Analysis of Catering Co-Branding Campaign Outcomes Through Brand Awareness Data

With the development of the competitive environment and the spread of attention, brands are now more cooperative to maintain their vitality in the market. Co-branding is a kind of strategic alliance in which two or more different brands work together to develop a unified product or service, and it is also a way to allocate resources and transfer equity. In the catering industry, this strategy has been widely adopted; that is to say, limited-edition culinary delicacies and cross-promotional activities will be held to combine different brand identities and attract consumers. But although such cooperation is common, its actual effects still need to be studied academically, especially regarding brand awareness.

Brand awareness includes both brand recognition and recall, and it is the foundation of consumer-based brand equity ^[1]. It is the cognitive cause of adding to the evoked set of consumers, thus reducing perceived risk and increasing the probability of purchase. In the context of catering co-branding, campaigns are basically organised to use the previous awareness of allied entities to increase the number of audiences and create cognitive synergy. Nevertheless, according to the actual operation of these campaigns, not all partnerships can be effective in increasing brand awareness; there may be other reasons why the brand image is weakened or consumers' enthusiasm is reduced ^[4]. Therefore, studying the results of the campaign through brand awareness data will have significant theoretical and practical value.

This paper is a supplement to the research on co-branding in the marketing of durable goods and high-tech products, but there is little research in the catering sector. Catering has the characteristics of experiential consumption and hedonic consumption, short-term consumption of products, and a prominent influence of sensory marketing. Therefore, traditional spillover effect models need to be adjusted based on context. In practice, the profit margins in the catering industry are small and there is high consumer volatility; hence, the effect of marketing expenditures is a problem, as existing findings show that brand awareness exerts significant positive effects on key consumer behavioral outcomes ^[7]. Through the above analysis of the changes in awareness indicators of co-branding projects, such as aided recall and unaided recognition frequency, some practical suggestions have been made, including guidance for brands to leverage social media content and user participation to enhance the impact of their marketing efforts on consumer decisions ^[10][13]. It helps marketers move away from rules of thumb and systemically evaluate whether the brands align and how the resources can be mutually beneficial before large amounts of capital are invested in cooperation. Therefore, this paper will attempt to narrow the empirical gap by quantifying how co-branding campaigns in the catering industry change the path of brand awareness and provide some support for the theory of strategic marketing and practical campaign improvement.

In the field of food services, food-service co-branding is the short-term or medium-term marketing activity for multiple food-service or consumer brands to launch jointly developed products, limited-time experiences, and publicity activities, among other things. A strategic alliance is formed to strengthen the brand equity of both parties and attract new customers. Brand awareness in the context of catering consumer choice is how well and readily the brand is remembered in people's minds; it is one of the necessary conditions for entering the consumer's consideration set. Catering is the industry that is subject to frequent consumption and fierce competition. The Measurable Co-branding Campaign Results are the quantifiable changes in visibility and memory of target audiences after a joint campaign and serve as direct indicators of short-term tactical success and long-term equity improvement.

The theoretical basis of this paper is built on three pillars. The first is the theory of associative network memory, according to which consumer knowledge is organised in a network of related nodes. When a co-branding campaign of catering is launched, the activation of one partner's brand node will naturally trigger the connected node of the other partner, thus activating the old memories of both brands in consumers' minds at the same time. This is how co-branding can enlarge the range of choices for consumers and make the brand more noticeable. Second, the Brand Transfer Effect Framework is used to study the direction in which the brand associations of the two cooperating parties are shared. It shows that, through positive feeling attributes like perceived quality and lifestyle symbolism, higher-equity enterprises can pass on to lower-equity ones or increase both at the same time to reduce the perceived risk of consumers' trial purchases. Third, the Theory of Marketing Performance Measurement is to adjust the general marketing KPI system according to the characteristics of the food service industry; thus, real-time, behavioural, and visibility-oriented indicators are used instead of long-term financial data, and thus they can fit the needs of the fast-paced consumer market.

Moreover, based on the relevant domestic and foreign academic and industry studies of catering co-branding in the past, it can be seen that most of them use subjective survey scales or qualitative case studies to evaluate the effect of the campaign. Although these ways have provided some new ideas, they lack objectivity in measuring the effect of the campaign based on brand awareness data, such as search index and digital footprint. The above method is not accurate for empirically verifying high-speed catering markets. By systematically integrating the above definitions and theoretical perspectives, a good theoretical foundation for constructing indicators and carrying out empirical analysis in the next parts has been laid, ensuring that the measurement of co-branding results is both theoretically sound and empirically rigorous.

