Maximizing ROI in Financial Marketing: Campaign Optimization Strategies
Posted: Sun Dec 22, 2024 5:41 am
In traditional marketing, success is often assessed based on hypotheses and estimates, without the precision provided by digital marketing tools.
Key metrics in traditional marketing include exposure estimates based on media audiences and comparisons of sales during an advertising campaign compared to previous periods. However, this method ignores the impact of other factors that may influence sales, which limits the accuracy and reliability of the analysis.
On the other hand, digital marketing uses precise metrics to evaluate the success of a campaign, with return on investment (ROI) being one of the most important. ROI provides a clear view of the results of investments in marketing actions, demonstrating the profit or return generated in relation to the cost invested.
ROI calculations consider revenue, cost savings and other monetary benefits obtained as profit, while costs include marketing expenses, production costs and labor time, among other factors. ROI is currently one of the main metrics for marketing professionals: according to a study by Kantar, 44% of respondents point to ROI as a determining indicator of a campaign's success.
However, many companies in the financial sector face significant challenges in increasing ROI due to a lack of data integration, which hampers their marketing strategies.
A study by Mosaic Smart Data, published in Financial IT , analyzed millions of banking transactions around the world and revealed that 66% of these institutions have gaps with data that is not fully captured, and 83% of banks do not have real-time access to transactions and analysis, mainly due to the absence of a central repository.
To avoid missing out on opportunities to boost ROI, here’s how it works and the best practices for maximizing it.
ROI Calculation
ROI is defined by the following formula: (revenue – investment) / investment. To obtain the percentage, the result is multiplied by 100.
For example, if a company invests R$10,000 in a campaign and obtains revenue of R$20,000, the ROI is calculated as follows: ROI = (20,000−10,000)/10,000.
The value obtained is 1, which when multiplied by 100 and converted to a percentage, results in an ROI of 100%. In other words, the company doubled the value of the initial investment.
A positive ROI indicates a profitable investment. A negative ROI indicates a situation in which costs exceeded gains. When the indicator is equal to zero, it is understood that the investment paid for itself, but generated neither profit nor loss.
The calculation serves as a basis for strategic decisions, as it allows you to identify which campaigns are more profitable, which ones require adjustments and to which the budget can be better allocated.
Maximizing ROI in the financial market
The success of a digital marketing campaign is directly linked to maximizing ROI. For a financial market company, some advantages are:
Definition of the best means of communication and platforms;
Comparison of efficiency actions;
Macro and analytical vision of projects;
Goals based on accurate data;
Fundamentals for reducing expenses.
Therefore, there are five impact strategies to optimize campaigns and boost ROI, namely:
1. Segmentation of marketing actions
The diversity of services in the financial sector, ranging from investments to insurance and loans, makes it essential to consider the specific needs of different customer segments. Therefore, segmenting your target audience is an effective strategy for increasing your return on investment (ROI).
To highlight the importance of segmentation in marketing, an article by Salesforce reveals that 65% of customers expect companies to adapt to their needs and preferences. Therefore, marketing campaigns aligned with the expectations of different target audiences are essential for customer loyalty and, consequently, for increasing ROI.
Some types of segmentation adopted in marketing:
Demographic segmentation (age, gender, religion, ethnicity and profession)
Demographic segmentation (location, culture and language)
Psychographic segmentation (attitudes, values, social status, lifestyle and opinions)
Behavioral segmentation (purchasing behavior, stage in the customer journey, usage behavior and benefits sought)
Another advantage of segmentation is the generation of data that guides the allocation of funds, identifying which customer groups offer the greatest return on investment. Thus, if the ROI of a strategy for a given segment is higher than that of others, it makes sense to allocate more resources to that group.
This reasoning also applies to investments in media and communication channels. For example, a higher ROI on social media campaigns justifies increased investment in these platforms for future actions.
2. Real-time data analysis
Real-time data analysis enables rapid campaign optimization, resulting in significant improvements in ROI.
According to data from the C for Economics and Business Research , cited by Pragmatic , 80% of companies that use real-time data analytics have seen an increase in revenue, totaling $2.6 trillion.
By continuously monitoring information, institutions can identify new business opportunities [https://dbtodata.com/uk-whatsapp]uk number for whatsapp[/url] and adopt proactive rather than reactive approaches. This allows digital marketing campaigns to be adjusted instantly, improving performance with more relevant offers and messages that positively surprise customers.
In addition, real-time data helps predict trends and run simulations of different scenarios, assessing their potential financial consequences. This way, companies have a clearer view of the directions in which ROI can go.
3. Use of Artificial Intelligence and Machine Learning
Artificial intelligence (AI) and machine learning (ML) are powerful tools for managing financial marketing campaigns and maximizing ROI. These technologies streamline processes, personalize customer interactions, and increase the efficiency of marketing campaigns.
By analyzing large volumes of data from purchase transactions, browsing behavior, social media interactions, service histories, and other sources, AI and ML enable the creation of detailed customer profiles. Additionally, these tools continually update the profiles, capturing changes in customer behavior and preferences in near real time.
