I know the KPIs the customer uses to measure success

Distressed ratings on knowing the KPIs the financial institution uses to measure success came from a minority of answers (226 of 787 answers, 28.72%). Engagements whose respondents reported weaker knowledge of those KPIs were canceled more often (r = −0.64, 95% CI −0.72 to −0.53, p < 0.001, n = 150 engagements). Stronger KPI knowledge came with higher customer satisfaction on active engagements (r = 0.65, 95% CI 0.51 to 0.75, p < 0.001, n = 94 active engagements). Ratings rose with the engagement indicator mean (r = 0.81, 95% CI 0.75 to 0.86, p < 0.001, n = 150 engagements). Respondents who reported weaker KPI knowledge reported higher intent to leave (r = −0.63, cluster bootstrap 95% CI −0.69 to −0.57, p < 0.001, n = 738 respondents). Ratings differed after Holm control by engagement status and lifecycle stage (Welch F 38.10, Holm p < 0.001, omega squared 0.397), by respondent role (Welch F 8.52, Holm p 0.003, omega squared 0.016), and by vendor type (Welch F 3.59, Holm p 0.019, omega squared 0.017).

One written response was coded to the indicator, from 1 of 850 respondents on 1 of 150 engagements. The single scored response was negative, with a score of −10. It was a secondary response, written in answer to a question outside the indicator’s own survey section. The rating of respondents who wrote was not compared with that of respondents who did not, because one respondent wrote.

The study set mean was 4.8 on 787 answering respondents, with a median of 5.0. 226 answers, 28.72% of the 787, were at the distressed end. A distressed engagement mean was recorded in 30 of 150 engagements.

The single response coded to the indicator was a secondary response. The response was written in answer to C114, a question outside the indicator’s own survey section.

The distribution was reported as counts only, because fewer than 10 responses carried a score. The single scored response carried a score of −10, the strongest negative score.

Figures are respondent level. Means are on the goodness view. A level under 30 prints no interval and a level under 10 prints counts only.

Engagement families were tested on engagement means, and respondent role and vendor type were tested on respondents. A distressed p marked exact is a Fisher exact test for a two by two table, and one marked simulated is a Monte Carlo test run until its p is stable to the third decimal, both used where an expected count is under 5. Engagement status and lifecycle stage differed after Holm control (Welch F 38.10, Holm p < 0.001, tested on engagement means). In Descriptive Statistics by Segment, Active, Production had the highest mean, 5.9 (n = 95 respondents), and Canceled the lowest, 3.4 (n = 296 respondents). Respondent role differed after Holm control (Welch F 8.52, Holm p 0.003, tested on respondents). In Descriptive Statistics by Segment, Portfolio Director had the highest mean, 6.1 (n = 26 respondents), and Individual Contributor the lowest, 4.6 (n = 609 respondents). Vendor type differed after Holm control (Welch F 3.59, Holm p 0.019, tested on respondents). In Descriptive Statistics by Segment, Consulting firm had the highest mean, 5.1 (n = 105 respondents), and Not reported, recovered by engagement the lowest, 4.3 (n = 54 respondents). No difference held after Holm control for outsourcing model, institution type, process family and class of work.

The share of engagements canceled on the study date, measured at engagement level (base 150 engagements, 56 canceled). The association is a descriptive contrast, inflated because canceled engagements were rated after cancellation, and is not predictive.

The study set r was −0.64 (95% CI −0.72 to −0.53, Holm p < 0.001, n = 150 engagements). Of the 20 tested segment levels, 13 held after Holm control.

The satisfaction score recorded against each engagement on a 100-point scale. The primary estimate uses the 94 active engagements. A descriptive contrast uses all 150 engagements.

The study set r was 0.65 (95% CI 0.51 to 0.75, Holm p < 0.001, n = 94 engagements). Of the 16 tested segment levels, 15 held after Holm control.

The mean of an engagement’s 100 indicator ratings, measured at engagement level (base 150 engagements). Where this indicator is one of the 100, the mean is recomputed without it.

The study set r was 0.81 (95% CI 0.75 to 0.86, Holm p < 0.001, n = 150 engagements). Of the 24 tested segment levels, 24 held after Holm control.

The respondent’s stated likelihood of leaving the current job within 12 months, rated 1 to 10 (C019). Measured at respondent level on up to 798 respondents, with the engagement-level form reported alongside.

The study set r was −0.63 (95% CI −0.67 to −0.59, Holm p < 0.001, n = 738 respondents). Of the 37 tested segment levels, 36 held after Holm control.

Pairs were compared on the engagement level form of every measure, each turned so that a higher value was the better outcome, on the 150 engagements where the indicator and all four measures existed. Of the 6 pairs, 4 differed after Holm control.

Clustering reduced the 787 respondent answers to about 218 independent answers. Respondent level intervals therefore used the cluster bootstrap, and respondent level tests on intent to leave used cluster-robust errors.

No measure showed a tested null. No result was inconclusive. The detection floor column is the study floor at 150 engagements. Intent to leave was tested on its effective n.

Each change was computed before rounding, so it can differ by 0.01 from the difference of the two printed values.

Every check held. A check holds when its test lands on the same side of 0.05 as the primary test and, where the primary test is below 0.05, its r keeps the primary sign. Thin-base rule: engagements in any level under 30 engagements of outsourcing model or process family removed. Institution type is not used, because every one of its levels is under 30.

Each of the four measures was computed from data this indicator does not contain, so each result is a finding rather than an arithmetic consequence.

Each deviation was computed before rounding, so it can differ by 0.01 from the difference of the two printed values.

The comparison ran on 150 engagements. The indicator followed the study’s typical pattern on every measure.

The response coded to the indicator was also coded to 7 other indicators.

Portfolio Director respondents reported stronger KPI knowledge (mean 6.1, n = 26 respondents) than Individual Contributor respondents did (mean 4.6, n = 609 respondents). Ratings were lowest on canceled engagements (mean 3.4, n = 296 respondents) and highest at Active, Production (mean 5.9, n = 95 respondents). The associations with the four study measures held under every robustness check run on this item (22 of 22 checks).

The written responses were not set against the rating, because fewer than 10 responses carried a score. Written responses were voluntary. They recorded what the respondents who wrote reported, and how common it was is measured by the ratings. The analysis of the written responses rested on 1 response, a base of anecdote strength.

The evidence is reported experience: what the delivery team said about its own engagement, gathered in one cross-section. The figures showed associations, not causes. Indicators about the financial institution were reported at second hand by vendor staff. Cancellation is a descriptive contrast, because canceled engagements were rated after they ended.

Tests across respondent role and vendor type were run at respondent level and treated respondents as independent. The section Clustering and Effective Sample Size states whether, and by how much, clustering within engagements reduced the information in respondent level figures.

Customer Management Executive answers were at anecdote strength (n = 2). Answers clustered within engagements, which reduced the effective number of independent answers (ICC 0.61, 787 answers worth about 218 independent answers).

Fewer than 10 responses carried a score, so written sentiment was reported as counts, with no statistic. Coding was performed by AI coders under a fixed rulebook. Agreement on whether a response was coded to the indicator rested on 1 response second-coded under rulebook 0.5, with the coders agreeing on 0 of 1. No response coded to the indicator was second-scored under rulebook 0.5, so agreement on the score could not be assessed. This base of second-coded responses is too small for an indicator-level alpha.