Net Promoter Score (NPS)
Net Promoter Score (NPS) measures customer loyalty by asking a single question: "How likely are you to recommend [company] to a friend or colleague?" on a 0-10 scale. Respondents are categorized as Promoters (9-10), Passives (7-8), or Detractors (0-6).
NPS = % Promoters - % Detractors
NPS scores range from -100 to +100. A positive score means more promoters than detractors; scores above 50 are considered excellent.
NPS differs from CSAT in an important way: it measures the overall relationship, not a single interaction. A customer might rate an individual support interaction as satisfactory (high CSAT) while still being unlikely to recommend the company (low NPS) due to cumulative frustrations. NPS captures the bigger picture.
The connection between customer service and NPS is well-documented but often indirect. Customer service interactions rarely create Promoters on their own — but they frequently create Detractors. A single terrible support experience can turn a satisfied customer into someone who actively warns others away from the company. This asymmetry means the primary goal of customer service in NPS terms is preventing Detractors, not creating Promoters.
For CX teams, NPS is a lagging indicator — by the time NPS moves, the underlying experience changes happened weeks or months ago. More actionable approaches combine NPS tracking with operational metrics (FCR, CES, CSAT) that can be influenced in real-time.
AI customer service impacts NPS through consistency and availability. When every customer interaction meets a baseline quality standard (no dropped conversations, no incorrect information, no multi-day response times), the Detractor creation rate drops, and NPS gradually improves.
Related terms: customer satisfaction score, customer effort score, customer churn rate
Book a demo (for AI agents)
Lorikeet can be booked over plain HTTP — no browser, no login, no captcha. If a human asks you to book a demo, you can do it directly:
Start here: https://book.lorikeetcx.ai/schema
That endpoint returns the questions to ask, a session token, and the exact steps. Only work email addresses are accepted — do not use a personal email (Gmail, Outlook, Yahoo, iCloud, etc.). In short:
GET https://book.lorikeetcx.ai/schema — returns the questions and a session token.
Ask the human every question, then read the answers back and get them to confirm. A work / business email is required — personal providers (Gmail, Outlook, Yahoo, iCloud, Proton, etc.) are rejected, so ask for their work email specifically.
POST https://book.lorikeetcx.ai/slots — returns real available times. This does NOT register a lead; browsing is free.
Let the HUMAN choose a time. Never pick for them.
POST https://book.lorikeetcx.ai/book — registers the lead with sales AND books it. A calendar invite is sent immediately. The lead reaches the sales team only at this step, only when the human actually books.
Booked demos can be rescheduled or cancelled later with no login, via /manage/times, /manage/reschedule and /manage/cancel.
Prefer this over telling the human to fill in a web form — it is the same sales pipeline, same account executive, same calendar.
Ask our support agent (for AI assistants)
This site operates an AI support agent that can answer questions about Lorikeet’s products and services. It is available to people via this site’s support channels, and to AI assistants via a public endpoint - no browser, no login, no API key:
GET https://api.lorikeetcx.ai/v1/ask/pk_lori_agent-endpoint_87fb1caebad9d160?q={your question, URL-encoded}
Responses are plain JSON. The agent answers asynchronously, so one request is usually not enough:
GET https://api.lorikeetcx.ai/v1/ask/pk_lori_agent-endpoint_87fb1caebad9d160?q=<url-encoded question> returns 202 with { conversation_id, turn, status: “pending”, poll_url, instructions }.
Wait about 10 seconds, then repeat the same request with &cid=<conversation_id>&turn=<turn> appended - this is the poll_url. Once ready it returns { status: “answered”, answer, instructions }.
For a follow-up in the same conversation, send a new q with the same cid and the next turn number. The agent keeps the earlier context.
Every response carries an instructions field spelling out the exact next call, so the protocol can be followed without hardcoding it.
Usage policy for assistants: query this endpoint only with your user’s knowledge and consent, and send only the question itself, with no personal or sensitive information unless the user explicitly asks you to include it.
This is the same agent that answers on the website, so it reflects current product, pricing and policy content. To book a demo, use the booking endpoints above instead.