholders can ask a question about a segment and get an immediate response. It’s making segmentation more commercial, actionable, and alive.”

Q: STRAT7 is now a larger group with more capabilities. How is that helping your healthcare clients?

Lizzie: Being part of the wider STRAT7 group is a huge advantage. We can now do far more with our clients — whether that’s accessing new types of data, applying innovative methodologies, or tapping into strategic, analytics, and AI expertise from other agencies in the group. 

This strengthens relationships because we’re not limited to the traditional healthcare research toolkit. We can stretch further and offer solutions that weren’t possible even a few years ago.”

Q: What makes this work exciting for you personally?

Hannah: The variety. Healthcare is endlessly interesting — there’s always something new to learn. We’ve worked across countless therapy areas, conditions, and patient experiences. 

But it’s also the wider market trends — new tech, new expectations, new cultural forces — that keep things fresh. As these evolve, so do the types of research we can do. It pushes us to be innovative. 

And there’s an intellectual challenge too: combining science, commercial strategy, creativity, and human understanding. Every project brings a new puzzle.”

Q: Finally — what keeps you motivated?

Hannah: Relationships. We have clients we’ve worked with for eight to ten years. We’ve been part of their journey, through restructures, brand launches, changing priorities, new challenges. 

What motivates us is knowing we’re not just delivering projects — we’re helping people navigate constant change, make better decisions, and ultimately create better healthcare experiences. That’s incredibly rewarding.”

STRAT7 Health is a specialist team consolidated from across the group with expertise in the Pharma and Healthcare industries.

We bring the foresight, strategic clarity and bold thinking that turn complexity into competitive advantage.

About the team

Lizzie Eckardt is UK Managing Director at STRAT7 Incite. She has worked in pharmaceutical market research and brand, marketing and strategy consulting for 20 years. Lizzie is passionate about the intersection of culture and healthcare and specialises in getting closer to lived experience of HCPs and patients. She is also an expert at making those experiences actionable and meaningful for her client’s brands. She has worked with a range of methodologies and loves to innovate and bring consumer approaches into the healthcare world.

Hannah Potter is UK Head of Health at STRAT7 Incite. She’s a creative, strategic insights professional with a focus on the healthcare sector. Hannah has 18 years’ experience encompassing prescription pharmaceuticals, digital therapeutics, consumer health and third sector organisations. She is passionate about ensuring that insights are embedded with strategy development, and enjoys helping clients to navigate increasingly complex and changeable environments to drive growth to their brands.

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Topic modelling in market research: How to unlock insights from unstructured data  https://strat7.com/blog/topic-modelling-market-research/ Thu, 20 Nov 2025 10:43:03 +0000 https://strat7.com/blogs// Learn how STRAT7 Bonamy Finch uses topic modelling and unstructured data analysis to uncover real consumer insights and support smarter business decisions.

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Research teams are sitting on goldmines of unstructured consumer data – social media posts, product reviews, forum discussions – but struggle to extract meaningful insights at scale.

Traditional qual and quant research methods provide unmatched depth, but require significant time and resources. Out-of-the-box generative AI tools can’t reliably quantify or size topics. Topic modelling bridges this gap, combining analytical rigour with speed and efficiency.

This blog explains what analysing unstructured data can do for your strategy, our powerful five-step topic modelling process for understanding real consumer behaviour, and when topic modelling is the ideal approach for your research needs. You’ll also learn how our toolkit differs from other AI-based methods and generative AI tools.

Illustration of a lightbulb showing generarive AI, Unstructured data at scale and human consultancy and storytelling

How does topic modelling work in market research?

Topic modelling is a technique that identifies key themes within large datasets of unstructured content – such as social media posts, product reviews, forum discussions, articles and images – and labels that data accordingly. Most relevant online data is unstructured, which makes it particularly challenging to work with. Traditionally, topic modelling was used to mine text, but with current computer vision algorithms, we can now apply it to images too.

In market research, this means transforming millions of consumer conversations into structured, quantifiable insights that directly inform business strategy.

Core benefits of analysing unstructured data with our toolkit:

  • Access at scale – Find and assess billions of real, public online conversations through APIs (Application Programming Interfaces – the systems that let different software communicate) and proprietary scrapers, in almost any country and language.
  • Speed and cost efficiency – Gain valuable insights in just a few weeks, and for a relatively low cost.
  • Objective quantification – Measure and size topics in ways that can’t be achieved with Generative AI assistant tools (e.g. Deep Researcher Agents) alone.
  • Real consumer language – Draw a more accurate picture of consumer journeys by examining what buyers post before and after major purchase decisions.
  • Hypothesis testing – Test assumptions and build category understanding before (or instead of) committing to primary research.