It is necessary to abandon the old way of constructing the Brand Awareness Evaluation Indicator System for catering co-branding campaigns in order to break away from the strong reliance on the subjectivity of self-reported data. In order to be more scientific in terms of data, this paper will build a multi-dimensional data-accessible system that can be used to monitor changes in brand awareness caused by joint promotion activities. The system is divided into three parts: the initial level of brand awareness before the start of the campaign, the changing level of awareness during the execution of the campaign and the lasting level of brand awareness after the campaign. According to this division, the effect of joint branding can be separated from the natural development of the brand.

The first dimension, the baseline brand awareness before the campaign, has been set as the empirical reference point for intervention. There are three sub-indicators that can be measured: the monthly average of brand searches on major search engines, the number of brand-related posts on social media per month, and the spontaneous recall rate of brands from third-party consumer tracking platforms. These indices can collectively show the original level of market attention among the participating parties before the collaboration.

The second dimension is the change in people's awareness due to the co-branding during the campaign; that is, it is a type of resonance of the joint promotion itself. The sub-indicators are the increase in search volume for co-branding-related content alone, the number of views of the hashtag on major social media platforms for the joint campaign and the amount of user-generated content tagged with both the name of the co-branding partner. These are indicators of how people's thinking changes and digitalisation is promoted in a certain way by the collaboration.

The third dimension, that is, the maintenance of people's awareness after the campaign, is to examine how well the campaign worked long-term. After the conclusion of the campaign, the three indicators of 30-day, 60-day and 90-day residual awareness growth rates will be calculated to assess whether people have formed a lasting memory of the brand due to the short-term attention drawn by the campaign.

To achieve reproducibility and ensure that the research can be compared objectively among all types of catering co-branding cases, a standardised data collection standard has been set. Data sources are only from public search trend platforms, social media public content databases and authorised third-party catering consumer behaviour tracking data sets. To avoid the problem of subjective weighting, we use the entropy weight method to combine all the sub-indicators. Based on the information entropy and variance of the data for each indicator, this objectivity-mathematical method will assign weights to each indicator; thus, indicators with higher differentiation abilities will be given more weight. Therefore, the constructed evaluation system is both quantifiable and has a certain degree of accuracy; it is a relatively complete tool that can be used to study the co-branding results of the catering industry systematically.

The empirical analysis needs to be based on reasonable sampling methods. In order to study the effect of catering co-branding campaigns through brand awareness data, a set of rigorous rules has been set up to exclude confounding factors that can distort awareness metrics. Between January 2021 and December 2023, 72 representative catering co-branding campaigns were launched in the mainland Chinese market. The time range for this research is relatively new, so the data should be relatively easy to obtain. Campaigns with large-scale offline public events or those disrupted by unexpected viral non-co-branding-related incidents are systematically excluded. Exogenous shocks to brand awareness will cause the data to be insufficiently isolated in empirical design. By removing these interferences, it can be guaranteed that the observed changes in awareness are due to the internal reasons of the co-branding initiative and not due to other noise in the market.

A large amount of structured data is collected from two different directions for each confirmed sample. Based on the first basic attributes of the two involved brands, brand positioning, average customer price, core target consumer age group and market share in their respective catering segments will be noted. The above indicators can be used to assess the degree of brand compatibility and resource imbalance, which are the foundation of the theory of co-branding. Second, the basic campaign parameters are recorded; the campaign length, form of joint output (co-branded limited food products, co-branded peripheral merchandise, cross-brand store pop-up events, etc.), and total estimated marketing investment are included. At the same time, all brand awareness indicator data defined in the above theoretical framework have been collected before, during and after the campaign to track the trend of changes in awareness.

After collecting the data, descriptive statistics will be carried out to characterise the data and find some distribution patterns. Visualisations like histograms, scatter plots, etc., are employed to present statistical characteristics more directly. The histogram is used to show the general distribution of the number of new customers in all 72 sampled marketing campaigns, that is, how concentrated the campaign results are. In addition, it is known that there was no measurable increase in awareness for some of the campaigns, and there were problems in the co-branding activities. The scatter plot shows the initial relationship between the size of investment and total improvement in awareness, and the box plot shows the distribution of various forms of joint production. The above descriptive section will give a general introduction to the sample and lay the basis for the next step of formal quantitative modelling.

