CC BY
0УДК 339.138
Fomicheva E.
master's degree State University of Management (Moscow, Russia)
MODELING THE EFFICIENCY OF MARKETING BUDGET ALLOCATION IN MULTICHANNEL PROMOTION ENVIRONMENTS
Abstract: this article analyzes the key platforms and metrics of multichannel promotion, such as social media, search engines, email marketing, and paid advertising, and also examines various models of marketing budget allocation. The primary focus is on attribution, nonlinear predictive models, and game theory-based approaches. Factors influencing the effectiveness of each platform, such as audience demographics, seasonality, and the competitive environment, are also studied. The integration of machine learning methods is explored to enhance the accuracy of decision-making regarding budget allocation across various channels.
Keywords: multichannel promotion, budget allocation, attribution models, return on investment, marketing analytics, key metrics, machine learning, channel optimization.
Introduction.
In the modern landscape of digital marketing, businesses increasingly rely on multichannel strategies to reach and engage their target audiences. These promotions involve using various touchpoints to deliver a cohesive message and maximize audience reach. While the benefits of multichannel approaches are well-recognized, one of the central challenges is effectively allocating marketing budgets across these diverse platforms to optimize performance and return on investment (ROI).
Marketing budgets are often limited, and the effectiveness of each channel varies depending on factors such as target demographics, the nature of the product, the competitive environment, and the timing of campaigns. As companies expand their
digital marketing efforts, the need for a structured and analytical approach to budget allocation becomes critical. Without a strategic plan, companies risk overspending on less effective means of communication while underfunding those that yield higher returns. The aim of this paper - to explore the different models used to optimize the allocation of marketing budgets across multiple channels of communication, focusing on their effectiveness, strengths, and limitations.
Main part. Theoretical foundations of multichannel promotion.
The term «multichannel promotion» refers to the use of multiple marketing channels (MC) to deliver a consistent message and guide consumers through the sales funnel. These MC can include digital platforms such as social media, search engines, and email, as well as offline channels like television, radio, and print. The integration of these various platforms is essential for creating a seamless customer experience and maximizing the impact of campaigns. According to a 2022 survey [1], more than 70 % of responding marketers from across the globe stated that they used social media to promote content (fig. 1).
Social media (organic!
Email marketing Social media (paid ads)
Organic search
Sponsorships [e.g., events, webinars, podcasts]
PR/media outreach Influencer marketing
RPC/paid advertising
NaSive advertising/ sponsored content
0 10 20 30 40 50 60 70 80
Fig. 1. Channels used for marketing content promotion worldwide in 2022, %.
The proliferation of digital technologies has made multichannel promotion more complex, as consumers now engage with brands through an ever-expanding number of MC. Companies must coordinate efforts across digital platforms (e.g., social
media, email, websites, and mobile apps) as well as offline mediums (e.g., direct mail, retail, events) to ensure a unified customer experience [2]. Understanding the role and characteristics of different marketing platform is essential for developing an effective multichannel strategy. Each channel offers unique advantages and plays a distinct role assessing key metrics in marketing (table 1).
Table 1. Key metrics for evaluating platforms effectiveness in multichannel promotion.
Channel Key metrics and their description Purpose
Social media Engagement rate: measures user interactions (likes, shares, comments) relative to followers. Click-through rate (CTR): percentage of users who click on a link or ad. ROI: profit generated versus marketing costs. To evaluate audience interaction, campaign effectiveness, and financial return from social media efforts.
Search engines Organic traffic: number of visitors who reach the site via unpaid search results. Search engine results page (SERP) ranking: Position in search engine results for targeted keywords. Cost-per-click (CPC): cost incurred per click on paid ads. To measure visibility in search engines, organic search performance, and cost efficiency in paid search campaigns.
Email marketing Open rate: percentage of recipients who open the email. Click rate: percentage of recipients who clicked on links in the email. Conversion rate: percentage of recipients who completed a desired action (e.g., purchase, signup). Bounce rate: percentage of undeliverable emails. To assess effectiveness of email campaigns, user engagement, and overall impact on conversions.
There also are paid advertising, which allows companies to display targeted ads to users searching for relevant products or services, and video marketing that creates visually compelling content that engages audiences. Both of these channels are highly effective for brand promotion and raising product awareness, particularly through storytelling and demonstrations.
When allocating marketing budgets across multiple platforms, it is essential to consider a variety of factors that can significantly influence the effectiveness of each touchpoint. In this case, timing is a critical factor in determining the effectiveness of a MC. The optimal time for deploying marketing efforts can vary depending on seasonality, industry trends, and consumer behavior patterns. Similarly, time-sensitive campaigns, like flash sales or product launches, require precise timing to ensure maximum visibility. Audience segmentation based on factors such as age, gender, geographic location, and behavior enables marketers to tailor messages and select the most suitable platforms for their target demographic [3].
In this regard, multichannel promotion is a powerful strategy for businesses seeking to reach customers across a variety of platforms, both online and offline. By leveraging the strengths of each MC and understanding the customer journey, marketers can develop integrated campaigns that deliver consistent, targeted messaging. As customer behavior becomes increasingly complex in a digital-first world, the importance of a well-coordinated multichannel strategy cannot be overstated. Each MC appeals to different audience segments, and understanding the characteristics of these audiences is crucial for choosing the right channels.
Analysis of various models in marketing budget allocation.