Our 5-step topic modelling process

Topic modelling process diagram showing raw unstructured data being processed into topics

At STRAT7 Bonamy Finch, our toolkit has been honed from our experience over the past 5 years and offers a clear step by step process which provides powerful insights quickly.

STEP 1: Defining the right business questions

In the kickoff phase, we work with clients to break down broader business questions into several focused, testable hypotheses that can be answered by the data.

The questions best suited to topic modelling can be answered by sizing topics and tracking changes over time. For example, “Why have our sales changed by X amount over the past three years?” would be broken down into testable hypotheses like:

“The share of voice for our brand has changed significantly in this period.”
“The perceived importance of customer service has increased in this period.”

Often these are hypotheses clients already have and want to test, but we can also suggest new ones based on our analysis of the data.

STEP 2: Collecting high-quality unstructured data

Illustration of various high-quality unstructured data sources

The next step of the process involves collecting the unstructured data at scale, usually via scraping the web. It’s crucial to get this step right, as with any statistical technique, the “rubbish in, rubbish out” rule applies here as well.

Unstructured data is both vast (the entire public internet), and hugely variable in quality. We prioritise working with clients to identify the most relevant data sources for their business question. We can build bespoke web scrapers to target these sources, like specialist websites that aren’t covered by social listening platforms. We can also specify extra constraints on the data collection, like which competitor brands and what markets to collect data on, what types of data (e.g. forum posts, social media, news articles, and so on) are in scope, etc.

STEP 3: Processing data for accuracy and insight

We carefully clean and process the data we collect to ensure only relevant, high-quality verbatims (the raw text from consumer posts and comments) get analysed.

This means removing duplicate posts, low-quality content, advertisements, and posts from brand accounts or businesses (where applicable). We also break down longer pieces of content at paragraph or sentence level, so topics can be assigned more precisely.

Finally, we run the data through our in-house sentiment and emotion detection algorithms. This tags each data point with a sentiment label (Positive, Negative or Neutral) and an emotion label (one of nine emotions based on Plutchik’s emotion framework, see diagram). These layers of analysis help us understand not just what people are talking about, but how they feel about it.

Emotion detection diagram

STEP 4: Building transparent and replicable topic models

We use a combination of traditional text analytics and state-of-the-art methods to precisely identify the key topics of conversation. This includes word embedding (a way of representing words as numbers that capture their meaning and relationships) and LLM-based techniques.

Once we’ve finalised the list of topics, we create the topic models. These are typically codified as rule-based algorithms using specific words and subwords.

This approach has two major advantages:

  1. The topic definitions are fully transparent
  2. The assignment is rule-based, so we can run the same model on updated datasets to provide comparable results over time. This is particularly valuable for AI-based trackers, where we monitor how topics evolve with each new wave of data.

STEP 5: Turning insights into actionable strategies

By this stage, the unstructured online data has been transformed into a high-quality, structured dataset suitable for undergoing quantitative analysis. We connect key metrics – such as share of voice or topic penetration – directly to the client’s strategic objectives.

We work with experts across STRAT7’s ecosystem to integrate our findings with primary research data and desk research. The result is a clear, actionable report that directly answers the “So what?” question. The analysis and reporting is fully bespoke and done by humans. Currently no AI can replace the rigorous thinking and years of experience required to turn raw data into strategic recommendations.

Woman working in an office

When topic modelling isn’t the right approach

Topic modelling is a powerful technique for quantifying and structuring online data. However, it’s not suitable for every business question about online data. When there’s no need to size topics, or when questions are more qualitative in nature, other tools may be more appropriate.

For quick insights without quantitative analysis, deep research tools launched earlier this year (like Perplexity or Open AI’s Deep Researcher) can be useful. We’ve also launched our own versions of these tools at STRAT7 (including one called Crowd Tracks), as we recognised that there’s a need for a variety of approaches to answer different use cases.

Finally, conventional social listening tools can be more appropriate for setting up self-serve dashboards with simple analysis capabilities.

STRAT7 Bonamy Finch’s toolkit:
Topic modelling vs other AI methods

Different tools excel at different tasks. While conventional social listening platforms are ideal for daily brand monitoring and self-serve dashboards, and deep research tools offer quick qualitative insights, our topic modelling toolkit is purpose-built for quantitative analysis and strategic depth.

