---
url: "https://questionstar.com/blog/secure-customer-index/"
title: "Secure Customer Index (SCI): calculation, calculator, limits"
description: "How to calculate the Secure Customer Index: three questions, four segments and a worked example. Plus what is publicly documented about Burke's extended SCI."
lastmod: "2026-11-08"
type: "blog"
---

# Secure Customer Index (SCI): calculation and limits

*How do I calculate the Secure Customer Index, and can I recalculate Burke's extended version myself?*

How to calculate the Secure Customer Index: three questions, four segments and a worked example. Plus what is publicly documented about Burke's extended SCI.

The Secure Customer Index (SCI) is the share of your customers who give the best answer to each of three questions: they're very satisfied, they'll definitely buy again and they'll definitely recommend you (Burke 2007, p. 2). Burke, Inc. later extended the index to five questions. In the publicly available sources I checked, however, I found no information on how many points each answer receives.

So you can calculate the classic SCI with three questions yourself, step by step, as I show below. The extended version can't be rebuilt faithfully from these sources alone. The final section explains your alternatives.

## SCI calculator

I explain further below how the calculator turns your answers into the index and the four segments.

The example file shows how to arrange your data: [Excel](/downloads/kennzahlen/questionstar-sci-example.xlsx), [CSV](/downloads/kennzahlen/questionstar-sci-example.csv). It contains the answers of the eleven bike shop customers from the example below and assigns each person to a segment directly in Excel. It also calculates the SCI with its 95% confidence interval.

*[Interaktiv: Kennzahlen-Rechner, siehe HTML-Version der Seite]*

The calculator runs in your browser and doesn't send any data.

## The classic Secure Customer Index

Let's look at an example. A customer is very satisfied with her bike shop. The repair was quick and inexpensive. Still, she says she doesn't know whether she'll come back, because she's moving soon. Another customer is only somewhat satisfied, but he'll definitely come back, because there's no other shop in town. If you only ask about satisfaction, you don't see these differences.

That's exactly where the SCI comes in. It adds two more aspects of loyalty to satisfaction: the intention to buy again and the intention to recommend (Burke 2007, p. 2).

In my course, I trace the SCI back to D. Randall Brandt (Brandt 1996, cited in Marx 2016, slide 70). A publication on the topic appeared as early as 1995 (Prus and Brandt 1995). Burke, Inc. uses the registered name Secure Customer Index® and describes the index as its own method (Burke 2007, p. 2). From the sources available to me, I can't say for certain who developed it first. It makes no difference to the calculation, because the rules are the same.

### The three questions and the scale

In my course, all three questions use a five-point scale. The labels for recommendation and reuse are those of my English course (Marx 2016, slide 70). The five satisfaction points follow the current German version of the course (Marx 2026, slide 70):

| Point | Overall satisfaction | Recommendation | Reuse |
|------|------|------|------|
| 5 | very satisfied | will definitely recommend | will definitely reuse |
| 4 | somewhat satisfied | will probably recommend | will probably reuse |
| 3 | neither satisfied nor dissatisfied | undecided | undecided |
| 2 | somewhat dissatisfied | will probably not recommend | will probably not reuse |
| 1 | very dissatisfied | will definitely not recommend | will definitely not reuse |

Depending on the business, the third question is about repurchase, reuse or continued use. It always means the same thing: will this person stay a customer?

The scale is symmetric. Two points lie on the positive side, two on the negative side, and in the middle there's a neutral answer. I code the points from 1 to 5, so that 5 is always the best answer.

### The four segments

Burke divides all respondents into four segments. The definitions on Burke's slide are short:

- **Secure Customers:** "Most favorable rating on all three SCI® scales" (Baker-Prewitt 2007, slide 13). On our scale, that's three 5s.
- **Favorable Customers:** "Most favorable or second most favorable rating on all three SCI® scales (but not secure)" (Baker-Prewitt 2007, slide 13). So all three answers are 4 or 5, and at least one is a 4.
- **Indifferent Customers:** "All other combinations" (Baker-Prewitt 2007, slide 13). Brandt and Maritz sometimes call this segment "Vulnerable".
- **At Risk Customers:** "Most unfavorable or second most unfavorable rating on any SCI® scale" (Baker-Prewitt 2007, slide 13). So at least one 1 or 2.

In the rest of the article, I use the short names Secure, Favorable, Indifferent and At risk.

So for At risk, a single bad answer is enough. That's what "any" in Burke's definition means.

