AI-Driven Analytics: Transforming Customer Engagement and Marketing in Retail Finance

The current trend in the financial services industry is using AI technology, which enhances the services provided. Rivalry within the industry forces companies to seek more efficient marketing strategies while maintaining decent customer relationships as they shift their focus from traditional to modern strategies. Investments in AI technologies enhance the overall customer experience as innovations improve web engagement and customer targeting. These strategies shift the attribution multi-touch analysis (MTA) and market mix modeling (MMM). This article looks at these applications of AI technologies through the lens of the latest studies and real-life situations and their impacts on the future.

AI-Powered Customer Segmentation: Hyper-segmentation Strategy 
AI developments have changed customer segmentation in retail finance, as demographic information and transaction history are integrated into one model in real-time. Rather than focusing solely on the few motives and needs these AI tools can generate, we now have the opportunity to perform algorithmic segmentation, which can analyze behavioral patterns that would have been difficult for a human to understand.

According to Maree and Omlin (2021), the behavior of customers over the period can be tracked, and micro-segments of customers can be created using recurrent neural networks (RNNs). Maree and Omlin state that they recognize specific customer segments through extremely fine modifications in their purchasing behaviour patterns, which is more advanced targeting of customers [https://openreview.net/forum?id=AXXohj2qWlw]. The work of Thiel and Raaij (2017), who conducted psychographic segmentation for robot assistants in the finance industry. They presented compelling arguments, emphasizing "financial literacy" and "rigid personality" as crucial psychological traits that enabled them to offer various forms of actionable financial advice [https://ideas.repec.org/a/ris/jofitr/1613.html]. Thanks to these AI-enhanced micro-segmentation approaches, transaction turnover has drastically increased while customer satisfaction has risen to new heights. For example, if banks offer individualized investment plans to customers whom they estimate to be risk-averse, they may also offer aggressive marketing campaigns to customers classified as hyperactive. Marketing studies show that increased precision in targeting improves engagement & true brand loyalty [https://journals.sagepub.com/doi/10.1177/0008125619859317]

Enhancing Clients Acquisition: AI-Powered Clickstream Analytics

When it comes to the world of e-banking, the involvement of consumers of the website greatly affects the survival or collapse of the businesses. In case web analytics are to add value and make sense, it is crucial to have web clickstream data, a scenario whereby the AI takes a series of user interactions and uses Long Short Term Memory (LSTM) to explain the set of activities that tell you the combination of operations that lead to completing or abandoning a task.

A study by Carmichael et al. (2018) found that using clickstream to segment retail bank members increased conversion and recommended a 15-20% increase. Through clickstream patterns, the bank has pinpointed critical touchpoints on the journey where users tend to abandon their tasks and, therefore, modified the type of interface they display to mitigate this problem [https://www.semanticscholar.org/paper/Data-driven-segmentation-of-consumers%27-purchase-in-Carmichael-Chen/479fb75bd22b6aeee3475ecae31aea2e986441f3]
A different view of a possible evolution is the unification of an intent-based clickstream model with social networks and external advertisement. Take a case, for example, of a person that goes online to read materials so that he is able to increase his understanding of investments before purchasing an investment service. Introducing the scoring system would encourage users to go back to the site and thus improve the average retention rate.

Multi-Touch Attribution (MTA): Effective Channel Attribution Metrics 
In Multi-Touch Attribution, the focus is on the entire ecosystem of marketing for each of the customer activities that they perform in the marketing mix. For instance, the user having a presence on social media or visiting the firm’s website directly. AI has started to work on cross-channel interaction issues, such as MTA. This should get better as AI gets better at ML techniques like XGBoost, which look at the "non-linear" effects of all the touchpoints when deciding if a sale or conversion happened.

According to Wani et al. (2023), there was enhanced banking attribution owing to AI-MTA, whereby the banking channel attributions increased to fifty percent, paving the way for banks to understand the value each channel adds towards converting customers. The study demonstrated how marketers can use opportunistic AI to target key channels and effectively use budgets [https://typeset.io/papers/unleashing-customer-insights-segmentation-through-machine-1e4apwb2qa]. In contrast, multi-touch attribution based on AI models paints a more revolutionary understanding of the topic. By the use of AI, it is possible to adjust the budget of the marketing campaign expected at each touchpoint of the customer journey. For instance, if AI detects an increase in users of a company’s mobile application, it is then compelled to increase the advertisement allocation of the app. 

Market Mix Modeling (MMM): Artificial Intelligence Based Advertisement Optimization 

Some may argue that market mix modeling, or MMM, is regarded as the most effective way of measuring the outputs of the market involved. However, presently, with the help of AI, MMM is no longer passive. It enables the managers to assess the marketing effectiveness of marketing outlays in real time; therefore, they focus on returns in ad spending not only in the creative advertising aspect but also in external influences like seasonality, the economy at the time, and even competitive behavior.

