Trent Turner
Case study 01 / Performance marketing

Marketing Portfolio Optimization

Growing customer acquisition while improving acquisition efficiency.

CompanyMosquito Squad of Wilmington
ScopeWilmington, North Carolina
Comparison2023 baseline → 2024 first management year
FocusCustomer acquisition & efficiency
Challenge
Improve customer acquisition efficiency within an established local service market.
Role
Led acquisition strategy, paid campaign optimization, investment allocation, audience targeting, and marketing performance analysis.
Actions
Refined paid search and negative keywords, geographic and demographic targeting, seasonal investment timing, and coordinated promotions.
Results
2023 → 2024: +15.2% YoY new customer growth and +137% YoY acquisition efficiency. Observational business-level results; no controlled lift is claimed.
01

Business Challenge

How can a mature local-service business expand customer acquisition while optimizing the efficiency of marketing investment?

The analysis compares the pre-management 2023 baseline with performance after becoming the marketing lead in 2024. Full-year results through 2025 show how acquisition efficiency changed beyond the first management year.

02

Market Context

Mosquito Squad of Wilmington operates in a mature local-service market. The 2023 pre-management baseline reflects a measured marketing program concentrated in Paid Google Ads and Direct Mail.

In 2024, my first management year, the objective was to expand customer acquisition while improving investment efficiency. The analysis follows this approach through 2025, the second full management year. Campaign targeting and seasonal investment timing were actively managed in 2024; broader channel diversification followed in 2025.

2023Pre-management baseline
2024First management year
2025Second full management year
03

Approach & Methodology

01

Establish Baseline Signals

Use 2023 as the pre-management benchmark and establish the acquisition, demand, and investment conditions present before active management.

02

Compare Outcomes to Investment

Evaluate customer acquisition and related outcomes relative to measured marketing investment.

03

Optimize Allocation Across Channels and Time

Use the analysis to inform how marketing investment is distributed between channels and across periods of demand.

04

Business Decisions & Implementation

2024: Refine Campaigns and Investment Timing

I adjusted the timing of marketing investment around seasonal demand and observed acquisition performance, reallocating within paid campaigns and across periods of the year. I evaluated where marketing dollars were most productive using acquisition outcomes and costs.

I refined negative keywords to reduce irrelevant paid-search traffic, improved geographic targeting toward stronger acquisition opportunities, and applied demographic targeting to improve audience relevance. I also aligned promotional messaging and coordinated campaign timing across paid search, direct mail, paid social where used, and related acquisition channels.

The annual mix remained approximately 60% Paid Google Ads and 40% Direct Mail in both 2023 and 2024. First-year improvements occurred alongside campaign-level optimization, geographic and audience refinement, and budget timing—not a major shift in annual channel mix. The comparison does not isolate the contribution of any individual action.

2025: Adapt Targeting and Broaden the Channel Strategy

I continued negative keyword management and adjusted Google Ads and other digital campaigns as advertising conditions and performance changed. I adapted targeting within Google’s evolving advertising ecosystem, refined demographic and geographic audience selection, and distinguished established territories from developing areas to evaluate emerging growth opportunities.

I broadened the channel strategy and used investment analysis to inform allocation across paid search, paid social, mobile display, and direct mail. These management decisions informed ongoing optimization; the evidence does not establish that they caused, or fully explain, annual performance changes.

Channel Strategy Evolution

Share of Wilmington Measured Media Spend · 2023–2025 full years; 2026 YTD through August

Paid Google AdsPaid Social AdvertisingMobile DisplayDirect Mail
2023
2024
2025
2026 · YTD through August
View chart data
YearPaid Google AdsPaid Social AdvertisingMobile DisplayDirect Mail
202360.36%0.00%0.00%39.64%
202459.69%0.00%0.00%40.31%
202555.11%14.09%30.79%0.00%
2026 YTD49.68%9.94%27.97%12.41%

Allocation considered market maturity, seasonal demand, audience fit, channel role, and observed efficiency. The chart documents how the mix evolved; it does not measure incremental channel lift.

05

Key Findings

Finding 1 / Wilmington

YoY Acquisition Efficiency

2023 baseline → 2024 first management year

+137%YoY Acquisition Efficiency
+15.2%YoY New Customer Acquisitions

Customer acquisition increased while measured marketing investment became substantially more productive. New customer acquisitions grew 15.2% YoY, while new customers generated per $1,000 of Measured Media Spend increased approximately 137% YoY (2023 → 2024).

The improvement occurred alongside a more optimized investment level.

Finding 2 / Wilmington

Acquisition Efficiency

Cost per recorded outcome · indexed to 2023 = 100

Cost per Lead

100.0
2023
46.2
2024
50.7
2025

Cost per New Customer Acquisition

100.0
2023
42.1
2024
58.2
2025

Cost index: 2023 = 100 for each measure. Lower is better; outlined bars mark the baseline.