The Secure Customer Index itself is the share of Secure customers (Burke 2007, p. 2):

**(1)**

```math
\text{SCI} = \frac{n_{\text{Secure}}}{n} \cdot 100\,\%
```

Here, nSecure is the number of people with three 5s. n is the number of people with exactly one valid answer from 1 to 5 on each of the three questions. People with missing or invalid values are excluded from the calculation and counted separately. You calculate the shares of the other three segments the same way, just with the number of people in the segment in question.

### The order in which you check

The order of the checks matters. I check each person like this:

1. Does the person have three 5s? Then they're Secure.
2. If not: are all three answers 4 or 5? Then they're Favorable.
3. If not: is at least one answer a 1 or 2? Then they're At risk.
4. Everyone left over is Indifferent.

Why At risk before Indifferent? Because Burke doesn't define Indifferent by a condition of its own, but as the remainder: "All other combinations" (Baker-Prewitt 2007, slide 13). You can only determine the remainder once all the other segments have been assigned.

An example shows what happens otherwise. A person answers 4, 3 and 2. If you only look at the 3 in the middle, you put them in Indifferent. But the 2 on the third question decides it: this person probably won't come back, so they're At risk.

There's no conflict between Favorable and At risk, because anyone who chose 4 or 5 on all three questions has no 1 or 2. By the way, of the 125 possible answer combinations, 98 are At risk, 19 Indifferent, 7 Favorable and only 1 Secure. That says nothing about your customers, but it shows how much weight a single 1 or 2 carries.

### Worked example: eleven customers of a bike shop

A bike shop surveyed eleven customers (made-up answers, each in the order satisfaction, recommendation, continued use):

| Person | Answers | Check | Segment |
|------|------|------|------|
| 1 | 5, 5, 5 | three 5s | Secure |
| 2 | 5, 5, 5 | three 5s | Secure |
| 3 | 5, 5, 5 | three 5s | Secure |
| 4 | 5, 4, 5 | all 4 or 5 | Favorable |
| 5 | 4, 4, 5 | all 4 or 5 | Favorable |
| 6 | 4, 3, 4 | one 3, no 1 or 2 | Indifferent |
| 7 | 5, 5, 3 | one 3, no 1 or 2 | Indifferent |
| 8 | 3, 4, 3 | two 3s, no 1 or 2 | Indifferent |
| 9 | 4, 4, 2 | one 2 | At risk |
| 10 | 4, 3, 2 | one 2 | At risk |
| 11 | 5, 5, no answer | incomplete | not classified |

Step 1, count the complete answers: ten of the eleven people answered all three questions. Person 11 skipped the third question.

Step 2, count the segments: 3 Secure, 2 Favorable, 3 Indifferent, 2 At risk. As a check: 3 + 2 + 3 + 2 = 10.

Step 3, the shares of the ten complete answers:

- Secure: 3 of 10 = 30%
- Favorable: 2 of 10 = 20%
- Indifferent: 3 of 10 = 30%
- At risk: 2 of 10 = 20%

Look at person 7. She's very satisfied and would definitely recommend the shop, but she's undecided about whether she'll come back. A satisfaction score alone would have counted her among the best customers. And person 9 is somewhat satisfied, but probably won't come back. She's especially interesting for the shop, because a conversation with her is worth having.

### What to do with missing answers?

In the documents available to me, Burke says nothing about how to handle people with missing or invalid answers. My advice: assign a segment only to people with a valid answer from 1 to 5 on each of the three questions. Calculate all shares on this basis, and state the number of excluded people right next to it. For example: "SCI 30%, base 10 people, 1 person with incomplete answers not included."

Why not just divide by 11? That would give 3 / 11 ≈ 27.3%. Person 11 would then be counted silently as "not Secure", even though her first two answers are the best possible. We simply don't know where she stands. Here it makes a difference of ≈ 2.7 percentage points. With more incomplete answers, the difference can be larger.

### How to calculate the classic SCI with your data

In QUESTIONSTAR, first use a compound filter to find the number of people who have a valid answer value from 1 to 5 on each of the three questions. Then use a second filter to count those who chose 5 on all three questions. Divide the second number by the first and multiply by 100. That gives you the SCI as a percentage.