To quantify the difference in results of the co-branding promotion campaign, we need to further study how the original brand conditions affect the effectiveness of the co-branding plan. A simple Pearson correlation and stepwise regression analysis is carried out here to study how the inherent basic attributes of catering brands before the co-branding campaign will affect the increase in brand awareness. Resource Allocation and Signalling Theory are used as the theoretical basis of this paper, and it is assumed that the base of brand equity before the campaign will affect the ability to absorb and use external brand associations, thus setting the upper limit of what can be achieved through co-branding.

Operationally, Pearson's correlation coefficient is first calculated to check whether there is a linear correlation between all the pre-campaign brand characteristics and the total weighted awareness growth score in the new indicator system. The characteristics studied here are the initial brand awareness level, the overlap rate of the brand's target audience with that of another partner, the average price difference of the brand's customers, and the position of the brand in the market. This first correlation analysis will distinguish the direction of the association and the initial significance of each variable to provide a basic knowledge of how the baseline characteristics affect campaign performance changes.

To determine the extent of the impact and the direction of causality, a stepwise regression model will be used to remove the influence of other factors. Through the control of confounding variables such as campaign investment scale and promotion channel coverage, the model will examine which pre-campaign basic characteristics have statistically significant positive or negative effects on co-branding performance and estimate their particular marginal effects in depth. The first case study in this process will test the hypothesis of "room for growth". That is to say, compared to brands that are already well-known, do brands with lower pre-campaign baseline awareness have a higher marginal awareness growth from co-branding cooperation? It is necessary to verify the statistical significance of this inverse relationship in the whole sample; otherwise, it cannot be clearly shown that the diminishing marginal returns of awareness are for already well-known brands and that collaborations can compensate for the disadvantages of emerging or niche brands. Finally, this research path will explain how the basic conditions of an existing brand may restrict or help increase awareness, and thus offer practical theoretical limits for selecting partners in catering co-branding.

The heterogeneity test of the effect of co-branding partner matching on awareness transmission is a necessary part of the analysis of the contingency of collaborative outcomes. Basically, co-branding efficiency is not the same; rather, it varies based on how well the two partner brands' strategies align, and thus whether or not they can successfully transmit their cognitive resources to consumers depends on this. Based on group regression and interaction effect analysis, this section will systematically break down the differences in awareness transmission effects among various combinations. Based on two fundamental dimensions of classification: brand positioning similarity (high and low) and target audience overlap degree (high and low), this paper will divide the samples into four different groups. In order to separate out the particular structural causes of synergy in cooperation and determine under what conditions awareness transmission will thrive or be stifled, a large number of dimensions of stratification are needed.

In the above analysis, the two main result indicators were compared for the generated groups: the total net awareness increment of the co-branding campaign and the unbalanced awareness transfer ratio between the two brands. The unbalanced ratio is the share of the total increase in awareness that a relatively stronger brand has compared to a relatively weaker brand; it is an accurate measure of whether there is a discrepancy in visibility increases. Based on the above metrics for different groups, it is clear which type of matching can maximise the overall increase in awareness of the cooperation and which configuration can solve the problem of "one-sided awareness gain". It is a common problem in alliance design that one party obtains most of the gains in visibility, while the other gets very little in awareness improvement. Studying how positioning similarity and audience overlap regulate such asymmetry can provide important theoretical knowledge about the operation of cognitive spillover and audience absorption in the catering industry.

From the operational side, the interaction effect models can tell us how the various combinations of matching factors contribute to the particular result, and how much of that contribution is statistically significant due to differences among them. Based on the above empirical results, some representative samples of different groups will be selected and analysed in order to provide practical strategic intelligence for catering business practitioners. The above cases show the statistical heterogeneity, that is, high audience overlap with low positioning similarity can lead to a large increase in the total awareness of the store but worsen the unbalanced transfer ratio, while high similarity and moderate overlap may promote a more equal distribution of awareness among all the stores. Based on the actual catering cooperation cases, the quantitative heterogeneity test will link the abstract econometric results with the reality of alliance construction, and then marketers can choose the matching mode of partners in advance to obtain good overall publicity and fair brand promotion.

