Marketing Mix Modelling (MMM) offers a powerful solution to attribution by effectively separating the impact of brand and performance media from other influences, such as policy price. By quantifying long-term brand-building effects alongside short-term performance media, insurers can make informed budgetary decisions that balance immediate acquisition with brand growth, ultimately leading to sustained increases in revenue over time.    

Additionally, geo-incrementality testing can validate MMM estimations, refining accuracy by providing real-world insights into media effectiveness. 

For insurance brands, optimising media spend across the marketing funnel, from awareness to sale, presents significant challenges. Unlike impulse-driven industries, insurance purchases involve long decision cycles, multiple touchpoints across online & offline channels and a strong reliance on seasonality, pricing & third-party aggregators. These factors make media attribution complex and difficult to measure accurately. 

This article explores how MMM can address key challenges and provide solutions to ensure a sustainable and value-driven marketing strategy for insurance brands. 

What are the main challenges facing insurance marketers? 

Long sales cycles and delayed attribution: Insurance purchases involve long decision-making periods as customers compare policies and wait for renewal dates. This delay complicates policy sale attribution, often favouring performance media and over-crediting the last touchpoint instead of considering the full customer journey. 

Limited cross-channel visibility: Customers engage with multiple touchpoints across online and offline channels, including walled gardens such as Google, Meta and Amazon which restrict data-sharing. This fragmentation makes it difficult to track the entire customer journey, leading to challenges in accurately measuring attribution and ROI. 

Balancing brand and performance marketing: Awareness and trust-building efforts via upper-funnel channels like TV and OOH have long-term effects that do not immediately translate into measurable sales. These brand-building activities are often undervalued due to a lack of precise measurement, making it harder to justify long-term investment. In contrast, performance marketing, such as PPC and paid social, deliver short-term, trackable results but cannot substitute for weak brand presence. This leads to higher customer acquisition costs. Achieving the right balance between brand and performance marketing is critical for an effective marketing strategy and sustainable growth.  

Impact of pricing and seasonality on media effectiveness: Insurance purchases are highly price-driven, particularly during peak renewal periods, making it difficult to isolate media-driven impacts from competitive pricing and seasonal demand fluctuations. 

Challenges with aggregator sites and attribution: A significant proportion of insurance policies are purchased via price comparison sites like CompareTheMarket and GoCompare. This complicates media attribution by raising questions about whether policies bought via aggregators should be credited to marketing efforts or pricing alone. Insurers must balance direct response marketing with maintaining a strong presence on these platforms.  

So, how can MMM tackle these issues? 

One of the key reasons for MMM’s popularity is its ability to isolate the impact of various factors – such as media efforts – on conversions. Even in the insurance industry where pricing, seasonality, long customer journeys and walled gardens limit cross-channel visibility; MMM can accurately attribute policy sales to marketing efforts. By incorporating time-lagged responses, decay curves, and a baseline, MMMs effectively separate media-driven impact from non-marketing influences, such as price fluctuations and their effect on policy uptakes.  

Unlike multi-touch attribution, which relies on user-level tracking via cookies, MMM leverages aggregated data to estimate the contribution of each channel, overcoming visibility limitations caused by walled gardens. This approach enables deeper insights into cross-channel interactions. Additional geo-incrementality testing can be used to analyse regional marketing variations and infer cross-channel effects without relying on user-level tracking, further enhancing MMM’s accuracy. 

MMM not only quantifies the long-term contribution of brand marketing but also provides visibility into the impact of performance media. This is achieved by incorporating adstock transformations or building a series of models to measure relationships across the marketing funnel – such as how heightened awareness from brand efforts leads to additional policy sales. Furthermore, MMM can identify delayed media effects and pinpoint saturation points, revealing when increased investment in a channel begins yielding diminishing returns. These insights support data-driven decision-making to optimise media investment, balancing brand and performance media to ensure sustained brand strength and marketing efficiency. 

By analysing historical data, MMM can estimate the extent to which aggregator-driven policy volumes is influenced by marketing efforts versus organic traffic. It quantifies the relationship between brand and performance media in driving policies, considering both aggregator sites and direct. As a result, MMM provides a holistic view of media effectiveness across all conversion paths, allowing insurance brands to optimise their marketing investments with confidence. 

What are the benefits of incorporating MMM into marketing strategies? 

By utilising MMMs into their marketing strategy, insurance brands can navigate the complexities of attribution with greater precision, ensuring that both brand-building and performance-driven efforts contribute effectively to overall policy sales and growth. The ability to quantify long-term brand impact, optimise media allocation and adjust for external factors like pricing and seasonality allows insurers to make data-driven decisions that maximize efficiency and ROI. 

Geo-incrementality testing further enhances MMM accuracy by providing real-world validation of media effectiveness, helping to refine model estimates and improve confidence in attribution results. 

Ultimately, MMM provides a holistic, scalable and future-proof approach to media measurement. It helps insurers reduce acquisition costs, refine their aggregator strategy and strengthen their competitive positioning; whilst finding an optimal mix between brand and performance marketing. In an industry where customer journeys are long and fragmented, MMM serves as a critical tool in balancing short-term conversions with sustainable brand revenue growth, driving higher policy sales and long-term marketing effectiveness. 

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