Neuromarketing and Big Data Analysis of Banking Firms’ Website Interfaces and Performance
Abstract
:1. Introduction
2. Literature Background
2.1. Banking Firms, Digital Marketing, and User Engagement
2.2. Metrics and KPIs of Friendly Website User Interface (UI)
2.3. Neuromarketing and Big Data Analysis Implications on Website Interface and Performance
2.4. Hypotheses Development
3. Materials and Methods
3.1. Methodological Concept
- The research started with the collection of data on website customers and digital marketing activities from banking firm websites. A website’s user behavioral data (pages per visit, bounce rate, time on site, etc.) were sourced from the website platform Semrush [61], which enables the extraction of big data from corporate webpages.
- The next step involved statistical analysis using methods such as descriptive statistics, correlation, and linear regression. By analyzing the coefficients obtained, researchers can determine the impact of banking firms’ website customer data on their digital marketing and interface performance metrics, including purchase conversion, display ads, organic traffic, and bounce rate.
- After statistical analysis, a hybrid model (HM) incorporating agent-based models (ABMs) and System Dynamics (SD) was used for the simulation. The software AnyLogic (version 8.9.1) [62] was employed to create a hybrid model that simulates the relationships between the study’s dependent and independent variables over 360 days. This model aims to represent the dynamic interaction between banking firms’ website interface metrics and key metrics of their digital marketing strategies.
- The final stage included a neuromarketing approach to gain deeper insights from 26 participants who viewed the websites of the selected banking firms. They were instructed to search and observe, in 20 s, the selected banking firm websites and their provided financial products and services. Eye-tracking and heatmap analysis were conducted using the SeeSo Web Analysis platform (Eyedid SDK) [63]. This method seeks to extract additional information about the onsite activity and engagement of the participants from the qualitative methodological concept.
3.2. Fuzzy Cognitive Mapping (FCM) Framework
3.3. Research Sample
4. Results
4.1. Statistical Analysis
4.2. Simulation Model
4.3. Neuromarketing Applications
5. Discussion
6. Conclusions
6.1. Theoretical, Practical, and Managerial Implications
6.2. Future Work and Limitations
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
Java Code of AnyLogic Simulation |
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@AnyLogicInternalCodegenAPI private void enterState(statechart_state self, boolean_destination) { switch( self ) { case Potential_Bank_Customers: logToDBEnterState(statechart, self); // (Simple state (not composite)) statechart.setActiveState_xjal(Potential_Bank_Customers); transition1.start(); transition2.start(); return; case Return_Visitors: logToDBEnterState(statechart, self); // (Simple state (not composite)) statechart.setActiveState_xjal(Return_Visitors); { return_Visitors++; pages_per_Visit = normal(0.97, 3.43); visit_Duration = normal(128.25/60, 519.40/60); referral_Domains = normal(794.22, 51,181.91); email_Sources = normal(300,170.77, 184,876.14) ;} transition3.start(); transition5.start(); return; case Bounce_Rate: logToDBEnterState(statechart, self); // (Simple state (not