TRANSLATING BUSINESS INTELLIGENCE INSIGHTS INTO GROWTH: A FRAMEWORK FOR BANK PRODUCT MANAGERS
Bibliographic record
Abstract
This study explores how business intelligence insights contribute to business growth in the Nigerian commercial banking sector. Business intelligence is increasingly adopted by Nigerian banks to gain a competitive edge; however, many financial institutions struggle to fully leverage their business intelligence systems due to technological and operational challenges. This study adopted a qualitative research design to examine how business intelligence insights influence key growth indicators, including customer satisfaction, customer loyalty, and internal process efficiency for bank product managers using a hybrid of technology organisation environment and dynamic capability theory. A thematic analysis method was used to analyse the information gathered from respondents and it was found that customers and employees sometimes experience issues when using the digital banking channels provided by the Nigerian banks. Also, there is no effective use of the information provided to the bank by consumers This study concludes that consumers and employees of Nigerian banks can have better experiences when using the digital banking channels provided if there can be improvement in operational activities through the use of predictive analysis to get real time information. This study contributes to previous research in business intelligence by examining not just the adoption of business intelligence tools but the use of business intelligence tools for building better relationship with consumers and enhancing operational effectiveness in the Nigerian banking industry. It was recommended that banks should have a single profile that analyse the information gotten from consumers, develop young talents to solve their technological issues, make use of predictive analytics to examine customers profile and provide personalised information tailored to customer needs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".