Rate the quality of the fallback the engagement has ready when a critical third-party feed, platform, or model stops working

The mean rating was 4.6 and the median 5.0 (n = 802 respondents). Ratings of 3 or below, the distressed range, made up 32.04% of the answers. The rating was associated with all four study measures. Higher ratings came with less cancellation (r = −0.64, 95% CI −0.73 to −0.54, p < 0.001, n = 150 engagements), higher customer satisfaction among active engagements (r = 0.69, 95% CI 0.57 to 0.79, p < 0.001, n = 94 engagements), a higher engagement indicator mean computed without the indicator (r = 0.87, 95% CI 0.83 to 0.91, p < 0.001, n = 150 engagements) and lower intent to leave (r = −0.66, cluster bootstrap 95% CI −0.72 to −0.59, p < 0.001 with standard errors clustered by engagement, n = 753 respondents). After Holm correction, ratings differed by engagement status and lifecycle stage (Welch F 38.74, Holm p < 0.001, omega squared 0.402), respondent role (Welch F 9.99, Holm p < 0.001, omega squared 0.020) and vendor type (Welch F 3.62, Holm p = 0.018, omega squared 0.017).

All 80 written responses came from C198, the comment field on the third-party ecosystem. Fifty-seven were negative and twenty-one positive, and forty-six carried the strongest negative score of −10. Twenty-five responses said there was nothing behind a critical feed or model, so the work stopped and waited. The positive responses described fallbacks the teams had built themselves, usually a saved copy or a manual route, and a few tested secondary sources. Respondents whose written responses were more negative reported higher intent to leave.

Ratings of 3 or below, the distressed range, accounted for 257 of the 802 answers (32.04%). The engagement mean fell in the distressed range in 39 of 150 engagements.

The primary responses numbered 80, all written in answer to C198, “Add any comments on the third-party ecosystem that the ratings in this section do not capture.” No response from any other question addressed the indicator, so every response was primary.

Twenty-five responses said there was no fallback for a critical feed, platform or model, and that the work stopped or waited when it failed: a bureau feed down for two days with the validation queue idle and no one to call, an intelligence feed down for a full shift with triage done from the console by hand, a half-day model outage that stopped supplier risk scoring, a single cloud region whose loss would stop settlement. Four contrasted this with earlier engagements where an old macro or manual checklist had remained. Five said the people or skills for manual work were gone: in one account the financial institution released its own service desk staff in the third month, and when the platform was down for most of a day no one left knew how to open a ticket by hand. Three said the fallback had never been tested, one adding that the ratings were high only because nothing had yet broken. Two described a quieter failure: when a price feed stopped, the agent kept answering with yesterday's figure and nothing raised an alarm.

Thirteen responses described a fallback the team maintained itself: last month's file or yesterday's price file kept for a rebuild, the leavers list compared by hand each morning, a printed copy of the twenty most common answers, and a manual payment submission run once a week to keep the skill alive, a practice the respondent said had saved the team twice that year. Five described a secondary route that had been tested or used, such as a second sanctions list reached within the hour and practised twice, a standby identity route, and a tested routing to a human queue when the model endpoint dropped.

Of the 80 responses, 57 (71.25%, 95% CI 59.49% to 82.35%) were negative and 46 (57.50%) carried the strongest negative score of −10. Positive responses numbered 21 (26.25%), and 2 were neutral. The mean written score was −4.9 (95% CI −6.6 to −3.1, n = 80 responses), and the median was −10.0.

Of the 80 responses, 76 came from respondents who also rated the indicator. Among them, the written score rose with the rating (Spearman rho 0.55, 95% CI 0.33 to 0.73, p < 0.001, n = 76 responses). The written responses corroborated the rating. Of the 55 negative responses with a rating, 28 (50.91%) came from respondents whose rating was in the distressed range.

Respondents who wrote about the indicator and gave a rating numbered 76, with a mean rating of 4.3. The 726 respondents who rated the indicator without writing about it had a mean of 4.6. The difference, −0.3 (95% CI −0.9 to 0.3), included zero. Ratings in the distressed range made up 40.79% of the ratings from respondents who wrote (31 of 76) and 31.13% of the ratings from respondents who did not (226 of 726).