AI, through natural language processing (NLP), analyzes sentiments expressed in customer feedback, social media, and other channels, enriching profiles with insights into customer perceptions of the company and its services. This enables adjustments to communication strategies, with tones and approaches more aligned with customer expectations and feelings.
In turn, ML improves its algorithms as more data is collected, making it easier to predict behaviors, implement dynamic pricing, and even develop new products based on identified emerging needs.
4. Applying attribution models
Attribution models assign credit to each point of contact a customer has with a company throughout their purchasing journey. This process ranges from the discovery of a need to the final decision to purchase a product or service, including comparisons with offers from other companies, evaluation of payment conditions, and the ease of use of payment systems, among other factors.
These models allow the development of quantitative evidence on the impact of each marketing initiative, facilitating the justification of investments for stakeholders and demonstrating how each channel or strategy contributes to the overall ROI.
Each stage of the customer journey represents an opportunity for engagement. With attribution models, you can identify the contact stages that most influence conversions and optimize investments in these areas to increase ROI.
Additionally, attribution models are valuable for identifying critical points in the customer journey, revealing where drop-offs and abandonment occur. This allows companies to fix flaws and eliminate friction, streamlining the customer’s path to conversion.
5. Checking attention metrics
It is essential to know that a marketing campaign is reaching its target audience, but it is even more important to understand how customers interpret messages. Attention metrics seek to assess audience understanding, interaction and engagement, contributing to increased ROI.
Here are the main factors evaluated by attention metrics:
Viewing time: Analyzes the time users spend viewing a specific ad or content.
Viewability: An ad is considered viewable if a specific percentage of its content is displayed within a minimum period of time.
Engagement: Measures actions that indicate active interest, such as clicks, interactions, page scrolling, and video interactions.
Positioning and context: Analyzes the position of the ad and how it affects attention.
Eye Deviation Rate: Eye tracking technology that checks the locations observed on a screen, allowing us to directly measure the visual attention devoted to different elements of an advertisement.
Interruptions and attention deviations: analyzes when and why users divert their attention from the ad, which may be caused by distracting elements or lack of interest.
Comprehension and retention: In addition to measuring immediate attention, some metrics assess message comprehension and retention with recall tests or post-exposure questionnaires.
To illustrate the impact of attention metrics, a collaborative study between Havas Media Group and Teads showed that ads receive significantly more attention when they are related to the content they are placed within, with articles generating almost 600% more engagement compared to those displayed out of context.
This concept also applies to the positioning of advertising pieces. The same study indicates that the comprehension of ads displayed on cell phone screens increases by 25% when compared to positioning them on the sides of the screen.
Key metrics in traditional marketing include exposure estimates based on media audiences and comparisons of sales during an advertising campaign compared to previous periods. However, this method ignores the impact of other factors that may influence sales, which limits the accuracy and reliability of the analysis.
On the other hand, digital marketing uses precise metrics to evaluate the success of a campaign, with return on investment (ROI) being one of the most important. ROI provides a clear view of the results of investments in marketing actions, demonstrating the profit or return generated in relation to the cost invested.
ROI calculations consider revenue, cost savings and other monetary benefits obtained as profit, while costs include marketing expenses, production costs and labor time, among other factors. ROI is currently one of the main metrics for marketing professionals: according to a study by Kantar, 44% of respondents point to ROI as a determining indicator of a campaign's success.
However, many companies in the financial sector face significant challenges in increasing ROI due to a lack of data integration, which hampers their marketing strategies.
A study by Mosaic Smart Data, published in Financial IT , analyzed millions of banking transactions around the world and revealed that 66% of these institutions have gaps with data that is not fully captured, and 83% of banks do not have real-time access to transactions and analysis, mainly due to the absence of a central repository.
To avoid missing out on opportunities to boost ROI, here’s how it works and the best practices for maximizing it.
ROI Calculation
ROI is defined by the following formula: (revenue – investment) / investment. To obtain the percentage, the result is multiplied by 100.
For example, if a company invests R$10,000 in a campaign and obtains revenue of R$20,000, the ROI is calculated as follows: ROI = (20,000−10,000)/10,000.
The value obtained is 1, which when multiplied by 100 and converted to a percentage, results in an ROI of 100%. In other words, the company doubled the value of the initial investment.
A positive ROI indicates a profitable investment. A negative ROI indicates a situation in which costs exceeded gains. When the indicator is equal to zero, it is understood that the investment paid for itself, but generated neither profit nor loss.
The calculation serves as a basis for strategic decisions, as it allows you to identify which campaigns are more profitable, which ones require adjustments and to which the budget can be better allocated.
Maximizing ROI in the financial market
The success of a digital marketing campaign is directly linked to maximizing ROI. For a financial market company, some advantages are:
Definition of the best means of communication and platforms;
Comparison of efficiency actions;
Macro and analytical vision of projects;
Goals based on accurate data;
Fundamentals for reducing expenses.