Translating the empirical results into concrete, data-based optimization plans offers the catering brand's marketing team a set of systematic steps to follow for all campaigns. The selection of partners before the start of the campaign should consider their baseline awareness and particular goals for growth based on the company's past data. In order to expand into new customer groups, the operation mode of the brand is to adopt a relatively high level of differentiation matching; partners with relatively small audience overlap but strong awareness in the untapped field are to be selected. A traditional regional bakery should take advantage of awareness data to collaborate with an esports platform and not choose an old-established dairy company if it wants to attract young people. If short-term general-purpose awareness is to be improved in a limited manner, choose a mode of matching with many people who have the same set of consumers in the form of high-adherence and short-term exposure. A fast-food chain and a large beverage company have combined their loyal customers.

The core of the in-campaign operation is a dynamic resource allocation that is based on real-time awareness tracking. The operation mode is to observe the growth trend of awareness in the first seven days continuously. Empirical research shows that the momentum in the early stage is strongly correlated with the final result. Thus, marketing teams should adjust their plans in real time and spread promotional spending in an orderly way to the places where awareness has increased quickly. If a co-branded catering campaign with a special drink and snacks shows that social media challenges have a steeper awareness curve than traditional discount codes, managers should immediately shift ad money to increase interactive challenges for maximum cumulative influence in the campaign.

At the end of the campaign effect retention phase, our goal is to systematically extend the publicity effect of the co-branding event. According to the data of 30 days of awareness retention in practice, the path to implement it is shown. If the retention rate has a sharp drop-off curve, some follow-up marketing work should be carried out as soon as possible. This means that the one-time co-branding consumers should be transformed into the core brand ecosystem by means of a loyalty programme. For example, if a pizza brand observes a rapid drop in awareness after the campaign, it may trigger a targeted follow-up email campaign to provide customers with a continuous loyalty reward for the co-branded product and thus convert temporary visibility into lasting brand equity. Through the above methods of data-driven awareness to partner selection, operation and effect retention in the catering sector, empirical variables can be turned into strategic assets, making co-branding campaigns more measurable and lasting in the market.

In short, this paper uses the data of brand awareness to study the results of catering co-branding campaigns and discuss the basic mechanism and strategic value of cross-brand alliances in the food and beverage industry. The core idea of catering co-branding is to combine the resources of two different brands to create a new product that is better than either brand alone in the market. The first step is to find a suitable partner according to the complementary nature of the audiences of each brand and the level of brand parity. By tracking brand awareness indicators such as aided recall, unaided recall and brand recognition, it is confirmed that these implementation paths can increase the cognitive accessibility of target consumer groups. The empirical results show that a successful co-branding strategy can act as a cognitive shortcut and rely on the existing association network of both brands to lower the cost of information search and increase the salience of perception when people are making choices ^[3].

In practical terms, it is difficult to overstate how useful the data on brand awareness is in measuring the results of co-branding. The previous financial indicators can only show how profitable a company was in the past, while brand awareness is a forward-looking index to see how widely known a company's products are among the general public and build a favourable image over time ^[6]. In a highly saturated catering market, where consumers are less and less willing to be influenced by advertising, co-branding campaigns provide a way to stand out. The data show that campaigns with the highest rise in awareness are those that follow the optimal incongruity principle; that is to say, the cooperating brands should be perceived as relatively similar in the minds of consumers but not too close to be too simple or too far apart to be memorable. Based on the above, the continuous monitoring of awareness changes by marketing departments will help us adjust the intensity and duration of campaigns in a timely manner to promote brand familiarity and avoid dilution or confusion. This study shows that brand awareness is not only a superficial visible indicator, but also a basic foundation for the construction of the structural integrity of the newly established co-brand identity, and has a strong positive association with consumers' purchase intentions ^[9]. In relevant marketing communication scenarios, the relationship between brand awareness and purchase intention also presents distinct characteristics in specific media contexts ^[12]. Therefore, for catering enterprises that want to establish long-term competitive advantages, co-branding can be applied through theoretically sound methods and evaluated carefully based on data; thus, it will be an effective means to promote the revitalisation of the market and expand the consumer base.

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