Brands at Play's Sources and Methodology for Identifying Benchmarks

Sources Consulted:

1. Boston Consulting Group (BCG) – Personalization at Scale
   Link: https://www.bcg.com/publications/2017/marketing-sales-boosting-revenues-through-personalization
   Relevance: BCG provides insights into how personalized marketing at scale can boost revenues by 10–30%. We used this data to set realistic upper-range benchmarks for conversion rate improvements when AI-driven personalization strategies are implemented.

2. Salesforce – State of Marketing Report
   Link: https://www.salesforce.com/form/marketing/state-of-marketing/
   Relevance: Salesforce’s research highlights that brands leveraging predictive lead scoring and AI-driven personalization see substantial improvements—often 20–30% in conversions and lead quality. We applied their findings to estimate potential uplifts in CMQL (Customer MQL) and overall lead quality metrics.

3. Google Marketing Platform & Bain & Company – Think with Google
   Link: https://www.thinkwithgoogle.com/
   Relevance: Studies from Google and Bain emphasize that advanced attribution and predictive analytics can deliver ROI improvements ranging from 15–35%. We utilized these benchmarks to adjust cost-per-lead (CPL) assumptions and ROI projections, reflecting a more data-driven approach to budget allocation and channel optimization.

4. Forrester Research
   Link: https://www.forrester.com/
   Relevance: Forrester’s expertise in data-driven personalization and customer experience provided insights into potential double-digit growth in engagement and lead generation. We leveraged their benchmarks to justify a 10% uplift in total leads when proper AI-driven audience segmentation and personalization tactics are employed.

5. McKinsey & Company
   Link: https://www.mckinsey.com/
   Relevance: McKinsey’s studies on AI integration and marketing efficiency highlight cost reductions and channel-level optimizations. We referenced McKinsey’s data to support CPL reduction assumptions and incremental improvements in ROI tied to predictive targeting and spend optimization.

6. Deloitte
   Link: https://www2.deloitte.com/
   Relevance: Deloitte’s work on personalization and AI adoption in marketing provided initial conservative estimates (e.g., 10–15% improvement in conversion rates). Although we later shifted to higher benchmarks from BCG and others, Deloitte’s findings helped establish a baseline.

Brands at Play's Methodology for Benchmark Identification:
To develop the AI ROI Navigator’s benchmarks, Brands at Play conducted a multi-step evaluation process:

1. Literature Review: We started by reviewing reputable, globally recognized consulting firms, research institutions, and technology leaders known for their in-depth analysis of marketing and AI trends. This helped us gather a wide range of performance improvement data.

2. Comparative Benchmarking: We then compared the reported ranges of improvements—such as increases in conversion rates, reductions in CPL, and gains in ROI—across different studies. Our goal was to identify common patterns and realistic uplift percentages that appear consistently in multiple reports.

3. Contextual Adjustment: Since each source’s numbers often depend on factors like industry vertical, baseline maturity of data infrastructure, and extent of AI adoption, we opted for “middle-to-high” uplift values to represent ambitious yet achievable outcomes. For instance, if a source stated a 10–30% range, we chose a figure near the midpoint or slightly above, reflecting a scenario where AI strategies are well-implemented but not necessarily at the absolute maximum potential.

4. Integration into the Tool’s Calculations: After establishing these benchmarks, we integrated the selected percentages into our ROI calculations. The improvements in conversion rate, CPL, total leads, and MQL/CMQL conversions were input as static assumptions. This enabled us to show users how their baseline metrics could be transformed under more advanced AI-driven marketing strategies.

5. Continuous Review: Lastly, we acknowledge that benchmarks evolve as AI technologies, data science practices, and marketing strategies advance. The chosen values represent current best-available estimates and may be updated periodically as new studies and market data emerge.

By combining multiple authoritative sources and carefully selecting realistic yet aspirational improvement percentages, we ensured that the benchmarks used in our tool are both credible and actionable, guiding marketers to understand the potential impact of integrating AI into their operations.