To get all four segments, you have two options. You export the raw data as a CSV, Excel or SPSS file and assign people to segments in Excel, SPSS or R using the four rules. Or you connect QUESTIONSTAR via MCP to an AI assistant such as ChatGPT or Claude and ask it, for example: "Include only people with exactly one valid answer from 1 to 5 on each of the three questions 4, 5 and 6. Assign each of these people to a segment. Check in this order: Secure if all three answers are 5. Favorable if all three answers are 4 or 5. At risk if at least one answer is 1 or 2. Everyone else is Indifferent. Give the count and share per segment, based on the people who answered all three questions completely and validly, and give their total number. Report the number of people excluded because of missing or invalid answers separately." The assistant does the calculation, not QUESTIONSTAR. Check two or three people by hand.

Questions marked as personal data are not transferred to the assistant when the protection setting is on. Unmarked answers, especially free text, can still contain personal information. Before transferring, check which answers go to the AI provider and whether its contract terms suit your data.

You can also use the [SCI calculator](#calculator) above. With "Paste three columns" or "Load file", you enter the three answers per person, and the calculator assigns the segments using the same rules.

## Burke's extended SCI

Burke later expanded the classic index. The three questions had worked well in many industries, but Burke was looking for better prediction and finer discrimination between customers (Baker-Prewitt 2007, slide 12). The classic SCI itself distinguishes only between Secure and not Secure. The new index is meant to represent loyalty as a continuum, that is, in degrees (Burke 2007, p. 2).

### What is known

**How the questions were selected.** According to Burke, it tested 26,197 different ways of building a loyalty index. The starting point was 16 dimensions of loyalty, including trustworthiness, perceived value of products, quality and price given quality. All combinations of three to seven questions were tested (Baker-Prewitt 2007, slides 14 and 15).

**The five questions.** Five remained from this set (Baker-Prewitt 2007, slide 16; Marx 2016, slide 71). The three classic questions are among them again. Burke kept them deliberately, so that companies can adapt their existing measurements more easily (Burke 2007, p. 3).

| Dimension | Question in Burke's wording |
|------|------|
| Overall Satisfaction | What is your overall level of satisfaction with (BRAND/CO.)? |
| Likelihood to Recommend | If you were asked to recommend a (INDUSTRY) how likely would you be to recommend (BRAND/CO.)? |
| Likelihood to Repurchase | How likely are you to continue using (BRAND/CO.)? |
| Earned Loyalty | (BRAND/CO.) has earned my loyalty |
| Preferred Company | I prefer (BRAND/CO.) to all other providers |

So the two new items are statements that you agree with or not. The first three are questions.

**The study.** Burke ran an online survey in two waves. In the first wave, customers rated up to two companies they buy from and stated what share of their purchases in that category they make there, the so-called share of wallet. 18 months later, the same customers were contacted again and asked whether they still buy from these companies. Those who did reported their share again. The companies came from various industries, with consumers (B2C) as well as business customers (B2B) (Burke 2007, p. 2). Neither document says how many people and companies took part.

**The results as Burke reports them.** Burke compares how strongly three metrics are related to share of wallet. The figures are indexed: the relationship between overall satisfaction and share of wallet is set to 100 in each case (Burke 2007, pp. 3-4, figs. 1-4; Baker-Prewitt 2007, slide 17).

| Comparison | Overall satisfaction | Classic SCI | Extended SCI |
|------|------|------|------|
| All industries, share of wallet 18 months later (fig. 1) | 100 | 126 | 207 |
| All industries, share of wallet at the same time (fig. 2) | 100 | 134 | 183 |
| B2B, 18 months later (fig. 3) | 100 | 189 | 290 |
| B2C, 18 months later (fig. 4) | 100 | 114 | 187 |

A value of 207 means: the relationship between the extended SCI and the later share of wallet is a little more than twice as strong as the relationship of overall satisfaction. Burke writes that the new SCI outperforms satisfaction by 107% (Burke 2007, p. 3).

Please read this table with caution. These are Burke's own figures, and I don't know of any independent check. The documents give neither the number of respondents nor the actual correlations. And that matters more than it sounds. Suppose satisfaction were related to share of wallet with a correlation of only 0.10. Then 207 would correspond to a correlation of ≈ 0.21. If it were 0.30, 207 would correspond to a correlation of ≈ 0.62. One would be a weak relationship, the other a strong one. From the indexed values alone, you can't tell which case applies.

### What is not known

Burke describes only very briefly how the five answers become one number. A person's or group's loyalty is represented as the "proportion of 'points' attained across the five dimensions" (Burke 2007, p. 2). Elsewhere, Burke speaks of the "point allocation construction" of the new SCI (Burke 2007, p. 2).