In the context of multichannel promotion, efficiently allocating marketing budgets across various platforms is essential to maximizing ROI and driving business growth. Different channels, such as social media, email, search engines, and display advertising, offer unique opportunities for customer engagement. Their effectiveness varies depending on multiple factors, including audience characteristics, the product or service being promoted, and the competitive environment. To address these
complexities, marketing professionals use a variety of models to allocate resources in a way that optimizes performance.
Attribution models are fundamental in helping marketers determine how to distribute credit for conversions across different touchpoints in the customer journey. In a multichannel environment, customers often interact with multiple marketing source of promotion before making a purchase. With advancement of artificial intelligence, these algorithms can provide a data-driven, dynamic approach to attribution, assigning weight to each interaction based on its contribution to conversions [4]. The challenge is to identify the role that each channel plays in driving conversions and to allocate budgets accordingly.
The linear attribution model assumes that all marketing platforms involved in a customer's journey should be credited equally. If a customer first encounters a brand via a social media ad, then clicks on a search engine result, and finally makes a purchase through an email promotion, each of these channels would receive an equal share of the credit for the conversion. While this approach is simple and intuitive, it often oversimplifies the complexities of multichannel marketing. Not all platforms contribute equally to conversions, and a more nuanced model may be necessary to achieve optimal budget allocation.
In contrast to linear attribution, the time decay attribution model gives more credit to marketing interactions that occur closer to the time of conversion. The rationale behind this approach is that the final interactions in the customer journey are often the most influential in prompting the purchase decision. This model is useful for businesses that experience long sales cycles or require multiple touchpoints to build customer trust. It may undervalue earlier interactions that help build awareness and engagement, which are also critical to the overall conversion process.
Data-driven attribution is a more advanced method that uses historical data and machine learning algorithms to assign credit to different MC [5]. Unlike rule-based models (such as linear or time decay), data-driven attribution analyzes past customer interactions and learns which MC are most likely to drive conversions. This approach allows marketers to allocate budgets based on empirical evidence, providing more
accurate insights into platform performance. Data-driven attribution requires significant amounts of information and sophisticated tools, making it less accessible for small businesses or organizations with limited analytics capabilities.
Game theory provides another framework for analyzing how to optimally allocate marketing budgets across platforms. This approach views the allocation problem as a strategic game where different MC «compete» for a share of the budget, and the marketer's objective is to find a Nash equilibrium, where no channel can improve its performance by unilaterally changing its budget [6].
In cooperative game theory, platform can form alliances to maximize collective performance. Search engine marketing (SEM) and social media might work together to reinforce brand messaging, leading to higher conversions. The goal is to find an optimal allocation strategy that benefits all MC and maximizes overall ROI. Cooperative game theory has been applied in marketing to explore synergies between channels, but it requires a deep understanding of how platform interact, which may not always be apparent.
The Shapley value, derived from game theory, is a method for fairly distributing the total payoff (in this case, conversions or revenue) among players [7]. Each MC's contribution is calculated based on its marginal impact on conversions when combined with other platforms. This approach ensures that channels contributing more to conversions receive a proportionately larger share of the budget.
Predictive models are commonly used in marketing to forecast the potential outcomes of different budget allocation strategies. These models can be either linear or nonlinear, depending on the complexity of the relationships between marketing inputs and outputs.
Linear models assume a direct, proportional relationship between the budget allocated to a channel and its performance. Doubling the social media budget leads to twice as many conversions, a linear model would accurately predict the outcome. These approaches are easy to interpret and implement but may not always capture the diminishing returns that often occur as marketing investments in a platform increase.
Nonlinear models, such as logarithmic or polynomial models, account for diminishing returns and complex interactions between MC. These models recognize that as more budget is allocated to a channel, the marginal gains in performance may decrease. Nonlinear models provide more realistic predictions in scenarios where additional investments on a platform may yield smaller incremental benefits.
Multifactor analysis incorporates multiple variables into the budget allocation process, recognizing that channel performance is influenced by a range of factors, including audience demographics, seasonality, competition, and media saturation. Multifactor models allow marketers to account for these variables and allocate budgets more precisely.
Marketing campaigns often perform differently depending on the time of year. Retail campaigns may sa significant boost in effectiveness during the holiday season. Multifactor models can incorporate seasonal data to allocate budgets dynamically, ensuring that the highest-performing MC receive the most resources during peak periods. Different audience segments may respond differently to various MC. Multifactor models can include audience segmentation as a variable, allowing marketers to allocate budgets more effectively based on demographic or behavioral data. Businesses might allocate more of their budget to social media for younger demographics, while email marketing may be more effective for older segments.
Conclusion.
The effective allocation of marketing budgets across multiple platforms is a critical factor in maximizing the efficiency of marketing campaigns, particularly in the increasingly complex landscape of multichannel promotion. Attribution models offer valuable insights into how different channels contribute to the customer journey. Game theory-based approaches, particularly the use of the Shapley value, provide a fair and strategic way to allocate resources based on each MC's contribution to the overall performance. Predictive models allow for the forecasting of MC performance, while multifactor analysis adds depth by accounting for additional variables. By leveraging these theoretical frameworks, marketers can better understand the complexities of consumer behavior across multiple MC and allocate budgets more effectively,
ultimately driving higher performance and competitive advantage in the digital marketing landscape.
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