Defining the competitive landscape

I start by mapping the category, identifying direct competitors, adjacent players, and aspirational brands that are shaping expectations.

AI plays a key role here by rapidly synthesising an initial landscape based on prompts around category, audience and geography. This allows me to explore a broader set of competitors than traditional desk research alone.

From there, I validate and refine using market data to ensure the landscape reflects real competitive pressure, not just perception.

This step often reveals:

• Unexpected competitors

• Overlooked categories

• Shifts in how the market is evolving

Outcome:
A clear, data-informed view of who we’re actually competing with and where we sit within that landscape.

Understanding how brands show up

Once the landscape is defined, I analyse how competitors position themselves across messaging, campaigns, and digital experiences.

AI is used to process and synthesise large volumes of content, from websites to campaign copy, allowing patterns to emerge quickly and clearly.

Through this, I can identify:

• Common positioning themes

• Repeated messaging patterns

• Tone of voice archetypes

Rather than manually reviewing each brand in isolation, AI enables a more holistic view of the category.

Outcome:
A structured understanding of category norms, clichés, and opportunities for differentiation.

Grounding strategy in real audience behaviour

Brand strategy should reflect what audiences actually care about, not just what brands say.

Using tools like SEMrush, I validate insights against:

• Search demand

• Keyword trends

• Competitor visibility

AI supports this stage by helping interpret and translate data into meaningful insights, highlighting patterns, surfacing themes, and connecting search behaviour back to messaging opportunities.

Outcome:
Strategic directions that are both creatively strong and grounded in real audience intent.

Uncovering audience perception and emotional drivers

Beyond positioning, I look at how brands are experienced by their audiences.

By analysing social channels and audience interactions, I explore:

• Content themes and engagement patterns

• Tone of voice in practice

• Audience sentiment and feedback

AI is particularly powerful here. By feeding in comments, captions and discussions, I can synthesise large volumes of qualitative data to uncover:

• Emotional drivers

• Friction points

• Unmet needs

This allows me to move beyond surface-level engagement metrics into deeper audience understanding.

Outcome:
A more human, insight-led view of audience perception and behaviour.

Turning insight into brand strategy

The final stage brings everything together.

AI supports the synthesis process by helping to:

• Structure insights into clear themes

• Explore multiple positioning directions

• Refine messaging and tone

This doesn’t replace strategic thinking, it accelerates it, allowing for faster iteration and more confident decision-making.

Insights from across the process are then translated into:

• Positioning territories

• Messaging frameworks

• Tone of voice direction

• Campaign platforms

Outcome:
Clear, differentiated brand strategy that connects insight, creativity and commercial impact.