Every figure in the segment tables is at respondent level. Segments with fewer than 30 respondents carry no interval. Segments with fewer than 10 report counts only.

Segment families recorded at engagement level were tested on engagement means. Respondent role and vendor type were tested on respondents. Where an expected count fell below 5, the test of the distressed share used Fisher's exact test for a two-by-two table, marked exact, or a Monte Carlo test, marked simulated. After Holm correction, ratings differed by engagement status and lifecycle stage (Welch F 38.74, Holm p < 0.001, omega squared 0.402). Active engagements in Transition/Cut-Over rated highest (mean 5.6, n = 231 respondents) and canceled engagements lowest (mean 3.1, n = 301 respondents). Ratings also differed by respondent role (Welch F 9.99, Holm p < 0.001, omega squared 0.020). Portfolio directors rated highest (mean 5.8, n = 31 respondents) and individual contributors lowest (mean 4.4, n = 620 respondents). Ratings also differed by vendor type (Welch F 3.62, Holm p = 0.018, omega squared 0.017). Boutique or specialist providers rated highest (mean 5.2, n = 80 respondents) and BPO specialists lowest (mean 4.2, n = 189 respondents). Outsourcing model, institution type, process family and class of work showed no difference.

Written sentiment did not differ by engagement status and lifecycle stage across the 62 engagements with a written response (Kruskal-Wallis H 2.59, df 3, p = 0.460). Of the groups with 10 or more responses, responses from canceled engagements were the most negative (mean −6.7, 33 responses) and responses from active engagements in Production the least negative (mean −1.4, 11 responses).

No test across respondent roles was run, because fewer than two roles had 30 or more written responses. Individual contributors wrote 52, engagement managers wrote 22 and portfolio directors wrote 6 responses.

Cancellation rate is the share of engagements canceled by the study date, measured at engagement level (150 engagements, 56 canceled). Canceled engagements were rated after cancellation. The association is therefore a descriptive contrast and not a forecast of cancellation.

Across the study set, higher ratings came with less cancellation (r = −0.64, 95% CI −0.73 to −0.54, Holm p < 0.001, n = 150 engagements). The association held after Holm correction in fourteen of the 20 segment levels tested.

Customer satisfaction is the percentage recorded against each engagement. The primary estimate uses the 94 active engagements. A descriptive contrast uses all 150 engagements.

Across the active engagements, higher ratings came with higher customer satisfaction (r = 0.69, 95% CI 0.57 to 0.79, Holm p < 0.001, n = 94 engagements). The association held after Holm correction in fifteen of the 16 segment levels tested.

The engagement indicator mean is the mean of an engagement's ratings across the rated indicators, measured at engagement level (150 engagements). The indicator is one of the rated indicators. Each comparison therefore uses the mean recomputed without it.

Higher ratings came with a higher engagement indicator mean (r = 0.87, 95% CI 0.83 to 0.91, Holm p < 0.001, n = 150 engagements). The association held after Holm correction in twenty-four of the 24 segment levels tested.

Intent to leave is the answer to C019, “How likely are you to leave your current job in the next 12 months?”, rated 1 to 10. It is measured at respondent level on up to 798 respondents, with the engagement-level form reported alongside.

Respondents who gave higher ratings reported lower intent to leave (r = −0.66, 95% CI −0.70 to −0.61, Holm p < 0.001, n = 753 respondents). The association held after Holm correction in thirty-six of the 37 segment levels tested.

Two comparisons set the engagements with at least one negative response against all other engagements. Of the 105 other engagements, 88 had no written response addressing the indicator. These two comparisons therefore do not isolate written sentiment. Of the 45 engagements with a negative response, 19 (42.22%) were canceled, against 37 of the 105 other engagements (35.24%). The difference did not reach significance (Fisher exact p = 0.463). Customer satisfaction among active engagements did not differ between the two groups (mean 66.00% on 26 engagements against 63.37% on 68, Welch t 1.06, p = 0.294).