composite)) statechart.setActiveState_xjal(Bounce_Rate); { bounce_Rate = organic_Traffic*(1.045) + paid_Costs*(0.025) + referral_Domains*(0.334) + email_Sources*(−0.043) ;} transition.start(); return; case Visitors_To_Traffic: logToDBEnterState(statechart, self); // (Simple state (not composite)) statechart.setActiveState_xjal(Visitors_To_Traffic); transition7.start(); transition8.start(); return; case Organic_Traffic: logToDBEnterState(statechart, self); // (Simple state (not composite)) statechart.setActiveState_xjal(Organic_Traffic); { organic_Costs = normal(5,822,486.64, 37,155,781.98); organic_Traffic = paid_Costs*(−0.024) + referral_Domains*(−0.319) + email_Sources*(0.041) ;} transition13.start(); return; case Display_Ads: logToDBEnterState(statechart, self); // (Simple state (not composite)) statechart.setActiveState_xjal(Display_Ads); { display_Ads = paid_Costs*(0.198) + referral_Domains*(−0.065) + email_Sources*(−0.135) ;} transition10.start(); transition11.start(); return; case Purchase_Convertion: logToDBEnterState(statechart, self); // (Simple state (not composite)) statechart.setActiveState_xjal(Purchase_Convertion); { purchase_Convertion = organic_Costs*(−1.670) + paid_Costs*(−1.369) + referral_Domains*(1.696) + email_Sources*(0.167) ;} transition9.start(); return; case Paid_Traffic: logToDBEnterState(statechart, self); // (Simple state (not composite)) statechart.setActiveState_xjal(Paid_Traffic); { paid_Costs = normal(406,005.96, 1,514,463.27); paid_Traffic = normal(666.9666, 3378.9857) ;} transition14.start(); return; case New_Visitors: logToDBEnterState(statechart, self); // (Simple state (not composite)) statechart.setActiveState_xjal(New_Visitors); { new_Visitors++; pages_per_Visit = normal(0.97, 3.43); visit_Duration = normal(128.25/60, 519.40/60); referral_Domains = normal(794.22, 51,181.91); email_Sources = normal(300,170.77, 184,876.14) ;} transition4.start(); transition6.start(); return; default: return; } } |
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Mean | Min | Max | Std. Deviation | Skewness | Kurtosis | |
---|---|---|---|---|---|---|
Organic Traffic | 9,868,004.17 | 9,486,121.00 | 10,700,067.60 | 351,366.56 | 1.342 | 1.651 |
Organic Keywords | 987,820.46 | 889,059.20 | 1,193,079.60 | 76,418.52 | 1.592 | 1.851 |
Organic Traffic Costs | 37,155,781.98 | 28,929,891.40 | 44,660,727.20 | 5,822,486.64 | −0.188 | −1.627 |
Paid Traffic | 337,898.57 | 232,588.80 | 487,373.40 | 66,696.66 | 0.396 | 1.333 |
Paid Keywords | 6510.47 | 1815.20 | 9700.60 | 2624.74 | −0.757 | −0.580 |
Paid Traffic Costs | 1,514,463.27 | 992,316.60 | 2,491,839.60 | 406,005.96 | 0.998 | 1.667 |
Email Sources | 184,876.14 | 0.00 | 720,314.00 | 300,170.77 | 1.379 | 0.219 |
Display Ads | 4199.57 | 0.00 | 20,892.00 | 7636.02 | 1.982 | 1.927 |
Purchase Conversion | 7.71 | 7.00 | 8.00 | 0.49 | −1.230 | −0.840 |
Referral Domains | 51,181.91 | 49,694.40 | 52,457.40 | 794.22 | −0.360 | −0.317 |
Visit Duration | 519.40 | 368.00 | 737.00 | 128.25 | 0.658 | −0.174 |
Bounce Rate | 0.45 | 0.42 | 0.49 | 0.02 | 0.606 | −1.361 |
Pages per Visit | 3.43 | 2.00 | 5.00 | 0.97 | 0.277 | 0.042 |
New Visitors | 15,149,188.40 | 14,150,098.00 | 16,212,804.00 | 801,388.14 | 0.025 | −1.625 |
Returning Visitors | 47,056,175.89 | 44,705,979.00 | 51,410,725.00 | 2,301,015.96 | 1.103 | 1.599 |