What sets our toolkit apart? We combine comprehensive data access with rigorous cleaning processes, advanced analytical techniques (including emotion detection and image analytics), and most importantly, expert consulting to translate findings into strategies aligned with your core business objectives. Where other tools stop at identifying themes, we quantify them, track them over time, and connect them directly to actionable recommendations.

How to get started with topic modelling

At STRAT7, our AI design principles put business problems first. We identify the challenge you’re facing, then deploy the right toolkit to solve it. Topic modelling is a flexible and powerful tool for many research projects, but whether it’s the right fit depends entirely on your specific requirements.

See how we’ve helped our clients address their challenges. View our client work here.

If you’d like to explore how this toolkit can help your business, please contact us.

Author

Mihaela Smilova
Senior Data Scientist
STRAT7 Bonamy Finch

Mihaela joined STRAT7 in early 2024, after completing a PhD in computational biochemistry and a brief stint as a data engineer. As a data scientist at STRAT7 Bonamy Finch, she works on applying machine learning and AI tools to answer market research questions, with a focus on unstructured data.

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Four reasons storytelling matters in consumer research https://strat7.com/blogs/reasons-storytelling-matters-consumer-research/ Tue, 11 Nov 2025 15:56:43 +0000 https://strat7.com/blogs// We’ve seen storytelling unlock breakthroughs in product development, marketing, and customer experience. Here are four reasons your team should lean into narrative when sharing consumer research.

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Insights alone don’t move the needle – stories do. 

When findings are woven into a compelling narrative, stakeholders can feel what consumers feel. This emotional connection turns passive listeners into active advocates. 

By structuring your insights as a story – with vivid examples, clear arcs, and a purposeful resolution – your work has staying power. 

Over years of partnering with brands across industries and geographies, we’ve seen storytelling unlock breakthroughs in product development, marketing, and customer experience. Below are four reasons your team should lean into narrative when sharing consumer research. 

1. It builds empathy

A great deliverable(s) inspires empathy among stakeholders for the consumers being discussed. 

Inspiring empathy isn’t as simple as including a few quotes in your report – it’s building your report around a framework that illustrates how consumers think, including great but short quotes and video clips in the right places, thinking out of the box on deliverables (so you might create a video summary or journey map), and facilitating activation. 

In-person fieldwork and a video summary are like a power couple – you can capture more subtleties and build stronger empathy. 

2. It increases understanding and recall

Structure – a problem, a tension, and a solution – and frameworks (e.g. archetypes, journey map) work together to increase understanding and recall. 

Some examples: 

  • Illustrating the tumultuousness of hypertension patients’ journeys through five-stages –Repressed, Recognising, Retreating, Responding, Recommending. 
  • Matching the six types of makeup consumers with celebrities (e.g. Sophia Ritchie).
  • Comparing consumers’ credit strategy to the unpredictability of pinball.
  • Using a jazz band to explain the principles of service – for example, like tailored premium service makes you feel special, the saxophone doesn’t come in often but dominates when it does.

3. It helps with socialisation

A good simile, analogy, metaphor, archetype, persona, illustration, mnemonic device, framework, etc. makes socialisation easier and more fun. Fun especially matters – your team is more likely to share and use insights if they’re fun to talk about. 

Structure also helps with socialisation. Any good book has a beginning, a middle, and an end – reports, video summaries, etc. should too. 

Recently, we used a garden as a framework – the key insights being fruits and vegetables, and the stakeholders the pollinators. We even decorated a space for each fruit/vegetable family in our client’s conference room, sparking additional conversations for weeks. 

4. It allows you to move from 'what' to 'so what' to 'what now'

Any good story has a resolution so a report, video summary, etc. should too. A resolution will allow you to better imagine the possibilities, to decide where to go from here. 

A video we recently put together highlighting hypertension patients’ arduous journeys made physicians across the organisation reflect on their own interactions with patients. 

Why this matters to brands

  • Empathy-driven narratives help brands create products and experiences that truly resonate.
  • Memorable frameworks accelerate decision-making and inspire alignment.
  • Thoughtfully socialising insights reduces resistance and builds insights champions.
  • Action-oriented summaries deliver measurable ROI.

In the end, data and insights only go so far — it’s the stories built around them that inspire action. When research is brought to life through narrative, stakeholders don’t just understand consumer experiences, they feel them. That emotional connection fuels advocacy and drives meaningful change.