Two correlations test written sentiment directly. Across the 62 engagements with a written response, the engagement mean written score was not associated with cancellation (point-biserial r −0.17, p = 0.194). Across 76 respondents, a more negative respondent mean written score came with higher intent to leave (Spearman rho −0.33, p = 0.004). Of the four comparisons, one reached significance at 0.05.

The four associations were compared in pairs on their engagement-level forms, each oriented so that a higher value is the better outcome, across the 150 engagements with all four measures. After Holm correction, five of the six pairs differed. The association with the engagement indicator mean was the strongest and the association with cancellation the weakest. The associations with customer satisfaction and intent to leave did not differ (Holm p = 0.771).

Answers clustered within engagements (ICC 0.67). Clustering reduced the 802 respondent answers to the equivalent of about 205 independent answers. Respondent-level intervals therefore used the cluster bootstrap. Respondent-level tests of intent to leave used standard errors that allow for clustering by engagement.

No association was equivalent to zero. Each of the four differed from zero. The detection floor shown is the study floor at 150 engagements. Intent to leave was tested on its effective sample size.

Each change was computed before rounding and may differ by 0.01 from the difference between the two printed values.

All 22 checks held. A check holds when its test falls on the same side of 0.05 as the primary test. Where the primary test falls below 0.05, the check must also keep the sign of the primary correlation. The thin-base check removes the engagements in any level of outsourcing model or process family with fewer than 30 engagements. Institution type is not used for that check, because every one of its levels has fewer than 30 engagements.

Each of the four measures was computed from data the indicator does not contain. Each result is therefore a finding and not an arithmetic consequence. The indicator is one of the rated indicators, and the engagement indicator mean was recomputed without it. The correction changed r by 0.00, from 0.88 to 0.87.

Each deviation was computed before rounding and may differ by 0.01 from the difference between the two printed values.

The comparison covered 150 engagements. The indicator followed the study's typical pattern on all four measures, and no deviation held after Holm correction.

Among the 71 Part 2 indicators with 30 or more written responses, the indicator's share of negative responses, 71.25%, ranked 57th from the highest, against a median of 80.82%. Its mean written score, −4.9, ranked 31st from the most negative, against a median of −4.7.

The indicator most often present in the same responses was C190, “Rate the engagement's ability to still perform by hand the manual workarounds that once absorbed third-party quirks and breakage.”, in 25 of the 80 responses (31.25%). C181, “Rate the quality of advance notice third-party providers give before changes that can affect the agents on this engagement.” (21 responses), C178, “Rate how well the agents on this engagement are protected from breaking silently when a third party changes something without warning.” (19 responses), C187, “Rate the stability of the third-party APIs and data structures the agents depend on, from one provider release to the next.” (16 responses) and C193, “Rate how well the engagement manages its dependence on a small number of model and cloud providers.” (13 responses) followed.

The most frequent theme was “Third-party work done by hand”, in 18 responses (22.50%). The second was “Detection only by failure”, in 18 responses.

About a third of all ratings of the fallback ready for a critical third-party failure fell in the distressed range (257 of 802 answers, 32.04%). The rating was associated with all four study measures, and every association held under each of the 22 robustness checks. Canceled engagements rated far below the three active stages (mean 3.1, n = 301 respondents).

The written evidence shows that the default fallback was the manual work the agents had replaced, and that it was eroding as the people who knew it left or were released. Where teams kept a copy, a manual channel or a tested secondary route, an outage became an inconvenience, and where they did not, the work stopped. Written sentiment was associated with intent to leave.

The evidence is the reported experience of the vendor's delivery teams, collected in a single cross-section. The associations reported here are not causal.

Tests by respondent role and by vendor type treated respondents as independent. The section on clustering reports how far clustering within engagements reduced the information in respondent-level figures.

Customer management executives gave 1 answers, a base of anecdote strength.

Shares of written sentiment are shares of responses and not of the study set of respondents. The written score takes five fixed values, so its mean and median are coarse. Responses clustered within engagements (80 responses on 62 engagements). Intervals for written sentiment therefore used a bootstrap by engagement.