‘Build it and they will come’ is a myth: Building a digital activation strategy
Bibliographic record
Abstract
Although the Royal Bank of Canada (RBC) is a worldwide leader in innovative mobile payment technology, front-line employees had never been trained on mobile capabilities. Agile development had taken root, and a deep series of agile product roadmap releases were set to come fast and furious for the foreseeable future. There was a need for a new methodology to support the bank’s digital fluency in what would be very rapid digital transformation. There was no method to accommodate just-in-time learning, let alone learning that was experiential and could take advantage of the latest in cognitive behavioural science. In this case study, readers will learn: 1) How RBC implemented a Celent Model Bank Award-winning digital activation strategy that may be the first financial industry mobile activation strategy in North America; 2) Why being a large organisation is no longer an excuse for inaction; 3) How to develop a model for digital activation that is straightforward for almost any organisation to adopt; 4) A way to quickly build an enterprise-wide interactive learning platform for employees and clients.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".