Organic Traffic | Organic Traffic Costs | Paid Keywords | Paid Traffic Costs | Email Sources | Display Ads | Purchase Conversion | Referral Domains | Visit Duration | Bounce Rate | Pages per Visit | New Visitors | Return Visitors | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Organic Traffic | 1 | 0.604 * | 0.264 | 0.037 | 0.174 | −0.013 | 0.619 | 0.545 | 0.529 | 0.905** | 0.068 | 0.796* | 0.469 |
Organic Traffic Costs | 0.604 * | 1 | 0.037 | 0.000 | 0.607 | 0.413 | 0.206 | 0.830 ** | 0.124 | 0.242 | 0.657 | 0.489 | 0.628 |
Paid Traffic | −0.122 | −0.052 | 0.533 | 0.889 ** | −0.220 | −0.304 | −0.521 | 0.249 | −0.705 | −0.298 | −0.022 | −0.587 | −0.539 |
Paid Traffic Costs | 0.037 | 0.000 | 0.379 | 1 | −0.371 | −0.315 | −0.547 | 0.241 | −0.549 | −0.193 | −0.070 | −0.458 | −0.524 |
Email Sources | 0.174 | 0.607 | −0.257 | −0.371 | 1 | 0.590 | 0.344 | 0.424 | 0.145 | 0.002 | 0.709 | 0.356 | 0.698 |
Display Ads | −0.013 | 0.413 | −0.456 | −0.315 | 0.590 | 1 | 0.160 | 0.299 | 0.635 | −0.316 | 0.843 * | 0.554 | 0.857 * |
Purchase Conversion | 0.619 | 0.206 | −0.555 | −0.547 | 0.344 | 0.160 | 1 | 0.175 | 0.224 | 0.600 | 0.300 | 0.539 | 0.485 |
Referral Domains | 0.545 | 0.830 ** | 0.249 | 0.241 | 0.424 | 0.299 | 0.175 | 1 | −0.223 | 0.179 | 0.737 * | 0.269 | 0.394 |
Visit Duration | 0.529 | 0.124 | −0.748 | −0.549 | 0.145 | 0.635 | 0.224 | −0.223 | 1 | 0.163 | 0.309 | 0.804 * | 0.717 |
Bounce Rate | 0.905 ** | 0.242 | −0.542 | −0.193 | 0.002 | −0.316 | 0.600 | 0.179 | 0.163 | 1 | −0.051 | 0.581 | 0.192 |
Pages per Visit | 0.068 | 0.657 | −0.410 | −0.070 | 0.709 | 0.843 * | 0.300 | 0.737 * | 0.309 | −0.051 | 1 | 0.558 | 0.830 * |
New Visitors | 0.796 * | 0.489 | −0.904 ** | −0.458 | 0.356 | 0.554 | 0.539 | 0.269 | 0.804 * | 0.581 | 0.558 | 1 | 0.856 * |
Returning Visitors | 0.469 | 0.628 | −0.773 * | −0.524 | 0.698 | 0.857 * | 0.485 | 0.394 | 0.717 | 0.192 | 0.830 * | 0.856 * | 1 |
Variables | Standardized Coefficient | R2 | F | p-Value |
---|---|---|---|---|
Organic Traffic Costs | −1.670 | 1.000 | - | 0.000 ** |
Paid Traffic Costs | −1.369 | 0.000 ** | ||
Referral Domains | 1.696 | 0.000 ** | ||
Email Sources | 0.167 | 0.000 ** |
Variables | Standardized Coefficient | R2 | F | p-Value |
---|---|---|---|---|
Paid Traffic Costs | 0.198 | 1.000 | - | 0.000 ** |
Referral Domains | −0.065 | 0.000 ** | ||
Email Sources | −0.135 | 0.000 ** |
Variables | Standardized Coefficient | R2 | F | p-Value |
---|---|---|---|---|
Paid Traffic Costs | −0.024 | 1.000 | - | 0.000 ** |
Referral Domains | −0.319 | 0.000 ** | ||
Email Sources | 0.041 | 0.000 ** |
Variables | Standardized Coefficient | R2 | F | p-Value |
---|---|---|---|---|
Paid Traffic Costs | 0.025 | 0.000 ** | ||
Referral Domains | 0.334 | 0.000 ** | ||
Email Sources | −0.043 | 0.000 ** |
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Share and Cite
Giannakopoulos, N.T.; Sakas, D.P.; Migkos, S.P. Neuromarketing and Big Data Analysis of Banking Firms’ Website Interfaces and Performance. Electronics 2024, 13, 3256. https://doi.org/10.3390/electronics13163256
Giannakopoulos NT, Sakas DP, Migkos SP. Neuromarketing and Big Data Analysis of Banking Firms’ Website Interfaces and Performance. Electronics. 2024; 13(16):3256. https://doi.org/10.3390/electronics13163256
Chicago/Turabian StyleGiannakopoulos, Nikolaos T., Damianos P. Sakas, and Stavros P. Migkos. 2024. "Neuromarketing and Big Data Analysis of Banking Firms’ Website Interfaces and Performance" Electronics 13, no. 16: 3256. https://doi.org/10.3390/electronics13163256