MT221 Unit 8 Service Metrics Review Example

A Four-Point Rise Inside a Six-Point Margin: A Unit 8 Service Metrics Review for a Mobile Auto Glass Company

[Student Name]

Purdue University Global

MT221: Customer Service

Unit 8 Service Metrics Review

[Instructor Name]

[Date]

Northline Auto Glass, its survey responses, measures and every figure are composite and illustrative; the sources cited are real.

The Dashboard

Six measures appear on Northline's quarterly dashboard. Customer satisfaction, measured as the share of survey respondents choosing one of the top two boxes on a five-point scale, was [81] percent. Net Promoter Score, the share of promoters minus the share of detractors on the zero-to-ten likelihood-to-recommend question, was plus [24], from [49] percent promoters and [25] percent detractors. First-visit completion, the share of jobs finished on the first visit, was [71] percent. Repeat contacts, jobs where the customer called again within seven days, were [22] percent. On-time arrival within the window was [88.2] percent. Logged complaints numbered [342], or about [6.2] per hundred visits.

How Much the Score Can Be Trusted

Reichheld (2003) promoted the single recommendation question as a simple measure linked to growth, and many companies adopted it for that reason. But a score built from a few hundred answers moves with chance. With [780] responses, [49] percent promoters and [25] percent detractors, the standard error of the score is about three points, so a ninety-five percent margin of error is roughly plus or minus six points. A rise from plus [20] to plus [24] sits well inside that range and could be noise.

Keiningham et al. (2007), checked the claim against several years of company data in Norway and found no evidence that it outperformed customer satisfaction measures. That does not make the score useless, but it argues against treating a small quarterly change as a result.

A four-point rise with a six-point margin is a reason to keep watching, not a reason to celebrate.

What the Score Hides

Splitting the score by repair path tells a more useful story. Customers whose glass was replaced on the first visit gave a score of about plus [38]. Customers who needed a second visit because the wrong glass arrived gave about minus [12]. The overall score is mostly a weighted average of those two groups, so it rises and falls with the share of first-visit jobs. That makes first-visit completion the measure worth managing; the recommendation score follows it.

The Operational Measures

First-visit completion at [71] percent means nearly three jobs in ten need a second trip. That rate can be changed by the booking script and parts check discussed in Units 6 and 7, and every point of improvement saves van trips and customer time. It is the measure to use for decisions about the booking process and the parts desk.

Repeat contacts at [22] percent mean more than one customer in five calls again within a week, usually to ask where a part is, to report a leak or to ask about a warning light. The measure should be split by reason, since a call about part status points to communication and a call about a leak points to workmanship. It can guide decisions about follow-up texts and technician quality checks.

On-time arrival at [88.2] percent counts how often vans arrive within the window but not whether customers were warned when they did not. Read together with how many late customers got a warning call, the Unit 5 measure, it guides dispatch and route decisions.

Complaints per hundred visits at [6.2] are useful as a trend but are shaped by how easy it is to complain, so a new online form could raise the count without any change in service.

What the Dashboard Leaves Out

Two things matter to customers and appear nowhere on the dashboard. The first is calibration success: the share of cars with cameras or sensors that leave the job with those systems working, without a dealer visit. As more vehicles carry driver assistance cameras, this measure will decide a growing share of satisfaction. The second is customer effort: how many calls, texts and waits a customer needed to get one windshield replaced. A customer who had to call twice to learn where her part was may still rate the technician well, so effort must be measured separately.

Decision Matrix

Each measure should be tied to the decision it can inform. First-visit completion guides the booking script, parts checks and stocking. Repeat contacts by reason guide follow-up communication and workmanship checks. On-time arrival and warned late visits guide dispatch and staffing. Customer satisfaction guides overall service priorities over several quarters. The recommendation score is best used as a long-term trend reported alongside its margin of error, not as a target for bonuses.

Recommendations

Northline should report the recommendation score with its margin of error, set first-visit completion as the primary service target with a goal of [85] percent within two quarters, split repeat contacts by reason, and add warned late visits to the dashboard. Zeithaml et al. (2018) note that service measures are most useful when each is linked to a process someone can change, and this dashboard should be redesigned around that idea.

References

Keiningham, T. L., Cooil, B., Andreassen, T. W., & Aksoy, L. (2007). A longitudinal examination of net promoter and firm revenue growth. Journal of Marketing, 71(3), 39-51. https://doi.org/10.1509/jmkg.71.3.039

Reichheld, F. F. (2003). The one number you need to grow. Harvard Business Review, 81(12), 46-54.

Zeithaml, V. A., Bitner, M. J., & Gremler, D. D. (2018). Services marketing: Integrating customer focus across the firm (7th ed.). McGraw-Hill.