View chart data
YearCost per Lead IndexCost per Acquisition Index
2023100.0100.0
202446.242.1
202550.758.2
2023 → 2024
−53.8%Cost per Lead
−57.9%Cost per New Customer Acquisition

Cost per Lead decreased approximately 54% in the first management year, while Cost per New Customer Acquisition decreased approximately 58%.

Both measures remained materially below the 2023 baseline in 2025.

06

Measurable Results

Using similar logic across the broader multi-brand home-services portfolio, the following results describe that wider scope.

First-year Portfolio New Customer Growth

2023 → 2024
+26.7%

All four businesses

First-year Portfolio Lead Growth

2023 → 2024
+22.8%

All four businesses

Longer-run Portfolio Lead Growth

2023 → 2025
+26.0%

All four businesses

View chart data
BusinessLead growth · 2023 → 2024Lead growth · 2024 → 2025New customer growth · 2023 → 2024New customer growth · 2024 → 2025
Conserva Irrigation+25.8%+5.2%+21.3%-4.9%
Mosquito Squad of Charlotte+61.2%-0.5%+98.2%-10.0%
Mosquito Squad of Wilmington+5.2%-14.0%+15.2%-31.5%
Outdoor Lighting Perspectives-11.7%+64.7%-33.8%+81.1%
Portfolio total+22.8%+2.6%+26.7%-11.5%

Calculated growth rates are shown in place of underlying lead and customer totals.

Source: WWJW Marketing workbook, the four business performance sheets; annual totals reconciled to monthly records. Growth = (ending total ÷ starting total − 1) × 100. First-year growth compares 2023 with 2024; the longer-run comparison ends in 2025. These are portfolio totals, distinct from the Wilmington-only efficiency measures.

Portfolio new customers declined 11.5% from 2024 to 2025, while leads rose 2.6%. The first-year results do not imply uninterrupted growth. Even with that decline, 2025 results show a meaningful improvement over the 2023 pre-management baseline, with both new customers and leads above their starting levels.

Returning to Mosquito Squad of Wilmington, acquisition efficiency remained stronger than the 2023 baseline through the second full management year.

Wilmington / Full years 2023–2025

Acquisition Efficiency Beyond the First Year

Efficiency index · 2023 = 100

2023
100.0
2024
237.3
2025
171.9

Acquisition efficiency indexed to 2023 = 100. Higher is better; outlined bar marks the baseline.

View chart data
YearAcquisition Efficiency Index
2023100.0
2024237.3
2025171.9

Acquisition Efficiency = New Customers ÷ Measured Media Spend × $1,000. Percentage changes use unrounded rates. Cost per Lead and Cost per New Customer Acquisition divide the same annual spend by the corresponding annual outcome count. Raw lead and customer totals, exact annual media spend, and absolute acquisition rates are not published.

Wilmington new customer acquisitions declined 31.5% from 2024 to 2025. Cost per Lead and Cost per New Customer Acquisition remained below the 2023 baseline, and Acquisition Efficiency remained above it. New Customers and Leads were below their 2023 levels, so this is evidence of sustained efficiency rather than uninterrupted acquisition growth.

07

Data & Definitions

Marketing Inputs

Paid Google Ads
Paid Social Advertising
Mobile Display
Direct Mail

Business Outcomes

Leads
New Customers
Net New Customer Value

Derived Measures

Cost per Lead
Cost per New Customer Acquisition
New Customers per $1K of Measured Media Spend

Acquisition Efficiency measures new customers generated per $1K of measured media spend. Efficiency calculations use total Wilmington Measured Media Spend rather than platform-reported attribution.

Full-year comparisons cover 2023–2025; the 2026 channel mix is YTD through August. Source: annual marketing and business-performance records, with channel-allocation and chart tables.

08

Limitations & Considerations

  • This is an observational business-performance comparison rather than a controlled test. Changes cannot be attributed to marketing decisions alone.
  • Cost per Lead, Cost per New Customer Acquisition, and Acquisition Efficiency use total Wilmington Measured Media Spend across the listed channels and all recorded outcomes, not only platform-attributed conversions.
  • Channel roles differ, so allocation percentages should not be interpreted as direct measures of incremental effectiveness. The incremental investment analysis does not establish controlled channel lift.
  • 2026 data is year-to-date through August where shown and is not directly comparable with a full year.
09

Tools / Methods

Budget AllocationPerformance MeasurementMarketing EfficiencyChannel Mix AnalysisBaseline ComparisonData VisualizationBusiness AnalyticsExcelPythonSEMrushJMPRStudio

Excel as the source; Python for extracting and validating chart tables.

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