Use case: NPS

Net Promoter Score: calculate, weight, track

DataLion computes the NPS as a documented codebook formula. Weight the score, break it down by segment, test for significance and smooth its trend over time. Driver analysis checks which factors explain it. ISO 27001-certified hosting, GDPR-compliant, made in Munich.

Interactive DataLion dashboard with an NPS score, promoter/detractor split and filters

DataLion computes the Net Promoter Score in the codebook as 100 × (share of promoters 9–10 − share of detractors 0–6) over valid answers. The calculation can be weighted. Break the score down by segment and test differences at confidence levels from 80% to 99%. SMA/EMA smooths the trend. Relative-importance analysis on R identifies possible drivers.

  • 🇩🇪 Made in Munich
  • GDPR-compliant
  • DPA included
  • Hosted in Germany
  • 🌐 Interface in EN, DE, FR & NL

Trusted by research institutes, brands & insights teams

  • YouGov
  • Mediengruppe RTL Deutschland
  • SevenOne Media
  • Nielsen Sports
  • Spiegel Institut
  • Messe Berlin
  • Hartmann
  • 0–10 recommendation scale
  • −100…+100 NPS range
  • 80–99% significance levels
  • SMA / EMA tracking smoothing

Why the NPS is often worth less than it could be

  • The score is computed by hand in Excel — sometimes weighted, sometimes not, with no documented formula.
  • Segment differences get interpreted without testing whether they are statistically significant.
  • The why-answers get collected but are never distilled into drivers.

The NPS as a codebook formula

DataLion records the NPS as a transparent codebook formula: 100 × (share of promoters [9–10] − share of detractors [0–6]) ÷ valid answers. Anyone answering 7 or 8 counts as a passive.

The formula excludes missing values. If a weight variable is present, DataLion calculates each case with its weight. You can use one or several weights.

  • Promoters (9–10), passives (7–8), detractors (0–6) as top-box/net rows
  • A reproducible codebook formula, not an opaque metric
  • Missing values cleanly excluded
  • Weighted NPS over one or several weight variables
DataLion table with NPS net rows and percentages

NPS by segment, with significance

Break the NPS down with live filters by region, touchpoint, product or customer group. Differences between segments are significance-tested right in the table. Choose 80%, 90%, 95% or 99% confidence and show the result as stars or letters.

The z-test, chi² and t-test run in the background. Pairwise and complement comparisons are built in, with an optional Yates correction for chi². This shows whether a higher segment NPS is statistically reliable.

  • Live filters by region, touchpoint, product or customer group
  • Significance at 80/90/95/99% — as stars (*/**/***) or letters
  • z-test, chi² and t-test; pairwise and complement comparisons
  • Optional Yates correction for chi²
Interactive DataLion dashboard comparing NPS across segments

NPS tracking with a smoothed trend

In tracking, DataLion shows the NPS as a timeline. Smooth it with a moving average (SMA) or an exponentially weighted average (EMA). You choose the window.

New waves are imported automatically. The dashboard updates, and wave-over-wave change can be significance-tested.

  • NPS as a timeline, wave after wave
  • SMA/EMA smoothing with a freely chosen window
  • Wave-over-wave change significance-tested
  • Automatic wave import — more on tracking studies
Timeline of an NPS across several waves in DataLion

Driver analysis, not gut feeling

The score does not tell you why it moves. Relative-importance analysis on the R engine checks which satisfaction or experience factors explain the NPS most. Regressions are also available.

Analyze the open follow-up as net codes or with AI sentiment & topic analysis. Custom labels can map the results to the NPS detractor/passive/promoter logic.

  • Relative importance: which drivers explain the NPS
  • Linear, ordinal & other regressions on R
  • Code open "why" responses by sentiment & topic with AI
  • Compare drivers by segment
Sentiment donut and topic ranking of the open-ended NPS reasons in DataLion

The NPS & eNPS question — with no recodes

Collect the NPS with the NPS question type (0–10) and an open follow-up. On publishing, DataLion automatically builds the project, dataset and codebook with value labels — answers are analyzable immediately, with no recoding.

The eNPS for employees uses the same workflow. Invite by anonymous link, QR code or one-time token.

  • NPS question type (0–10) plus an open follow-up
  • Auto-codebook with value labels — chartable at once, no recodes
  • eNPS for employees in the same workflow
  • Invite by anonymous link, QR code or one-time token
DataLion survey editor with the NPS question type

What you can build with DataLion

  • NPS tracking dashboard

    Score, net rows and a smoothed trend — wave after wave, significance-checked.

    See how →
  • Driver analysis

    Relative importance on R — which factors explain the NPS.

    See how →
  • Weighted NPS

    Weight to your target frame — from the dataset, a table or computed.

    See how →

See DataLion with your own data

Start free with your own raw data. Or book a personal demo of the path to a finished dashboard.

Top rated

4.5 out of 5 stars on G2 and OMR Reviews

What users say about DataLion

  • via G2
    Very professional company, attentive to the customer needs, provider of a great software and service.
    Generoso M. · CRM Analyst, Automotive
  • via G2
    The contacts at DataLion are very committed. If you have problems, you can count on help. DataLion reacts quickly to requests for new functions.
    Robert Q. · Managing Director
  • via G2
    User-friendliness, especially for market research topics. Structured backend with many customization options.
    Verified user · Market Research
  • via G2
    The embedding function allows us to generate insights of our data for our audience and customers by far less than half of the usual time needed before.
    Verified user · Leisure, Travel & Tourism
Read all 16 reviews on G2 →
We now work much more efficiently, giving us more time to take care of the derivations and insights from the data for the customers.
Jens Falkenau, Vice President of Market Research · Nielsen Sports
Read the case study →

The platform in detail

Go deeper

Common questions about NPS with DataLion

Exactly how does DataLion calculate the NPS?
Via a codebook formula: 100 × (share of promoters with 9–10 − share of detractors with 0–6) divided by valid answers. Passives (7–8) do not count toward the score. Missing values are excluded, and the range runs from −100 to +100.
Can I weight the NPS?
Yes. If a weight variable is present, DataLion computes the weighted NPS (each case multiplied by its weight). Weights come from the dataset, from a separate weights table (via a join), or are computed in DataLion to a target distribution.
Are segment and wave differences significant?
DataLion tests this right in the table: differences are tested at four confidence levels (80/90/95/99%) via z-test, chi² or t-test and shown as stars or letters, with an optional Yates correction for chi².
How do I understand what drives the NPS?
With relative-importance analysis — one of the predefined models on the R engine — you find which drivers explain the NPS most, complemented by regressions. You also analyze the open why-question in a structured way.
How do I track the NPS over time?
As a timeline with optional SMA/EMA smoothing and a freely chosen window. New waves import automatically, the dashboard refreshes itself, and wave-over-wave change can be significance-tested.

Ready to compute your NPS properly?

Try DataLion free with your own NPS survey — from the codebook formula to significance-checked tracking. Or book a personal demo.