How We Mapped the UK Banking Market and 3x'd Campaign Performance

When we started working with a new banking app client, they faced a classic growth challenge. They had a solid product but no clear strategy for who to target or how to message different audiences. The UK banking market is incredibly competitive, and they couldn't afford to waste budget on broad, unfocused campaigns.

The challenge was comprehensive market mapping. We needed to understand not just what customer segments existed, but their actual sizes, what messages appealed to each segment, and how much overlap existed between them. Traditional market research would have taken months and cost tens of thousands of pounds to get this level of insight.

Instead, we used TURF analysis to systematically map the entire UK banking market. We tested different value propositions across various customer segments - from young professionals focused on savings goals to families looking for better budgeting tools to freelancers needing business banking features.

The results were eye-opening. We discovered that certain combinations of customer segments and messaging had far greater total reach than others. More importantly, we found significant overlap between segments that initially appeared distinct - meaning we could reach multiple audiences with carefully crafted messages.

The TURF analysis revealed that our client's strongest positioning wasn't what they initially thought. While they wanted to focus on premium features for high-value customers, the data showed that a combination targeting everyday banking needs, savings automation, and financial wellness would reach 73% of their total addressable market with just three core messages.

Armed with accurate reach estimates, we could reverse-engineer the entire marketing strategy. We took historical CTR data from similar banking campaigns and worked backwards to predict exactly how many sales we'd generate per month with different budget allocations. This let us create a strategic targeting plan with confidence.

The impact was immediate and measurable. Instead of generic banking ads, we created segment-specific campaigns using the winning message combinations from our TURF analysis. We also optimized their homepage and broad-targeted campaigns with the message that scored highest for total unduplicated reach.

Campaign performance increased 3x compared to their previous unfocused approach. More importantly, we had a strategic roadmap for scaling - we knew exactly which segments to target next and what messages to test as they expanded their marketing efforts.

Step 1: Define Your Options Set

Start by listing all the products, features, messages, or customer segments you want to test. Include current offerings and potential new additions. Aim for 6-20 options total - enough to identify meaningful combinations without overwhelming the analysis.

Step 2: Generate Your Market Personas

Create AI personas representing your total addressable market, not just existing customers. Include diverse demographics, psychographics, needs, and behaviors. For comprehensive market mapping, generate 500-1000 personas across different segments.

Step 3: Measure Individual Appeal

Test each option individually first. Ask personas about their interest, purchase intent, or preference for each product, feature, or message. This establishes baseline appeal before testing combinations.

Step 4: Test All Possible Combinations

Systematically test different combinations of your options. Start with pairs, then move to larger combinations. For each combination, measure how many personas would be interested in at least one element of that combination.

Step 5: Calculate Unduplicated Reach

Determine the total percentage of personas reached by each combination without double-counting overlapping preferences. This is your unduplicated reach - the key metric that differentiates TURF from simple preference ranking.

Step 6: Analyze Frequency Patterns

Calculate average frequency - how many options within each combination appeal to interested personas. Higher frequency can indicate stronger engagement and potentially higher revenue per customer.

Step 7: Identify Optimal Combinations

Look for combinations that maximize both reach and efficiency. The best portfolio might not include the most individually popular options if those options appeal to overlapping audiences.

Step 8: Segment by Persona Characteristics

Analyze which combinations work best for different demographic or behavioral segments. This reveals targeting opportunities and helps optimize messaging for specific audiences.

Step 9: Model Resource Constraints

Apply real-world constraints like budget limits, shelf space, or development capacity. TURF analysis helps you choose the optimal combination within your actual limitations.

Step 10: Validate Strategic Implications

Use TURF findings to guide broader business decisions about product development, marketing strategy, and resource allocation. Test refined combinations based on initial insights.