‘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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".