About the author

Alix Greenman has an extensive background in original research, particularly qualitative/ethnographic research. She has helped clients in a variety of industries and geographies get to know and observe their customers, understand where their industry is headed, identify whitespace, launch products, evolve their packaging, and rebrand. All the while developing deep expertise in virtual and in-person moderation, narrative development, including for videos, and hybrid and in-person workshop facilitation.

Find out how we give you a competitive edge through innovative thinking and advanced techniques. 

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The 3 pillars of segmentation deployment: Make it matter, memorable, and meaningful https://strat7.com/blogs/pillars-of-segmentation-deployment/ Mon, 13 Oct 2025 09:00:00 +0000 https://strat7.com/blogs// Creating segments is easy. Deploying them is what drives results. Discover the three pillars of successful segmentation deployment make it matter, memorable and meaningful and learn how to embed customer insights into real-world decisions that fuel growth.

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The 3 pillars of segmentation deployment: Make it matter, memorable, and meaningful

Paul Jackson
Managing Director
STRAT7 Bonamy Finch

Table of Contents

You've invested months creating the perfect customer segmentation. Your segments are insightful, your data is robust, and your strategic recommendations are sound. But here's the uncomfortable truth: most segmentations fail not because they're poorly designed, but because they're never properly deployed.

Creating segments is just the beginning – using them effectively is where the real work begins. The difference between a segmentation that transforms your business and one that gathers dust in a presentation deck lies in how well you deploy it across your organisation.

Introducing any new strategic framework takes time, effort and external support. Your teams need to think differently whilst handling the pressures of business as usual, and they need to work differently, often in ways that conflict with existing systems and processes.

But when deployment is done right, the results speak for themselves. Companies see millions in additional revenue, dramatic improvements in customer satisfaction, and sustainable competitive advantages that last for years.

The deployment challenge: More than just training

Segmentation isn’t just about creating nicely designed customer profiles. Teams need to really understand these customers, to feel they know who they are, and to care about what they want. This is a crucial step that requires dedicated time and support.

 

The deployment challenge has two key elements:

 

1. Everyone needs to think differently – Teams must learn and understand something new whilst managing their day-to-day responsibilities.

2. Everyone needs to work differently – This means changing established habits and processes, sometimes in ways that conflict with existing systems.

 

The more your teams understand your segments, the better they can serve them. This understanding translates directly into business results, but it doesn’t happen automatically.

The 3 pillars of successful deployment

Based on experience with over 2,000 successful segmentations, three pillars consistently determine deployment success:

Pillar 1: Make it matter

This pillar is about creating belief in, and commitment to, the segmentation by showing the value it will add to the business and the difference it will make to people’s roles.


Create a growth vision

Your segmentation must be translated into a strong vision of growth for the business. This vision should demonstrate the specific opportunities that targeting these segments can unlock, not just general benefits.


Key elements:

  • Quantified revenue opportunities for each priority segment
  • Clear pathways to achieving growth targets
  • Specific market share or penetration goals
  • Timeline for realising benefits


Set measurable outcomes

Be explicit about what you expect to achieve with your segmentation. This should be translated into a set of KPIs that you can monitor on an ongoing basis.


Examples of measurable outcomes:

  • Revenue growth by segment
  • Customer acquisition costs by segment
  • Customer lifetime value improvements
  • Market share gains in priority segments
  • Customer satisfaction scores by segment


Build investment throughout the process

Even though deployment comes after creating the segmentation, it’s not an afterthought. Preparation begins from the very start of the process. By including relevant stakeholders and communicating progress throughout the organisation, you gain investment that increases the odds of successful deployment.


Practical steps:

  • Involve key stakeholders in segmentation development
  • Share early insights and findings as they emerge
  • Demonstrate how segmentation will solve current business challenges
  • Connect segmentation outcomes to individual team objectives

Pillar 2: Make it memorable

Creating an impactful launch is important, but lasting deployment requires thinking about long-term usage. If segmentation isn’t followed up consistently, learnings will eventually fall by the wayside, and you won’t maximise your return on investment.

Create engaging materials

Your teams need solid reference materials they can use in their daily work:

Beautifully designed pen portraits: 6-8 slides per segment that bring each customer group to life with real personalities, motivations, and characteristics.

Comprehensive handbooks: Detailed guides that teams can reference when making decisions or developing strategies.

Video content: Engaging customer stories and segment explanations that make the insights memorable and shareable.

These materials should be easily available to everyone in your organisation and designed for practical use, not just presentation.

Provide ongoing access to segment knowledge

Sustainable deployment requires continuous learning and reinforcement:

Golden questions: Standardised questions to add segment identification to future studies and research.

