NPS Campaign

Origin & evolution

Net Promoter Score was introduced by Fred Reichheld in The One Number You Need to Grow, published in Harvard Business Review in 2003, developed with Bain & Company and Satmetrix. Reichheld's argument was that a single question, likelihood to recommend, predicted company growth better than long satisfaction surveys. The arithmetic is deliberately blunt. Respondents scoring 9 or 10 are promoters, 7 or 8 are passives, and 0 through 6 are detractors. The score is the percentage of promoters minus the percentage of detractors, producing a number from -100 to +100 with the passives discarded.

Practice split the metric🔢MetricStrategyA unified metric that measures progress, health, or behaviour across the productView reference → by timing. Relational NPS asks the question periodically about the overall relationship, a temperature read on the customer base. Transactional NPS fires the same question right after a specific interaction, such as a support case or a renewal, to judge that interaction. The two answer different questions and should not be averaged together.

The critiques are as established as the metric. Reichheld's original growth claim relied on historical rather than future growth, correlating past growth with scores measured over an overlapping period, which shows association rather than the prediction it was sold as. The underlying studies were never published in full or peer-reviewed, and independent replications found weak correlations at best. The discarding of passives and the wide 0 to 6 detractor band also throw away information that a mean or a distribution would keep. Practitioners such as Itamar Gilad have catalogued these problems in detail. The honest position the field has reached is that NPS is a serviceable, comparable tracking signal as long as no one treats the single number as a validated forecast🔮ForecastSales & RevenueA revenue forecastView reference →.

How it works in practice

A SaaS company runs a relational NPS campaign quarterly and a transactional one after every support ticket🆘Support TicketCustomer SuccessCustomer support request or issueView reference →. One quarter the relational score drops from +42 to +31. The headline number says something is wrong; it does not say what. The verbatim comments attached to the detractor responses do: a recent pricing change is the recurring theme🎨ThemeProduct SpecificationA strategic grouping of related featuresView reference →.

The transactional campaign, meanwhile, holds steady at +55, which rules out support as the cause and points the team firmly at billing. The score functions as a smoke alarm here. It is loud, it is comparable quarter over quarter, and on its own it tells you nothing about the fire. The value comes from pairing the number with the open-text reasons and routing low scores into the customer-health signal so an account team can act before the detractor churns.

NPS campaign vs. its neighbours

• Customer health score💚Customer Health ScoreCustomer SuccessA composite health score for a customerView reference → blends many signals, such as usage, support load, and sentiment, into a forward-looking risk🎲RiskComplianceA risk to the product or businessView reference → read on an individual account. An NPS campaign contributes one signal to that blend. The score answers "is this account at risk?"; the campaign answers "would this customer recommend us right now?".
• Metric is the generic measured quantity. NPS is one metric, and a campaign is the programme that produces it on a cadence. The campaign is the machine; the metric is the reading on its dial.
• Feedback program🎤Feedback ProgramCustomer FeedbackA structured feedback collection programView reference → is the broader apparatus for collecting customer input, including interviews, surveys, and reviews. An NPS campaign is one channel a feedback programme runs, optimised for a comparable trend line rather than depth.

In the graph

In the Unified Product Graph, an NPS campaign sits in the feedback domain as a structured measurement programme. A feedback programme runs it through feedback_program_runs_nps_campaignFeedback ProgramrunsNPS Campaignhierarchy, and the campaign produces readings through nps_campaign_tracks_metricNPS CampaigntracksMetriccross-domain, with a product tied to its results by product_measured_by_nps_campaignProductmeasured byNPS Campaignhierarchy. The link the structure makes explicit is to account risk: nps_campaign_tracks_customer_health_scoreNPS CampaigntracksCustomer Health Scorecross-domain and its inverse customer_health_score_tracked_by_nps_campaignCustomer Health Scoretracked byNPS Campaignhierarchy wire the score into the health signal, so a detractor response becomes an input to retention, traceable to an account instead of stranded as a number on a slide. That wiring is the antidote to the metric's central weakness. The single number stays comparable, and every reading carries a path back to the account it came from and the action it should trigger.