Therefore, there are five impact strategies to optimize campaigns and boost ROI, namely:
1. Segmentation of marketing actions
The diversity of services in the financial sector, ranging from investments to insurance and loans, makes it essential to consider the specific needs of different customer segments. Therefore, segmenting your target audience is an effective strategy for increasing your return on investment (ROI).
To highlight the importance of segmentation in marketing, an article by Salesforce reveals that 65% of customers expect companies to adapt to their needs and preferences. Therefore, marketing campaigns aligned with the expectations of different target audiences are essential for customer loyalty and, consequently, for increasing ROI.
Some types of segmentation adopted in marketing:
Demographic segmentation (age, gender, religion, ethnicity and profession)
Demographic segmentation (location, culture and language)
Psychographic segmentation (attitudes, values, social status, lifestyle and opinions)
Behavioral segmentation (purchasing behavior, stage in the customer journey, usage behavior and benefits sought)
Another advantage of segmentation is the generation of data that guides the allocation of funds, identifying which customer groups offer the greatest return on investment. Thus, if the ROI of a strategy for a given segment is higher than that of others, it makes sense to allocate more resources to that group.
This reasoning also applies to investments in media and communication channels. For example, a higher ROI on social media campaigns justifies increased investment in these platforms for future actions.
2. Real-time data analysis
Real-time data analysis enables rapid campaign optimization, resulting in significant improvements in ROI.
According to data from the C for Economics and Business Research , cited by Pragmatic , 80% of companies that use real-time data analytics have seen an increase in revenue, totaling $2.6 trillion.
By continuously monitoring information, institutions can identify new business opportunities [https://dbtodata.com/uk-whatsapp]uk number for whatsapp[/url] and adopt proactive rather than reactive approaches. This allows digital marketing campaigns to be adjusted instantly, improving performance with more relevant offers and messages that positively surprise customers.
In addition, real-time data helps predict trends and run simulations of different scenarios, assessing their potential financial consequences. This way, companies have a clearer view of the directions in which ROI can go.
3. Use of Artificial Intelligence and Machine Learning
Artificial intelligence (AI) and machine learning (ML) are powerful tools for managing financial marketing campaigns and maximizing ROI. These technologies streamline processes, personalize customer interactions, and increase the efficiency of marketing campaigns.
By analyzing large volumes of data from purchase transactions, browsing behavior, social media interactions, service histories, and other sources, AI and ML enable the creation of detailed customer profiles. Additionally, these tools continually update the profiles, capturing changes in customer behavior and preferences in near real time.
AI, through natural language processing (NLP), analyzes sentiments expressed in customer feedback, social media, and other channels, enriching profiles with insights into customer perceptions of the company and its services. This enables adjustments to communication strategies, with tones and approaches more aligned with customer expectations and feelings.
In turn, ML improves its algorithms as more data is collected, making it easier to predict behaviors, implement dynamic pricing, and even develop new products based on identified emerging needs.
4. Applying attribution models
Attribution models assign credit to each point of contact a customer has with a company throughout their purchasing journey. This process ranges from the discovery of a need to the final decision to purchase a product or service, including comparisons with offers from other companies, evaluation of payment conditions, and the ease of use of payment systems, among other factors.
These models allow the development of quantitative evidence on the impact of each marketing initiative, facilitating the justification of investments for stakeholders and demonstrating how each channel or strategy contributes to the overall ROI.
Each stage of the customer journey represents an opportunity for engagement. With attribution models, you can identify the contact stages that most influence conversions and optimize investments in these areas to increase ROI.
Additionally, attribution models are valuable for identifying critical points in the customer journey, revealing where drop-offs and abandonment occur. This allows companies to fix flaws and eliminate friction, streamlining the customer’s path to conversion.
5. Checking attention metrics
It is essential to know that a marketing campaign is reaching its target audience, but it is even more important to understand how customers interpret messages. Attention metrics seek to assess audience understanding, interaction and engagement, contributing to increased ROI.
Here are the main factors evaluated by attention metrics:
Viewing time: Analyzes the time users spend viewing a specific ad or content.
Viewability: An ad is considered viewable if a specific percentage of its content is displayed within a minimum period of time.
Engagement: Measures actions that indicate active interest, such as clicks, interactions, page scrolling, and video interactions.
Positioning and context: Analyzes the position of the ad and how it affects attention.
Eye Deviation Rate: Eye tracking technology that checks the locations observed on a screen, allowing us to directly measure the visual attention devoted to different elements of an advertisement.
Interruptions and attention deviations: analyzes when and why users divert their attention from the ad, which may be caused by distracting elements or lack of interest.
Comprehension and retention: In addition to measuring immediate attention, some metrics assess message comprehension and retention with recall tests or post-exposure questionnaires.
To illustrate the impact of attention metrics, a collaborative study between Havas Media Group and Teads showed that ads receive significantly more attention when they are related to the content they are placed within, with articles generating almost 600% more engagement compared to those displayed out of context.
This concept also applies to the positioning of advertising pieces. The same study indicates that the comprehension of ads displayed on cell phone screens increases by 25% when compared to positioning them on the sides of the screen.