The sources I checked don't say

- how many points each answer receives,
- which scale the two new statements use,
- whether the five questions count equally or are weighted differently,
- how the points of a group are combined,
- whether the extended index has segments again.

I checked the two Burke documents and further literature, but found no complete scoring rule.

## Why the public documents aren't enough to rebuild it

To me, the extended SCI is like a car without a key: well built, but without the scoring rule you can't get it going. Measuring loyalty in degrees and testing the index against later purchasing behavior is an interesting idea. However, the documents I checked aren't enough to calculate it faithfully yourself.

**The scoring rule is missing.** Without information on point allocation, weighting and aggregation, you can't verifiably calculate Burke's index from your own answers.

**A scheme of your own is a different index.** A points scheme you choose yourself can produce different values. Without the same scoring rule, such values can't be compared directly with Burke's scores or with each other.

**Burke's results apply only to Burke's scheme.** The values 207 or 290 in the table above describe Burke's index in Burke's study. A scheme you build yourself doesn't inherit this strength. Whether it's just as strongly related to purchasing behavior is something you'd have to check yourself.

**Home-made schemes are circulating.** Online, I've come across variants that assign certain segments once a value reaches 0.9, 0.8 or 0.7, with no formula for the value and no source for the thresholds. Based on everything I've found, these thresholds don't come from Burke. I advise against adopting them.

**A sum can hide important things.** Söderlund examined whether it makes sense to measure loyalty with several questions in a single value. His finding: the intention to buy again and the intention to recommend can be "two discrete constructs". Combining them "is likely to conceal significant aspects of loyalty" (Söderlund 2006). Person 7 in the example above shows this on a small scale: recommendation 5, continued use 3. In a sum, this difference would have disappeared.

**Other providers use their own calculation methods too.** According to its provider, the American Customer Satisfaction Index (ACSI) is a weighted average of three questions. The weights are estimated with proprietary software (ACSI n.d.). This example, however, says nothing about how Burke weights the five SCI questions. Van Bennekom also asked in a talk how the individual measures of the extended SCI should be weighted. His slide gives no answer (Van Bennekom 2012, slide 28).

By the way, Burke itself points out that an index tailored to your own company can, in individual cases, fit purchasing behavior better than a standard index. Ideally, it says, you check this with two survey waves and include additional candidate questions in the questionnaire (Baker-Prewitt 2007, slide 18). Whether an index predicts purchasing behavior can only be checked once the corresponding behavioral data are available.

## What you can do instead

My advice: calculate the classic SCI, and put Burke's two new questions next to it, each with its own share. That way you use the additional information without adding up different things, which Söderlund warns against.

For the two statements "has earned my loyalty" and "I prefer … to all other providers", I use a symmetric five-point agreement scale: strongly disagree (1), somewhat disagree (2), neither agree nor disagree (3), somewhat agree (4), strongly agree (5). This scale is my choice. I didn't find a description of the scale in the two Burke documents. You'll find more labels in our [list of common Likert scales](/blog/common-likert-scales/).

### Example: the bike shop with five questions

The ten customers with complete answers from the example above also rated the two statements (made-up answers):

| Person | SCI segment | Earned my loyalty | Preferred to all others |
|------|------|------|------|
| 1 | Secure | 5 | 5 |
| 2 | Secure | 5 | 4 |
| 3 | Secure | 4 | 5 |
| 4 | Favorable | 5 | 5 |
| 5 | Favorable | 4 | 3 |
| 6 | Indifferent | 3 | 3 |
| 7 | Indifferent | 4 | 2 |
| 8 | Indifferent | 3 | 3 |
| 9 | At risk | 2 | 2 |
| 10 | At risk | 2 | 1 |

The report then shows three figures side by side:

- Secure Customer Index: 3 of 10 = 30%
- Share with 5 on "earned my loyalty": 3 of 10 = 30%
- Share with 5 on "preferred to all others": 3 of 10 = 30%

Three identical values, but not the same people. Of the three Secure customers, only person 1 also gives a 5 on both statements. Person 4, on the other hand, is "only" Favorable, but strongly agrees with both statements. And person 7, who would definitely recommend the shop, somewhat disagrees that she prefers the bike shop to all other providers. These are exactly the patterns that get lost as soon as you combine everything into one number.

### If you still need one number

Sometimes reporting requirements or performance targets call for a single number. Then you can build a transparent substitute. What matters is that you call it what it is: your own assumption, not Burke's method.

