Apple Podcasts  ·  Spotify  ·  Buzzsprout

Paper A

The adaptive engagement framework: enhancing banking customer experience through AI-powered invisible marketing

Ali Shahbazi, Sepehr Behtaji, Amir Hossein Tajiki, Nefise ŞİRZAD et al. — 2026 — Scientific Reports

peer reviewed journal article  ·   ·  read now

Key findings

• The two strongest signals for predicting whether a customer will engage with a bank are simple: how long their phone calls last and how much money they have in their account. These two factors nearly perfectly predicted which customers were 'high interaction' vs. 'low interaction'.
• A standard Random Forest model — a well-established machine learning approach — correctly classified customers as high or low interaction about 97% of the time, and almost never missed a high-interaction customer (recall of 99.9%).
• The researchers' new deep learning model (RDAD-CNN) pushed accuracy to about 99%, outperforming all other models tested. The improvement was statistically meaningful, not just random noise.
• The study argues that using behavioral signals (how you act) rather than demographic data (who you are — age, gender, etc.) is both more accurate and more ethically sound for targeting marketing messages in banking.

Marketing implications

• If you work in financial services marketing, prioritize call-duration data and account balance when building customer segments for outreach — these two signals alone are extraordinarily predictive of who will actually engage.
• Instead of blasting promotional messages to all customers, build a simple classifier (even a Random Forest is 97% accurate here) to identify your high-engagement customers first, then focus your budget and energy on them.
• Design product recommendations to feel like helpful service features rather than ads — if customers don't notice they're being marketed to, they're less likely to tune out or feel annoyed.

Paper B

Design and implementation of generative Artificial Intelligence–driven automation for enterprise customer relationship management decision support systems

Fahad Khayyam — 2026 — Global Journal of Engineering and Technology Advances

peer reviewed journal article  ·   ·  test this week

Key findings

• The paper proposes a blueprint (called GAI-CRM DSS) for plugging generative AI — specifically large language models — into a company's CRM software to automate tasks like predicting which customers might leave, grouping customers by behavior, and auto-generating responses to customer inquiries.
• In simulated test scenarios, the framework showed improvements in how accurately the system made decisions, how fast operations ran, and how quickly customer service could respond — though these are not results from real companies.
• The framework includes a built-in 'explainability' layer, meaning the AI is designed to show managers why it made a particular recommendation rather than just producing a black-box answer.
• The architecture is designed around cloud-based microservices, meaning companies could scale it up or down without rebuilding it from scratch, and it includes data privacy and access controls to meet compliance requirements.

Marketing implications

• This paper is a design proposal, not a tested solution — do not implement or budget around it. Treat it as a conceptual checklist of what a GenAI-enhanced CRM could theoretically do.
• If you are evaluating AI add-ons for your CRM, the framework's components (sentiment analysis, churn prediction, explainable recommendations, role-based access controls) are useful as a feature wishlist to ask vendors about.
• If your team is building internal AI tooling on top of CRM data, this paper outlines a plausible architecture — but validate each component with real data before assuming it works as described.

Paper C

Digital Commerce in the AI Era: Opportunities and Challenges

Vallabhadas Kalpana, P. Anupama, O. Padmaja — 2026 — International Journal of Emerging Research in Science Engineering and Management

peer reviewed journal article  ·   ·  watchlist

Key findings

• AI tools like recommendation engines, chatbots, and predictive analytics help online businesses serve customers more personally, catch fraud faster, and run their operations more smoothly — based on a review of existing research, not a new experiment.
• AI-powered product recommendations show customers items they are likely to want based on their browsing and purchase history, which multiple reviewed studies link to higher customer satisfaction and more sales.
• Human-AI collaboration in digital advertising — where AI handles targeting and personalization while humans guide strategy — appears to improve how customers respond to ads, according to one reviewed study.
• Significant barriers to AI adoption remain, including the high cost of building AI systems, risks of data privacy violations, potential bias in AI decision-making, and the threat of job losses in commerce-related roles.

Marketing implications

• If you run an e-commerce site and haven't tried AI product recommendations yet, this review confirms you're behind — try a tool like Barilliance, Nosto, or even built-in Shopify recommendations and measure whether average order value goes up.
• If you manage digital ads, look for platforms that let AI handle audience targeting and timing while you control the creative message — this split approach (human strategy + AI execution) is what the reviewed research points to as most effective.
• Be upfront with your customers about how you use their data for personalization — the review highlights that data privacy concerns are a real barrier to consumer trust, and transparency can be a competitive advantage.

Apple Podcasts  ·  Spotify  ·  Buzzsprout