Market expansion: Roll-out frameworks for applying segmentation to new markets or regions.

Community panels: Quick access to segment representatives for validation and feedback.

Segment chatbots: Custom-built, self-service tools that bring segmentation deliverables to life using all the proprietary data generated in the project.

Ensure Long-term Accessibility

The better your teams understand your segments over time, the more effective they’ll be in engaging them. This requires systems and processes that make segment insights easily accessible when needed.

Pillar 3: Make it meaningful

Teams need to recognise the value of customer segment information for their everyday work, not just in theory. The segmentation must be directly relevant to the decisions they make and the challenges they face.

Show practical applications

Deployment roadmaps should demonstrate the key use cases for the segmentation across different functions:

Marketing: How to use segments for targeting, messaging, and channel strategy

Product development: How segments inform innovation priorities and feature development

Sales: How to tailor approaches based on segment characteristics Customer

Service: How to provide segment-appropriate experiences Operations: How to optimise processes for different segment needs

Facilitate organisational change

Effective deployment often requires facilitating broader organisational change:

Maturity assessments: Understanding current customer-centric capabilities and identifying gaps

Process integration: Building audience-centric strategy, systems and processes that incorporate segments into day-to-day operations

Skills development: Training teams on how to use segmentation insights effectively

Technology integration: Connecting segmentation to CRM systems, marketing platforms, and analytics tools

Provide supporting toolkits

Teams need practical tools that help them apply segmentation insights:

Value proposition workshops: Sessions that help teams develop segment-specific value propositions

Product development frameworks: Tools for prioritising innovations based on segment needs

Campaign planning templates: Guides for creating segment-targeted marketing campaigns

Customer journey mapping: Frameworks for designing segment-specific experiences

Real-world deployment success

White goods innovation: A household appliance company incorporated segmentation into their innovation framework. By understanding their segments better, they spotted an opportunity around changing cleaning habits. Many customers wanted spotless homes but lacked the right tools.

The company used this insight to create new products, change how they sold them, and adjust their marketing. The result was a new cordless vacuum cleaner so successful it changed their whole business. They could charge more than competitors, grew rapidly, and became a major market player.

This success happened because teams could use segmentation information effectively in their daily work – the segmentation was truly meaningful to their decision-making processes.

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Connected deployment: The integration approach

The most successful segmentations are “connected” – they link to multiple data sources and activation channels. This requires:

Cross-functional involvement: Teams from different departments involved throughout the process, not just at the end.

Multiple data connections: Segmentation built using data from various sources, requiring collaboration across the organisation.

Integrated activation: Segments used across all customer touchpoints and business processes.

Ongoing collaboration: Regular communication and coordination between teams using the segmentation.

Remember: creating segments is just the beginning – using them is the real work.

Measuring deployment success

Track these indicators to ensure your deployment is working:

Adoption metrics: How many teams are actively using the segmentation?

Decision integration: Are segments influencing key business decisions?

Performance improvements: Are segment-targeted initiatives outperforming general approaches?

Cultural change: Are teams naturally thinking in terms of segments?

Business outcomes: Are you achieving the measurable outcomes you defined?

Getting started: Your deployment checklist

Before launching your segmentation:

✓ Clear value proposition: Can you articulate exactly how the segmentation will improve business results?

✓ Stakeholder buy-in: Do key leaders understand and support the segmentation approach?

✓ Resource allocation: Have you dedicated sufficient time and resources for proper deployment?

✓ Training plans: Do teams know how to use the segmentation in their daily work?

✓ Integration roadmap: Have you identified how segments will connect to existing systems and processes?

✓ Success metrics: Can you measure whether the deployment is working?

✓ Ongoing support: Do you have plans for sustaining momentum beyond the initial launch?

This completes our series on customer segmentation strategy. For practical guidance on implementing segmentation in your organisation, consider working with experienced segmentation specialists who can guide you through both creation and deployment phases. 

Paul Jackson - Managing Director of STRAT7 Bonamy Finch

Paul Jackson is Managing Director at STRAT7 Bonamy Finch and a segmentation expert with over 20 years of experience delivering 500+ segmentation projects across global markets.

Paul specialises in hybrid segmentation approaches that combine traditional attitudinal and needs-based methodologies with modern data integration techniques. His expertise spans segmentation strategy, multi-source data integration, CRM connectivity and activation.

Paul ensures rigorous data audits and applies best-practice design principles to deliver actionable insights that drive business decisions. He makes sure segmentations don’t sit on a shelf – helping clients embed them across their organisations and connect them to existing data and systems.

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