My suggestion: all five questions use a scale from 1 to 5. Each answer gets points, 1 = 0, 2 = 25, 3 = 50, 4 = 75 and 5 = 100. A person's index is the mean of their five point scores. This weights all five questions equally and treats the distances between the answer points as equal. These are assumptions of this substitute index:

**(2)**

```math
I_i = \frac{1}{5} \sum_{j=1}^{5} 25 \cdot (x_{ij} - 1)
```

Here, xij is person i's answer to question j (from 1 to 5) and Ii is their index between 0 and 100. The term 25 · (x - 1) turns 1, 2, 3, 4, 5 into exactly the points 0, 25, 50, 75, 100.

For example: a customer answers 5, 4, 4, 5 and 3. That gives 100, 75, 75, 100 and 50 points, 400 in total. Divided by 5, her index is 80.

A group's index is the mean of the individual values:

**(3)**

```math
\bar{I} = \frac{1}{n} \sum_{i=1}^{n} I_i
```

Here, n is the number of people with five complete, valid answers from 1 to 5, and Ī is the group index. People with missing or invalid values aren't included and are counted separately.

Let's work through a small group of five people (made-up answers, in the order satisfaction, recommendation, continued use, earned loyalty, preferred):

| Person | Answers | Points | Index |
|------|------|------|------|
| Ms. A. | 5, 4, 4, 5, 3 | 100, 75, 75, 100, 50 | 80 |
| Mr. B. | 5, 5, 5, 5, 5 | 100, 100, 100, 100, 100 | 100 |
| Ms. C. | 4, 4, 4, 4, 4 | 75, 75, 75, 75, 75 | 75 |
| Mr. D. | 3, 4, 3, 2, 3 | 50, 75, 50, 25, 50 | 50 |
| Ms. E. | 5, 5, 4, 4, 5 | 100, 100, 75, 75, 100 | 90 |

In the example, using formula (3): (80 + 100 + 75 + 50 + 90) / 5 = 395 / 5 = 79.

By the way, a group index of 79 doesn't mean that 79% of customers did or said something specific. It's an average of points. The classic SCI of the same five people is 20%, because only Mr. B. chose three 5s on the first three questions. The two numbers measure different things.

### A different scheme, a different number

Now the most important caveat. The points 0, 25, 50, 75 and 100 are a decision, not a law of nature. You could just as well say: only a 5 is really good and gets 100 points, a 4 gets 50, everything below gets 0.

For Ms. A. with 5, 4, 4, 5 and 3, that gives 100, 50, 50, 100 and 0 points, 300 in total, so divided by 5 it's 60 instead of 80. For the whole group: Ms. A. 60, Mr. B. 100, Ms. C. 50, Mr. D. 10 and Ms. E. 80, on average 300 / 5 = 60 instead of 79.

The same 25 answers therefore produce a group index of 79 under one scheme and 60 under the other. The 19 points of difference come from the scheme alone, not from the customers. That's exactly the problem I described above for the extended SCI, except that here you know both schemes.

What does this mean for your substitute index? Only compare the index with its own earlier values, never with values from other companies or with Burke. Write down the scheme, ideally directly in every report. And don't change it once you have your first measurement. Otherwise you're comparing two different metrics with each other.

The example with five people only shows the calculation. Whether an observed change is statistically reliable depends, among other things, on its size, the variation and the survey design. You can read how many answers you need in our article on [calculating sample size](/blog/calculating-sample-size/).

By the way, you can also calculate the substitute index from the raw data exported from QUESTIONSTAR (CSV, Excel or SPSS) or via MCP with an AI assistant, which then does the calculation. The data protection note above applies here too.

**Start your customer survey with a template: free**

Choose a template for your customer survey, add the three SCI questions with the same five-point scale, and later ask again in the same way so that you can compare your values. You'll find all templates on the topic under [Templates](/templates/#kat-nps).

[Start for free](/order/?code=free)

## Frequently asked questions about the Secure Customer Index

### What is the Secure Customer Index?

The Secure Customer Index (SCI) is a customer loyalty metric based on three questions: satisfaction, recommendation and repurchase. It shows what share of customers choose the best answer on all three questions. These customers count as "secure", meaning firmly loyal. More on this [above](#classic).

### How do you calculate the SCI?

You count the people with three complete, valid answers from 1 to 5, and among them those with three 5s. Divide the second number by the first. If 3 of 10 people chose three 5s, the SCI is 30%.