Hossain et al. (2022) showed in their research that financial marketers using AI-based MMM can enhance budget efficacy by up to 50%. The research used a machine learning technique to estimate the potential expenditure for advertising by allowing banks to spend increasing amounts of money influenced by market trends [https://pure.psu.edu/en/publications/operationalizing-artificial-intelligence-enabled-customer-analyti]. We expect future MMM models based on AI to apply reinforcement learning algorithms for more sophisticated budget spending optimization. This means that the marketing team will be able to update the spending according to changes in customers' sentiments or the current market condition. Therefore, the marketing team will consistently allocate resources to the most promising channels.

Case Study: AI-Enhanced Segmentation in the P2P Lending Market
The second area of AI development which transforms financial services in India is the peer-to-peer lending market. A study constructive case by Anil and Misra (2022) encompassed a total of six licensed NBFC P2P lenders in India. They demonstrated how AI-enhanced customer segmentation resulted in a change in their operational and marketing performance. Employing machine learning techniques that analyze the behavior of borrowers, these P2P lenders could fashion intricate profiles of the borrowers, which increased the targeting of the loan offers and minimized the chances of default [https://ideas.repec.org/a/eme/ijoemp/ijoem-05-2021-0822.html].

This case study shows how AI can not only allow lenders to have more accurate assessments of the risk taken while rendering the loan but can also help them focus on customers more accurately. These P2P lenders could also economize loan production by tailoring to the specific needs and financial behavior of the borrowers, thereby strengthening the relationship with them and enhancing the retention rate. Hence, these P2P lenders can set competitive rates for borrowers with sound credit risk evaluations and convert less risk averse borrowers into small loans.

Future Directions: The Importance of Emotion AI and Quantum Computing 

With the progression of the analytical tools particular to the retail finance sector, some trends are likely to appear that would raise its opportunities: 

 1. Quantum Computation: An artificial intelligence powered quantum driven computational model has the potential to completely change the analytic side of AI by removing the problem of large data set processing. The combination of AI and Quantum opens the doors for never-ending possibilities in marketing through nanosegmentation, which allows for the targeting of marketing by utilizing global real-time data.

2. Enhanced AI and Emotion AI: AI Emotion offers us limitless opportunities. Imagine an AI’s capability that is able to analyze customer’s tone and the degree of emotions as reflected in their physiology. This would enable the system to determine how that individual customer is feeling during the time of that particular interaction. In this way, we can aim to serve our customers dynamically. For instance, we can automatically involve a customer if they appear to be experiencing frustration during the mortgage application process.

3. XAI: The term "XAI" is likely to gain popularity due to the increasing regulations surrounding the derivation of insights from AI. XAI offers banks a means to limit risk and explain the use of AI in their work. This is critical in the financial industry, since banking undertakings are fanatical regarding the observance of regulations and do not have the level of transparency required. If a particular segment is the focus of the marketing campaign, XAI needs to answer that question.

The use of AI-based analytics solutions can assist in radically altering the classical regulations observed in the retail space of finance, which seeks to provide the right information to the right customer at the right time, which means more touches, efficient cost management, and income generation management. With the help of such methods as customer stratification and targeting, clickstream analytics, multi-touch attribution, or marketing mix modeling, companies can gain insights about their consumers and swiftly match their behavioral patterns. Quantum computing and emotional AI are only one of several future directions and innovations for human-computer interaction into a personalized and efficient form for consumers of retail finance strategic marketing. On the other hand, financial companies that make such investments today will dominate global markets tomorrow. Technology will permanently bridge the gap between customers, bringing them closer than ever before and paving the way for future digital opportunities.

References:
Maree, C., & Omlin, C. (2021). Discovering Novel Customer Features with Recurrent Neural Networks for Personality-Based Financial Services. ArXiv

Thiel, D. V. & Raaij, W. F. V. (2017). The study focuses on targeting the robo-advice customer through the development of a psychographic segmentation model for financial advice robots. Journal of Financial Transformation, 88-101.

Hossain, M. A., Akter, S., Yanamandram, V., & Gunasekaran, A. (2022). Operationalizing artificial intelligence-enabled customer analytics capability in retailing. Journal of Global Information Management.

Anil, K., & Misra, A. (2022). The article "Artificial Intelligence in Peer-to-Peer Lending in India: A Cross-Case Analysis" was published in the International Journal of Emerging Markets in 2022. 

Wani, A., Priyanka, M., & Prasath, R. (2023). Unleashing Customer Insights: Segmentation Through Machine Learning. 2023 World Conference on Communication & Computing (WCONF), pp. 1-5

Kumar, V., Rajan, B., & Lecinski, J. (2019). Understanding the Role of Artificial Intelligence in Personalized Engagement Marketing. California Management Review, 61(4).

Carmichael, G. B., Chen, Y., & Luo, C. (2018). Data-driven segmentation of consumers' purchase behaviour in the retail industry. 2018 4th International Conference on Information Management (ICIM).