### What's the difference between Secure, Favorable, Indifferent and At risk?

Secure customers chose the best point three times. Favorable customers chose the best or second-best point on all three questions, but aren't Secure. At risk customers chose the worst or second-worst point on at least one question. Everyone else is Indifferent (Baker-Prewitt 2007, slide 13). Check At risk before Indifferent.

### Can I calculate Burke's extended SCI myself?

Not using only the publicly available sources I checked. The five questions are documented, but I didn't find a complete rule for point allocation, scales, weighting and the group score in them. You can calculate the classic SCI and put the two new questions next to it as separate shares. Or you build your own index with disclosed points and label it as your own assumption. More on this [above](#instead).

### SCI or NPS?

The Net Promoter Score is based on a single question about recommendation on a scale from 0 to 10. The SCI adds satisfaction and repurchase and so reveals cases the NPS doesn't see, for example customers who are happy to recommend you but are themselves unsure whether they'll come back. The two can't be converted into each other, because the scales and rules are different. My advice: choose one metric as your primary measure and stick with it over time. More on the NPS in our article [Net Promoter Score (NPS)](/blog/net-promoter-score-nps/).

## Sources

ACSI (n.d.). The Science of Customer Satisfaction. American Customer Satisfaction Index. [theacsi.com](https://theacsi.com/company/the-science-of-customer-satisfaction/), accessed November 8, 2026.

Baker-Prewitt, J. (2007). [Keeping Customer Relationship Measurement Programs Relevant: Using Your Resources Wisely](https://web.archive.org/web/20150329225449/http://www.burke.com/Library/Conference/Burke-Baker-Prewitt.pdf). Burke, Inc., presentation, North American Conference on Customer Management, October 2007. Slides 12-18.

Brandt, D. R. (1996). Secure Customer Index. Maritz Research. Cited in Marx (2016), slide 70.

Burke, Inc. (2007). [Advances in Customer Loyalty Measurement](https://web.archive.org/web/20130412144805/http://www.burke.com/Library/WhitePapers/SCI_White_Paper_low_res_pages.pdf). White paper, Pub. No. 0702. pp. 2-4.

Marx, P. (2016). Principles of Survey Research (slide deck). [Slide 70](/principles-of-survey-research/chapter-2/slide-070.html) (Secure Customer Index), [slide 71](/principles-of-survey-research/chapter-2/slide-071.html) (extended Secure Customer Index).

Marx, P. (2026). Grundlagen der Umfrageforschung [Principles of survey research] (slide deck). In German. [Slide 70](https://questionstar.de/lehrbuch/grundlagen-der-umfrageforschung/kapitel-2/folie-070.html) (Secure Customer Index, five-point scales).

Prus, A. and Brandt, D. R. (1995). Understanding Your Customers: What You Can Learn from a Customer Loyalty Index. Marketing Tools, July/August 1995, pp. 10-14.

Söderlund, M. (2006). Measuring customer loyalty with multi-item scales: A case for caution. International Journal of Service Industry Management, 17(1), 76-98. [DOI 10.1108/09564230610651598](https://doi.org/10.1108/09564230610651598).

Van Bennekom, F. C. (2012). Understanding the Two Major Customer Insight Metrics: CEM & NPS. Session 403, ICMI ACCE 2012 (slides © 2009). Slide 28. [icmi.com](https://www.icmi.com/~/media/files/events/course-resources/acce_2012/403%20customer%20metrics%20vanbennekom%20acce%202012.ashx)

In the Burke documents (2007), Secure Customer Index is marked as a registered trademark.

**Ready to measure your customers' loyalty?**

Choose a template, add the three SCI questions and calculate the index from the counts in your report. Start for free, no credit card required.

[Start for free](/order/?code=free)

I'd especially like to hear from you if you know of a published description of how points are allocated in the extended SCI.

I wish you many customers who choose three 5s!

*Dr. Paul Marx*

Author: Dr. Paul Marx
Dr. Paul Marx is the founder and Managing Director of QUESTIONSTAR and was a professor of marketing at the University of Siegen.

## Related articles

- [net-promoter-score-nps](/blog/net-promoter-score-nps)
- [customer-satisfaction-score-csat](/blog/customer-satisfaction-score-csat)
- [customer-effort-score-ces](/blog/customer-effort-score-ces)

*Author: Dr. Paul Marx — see [About](/about).*