# Surveys

Viewing 45 posts - 1 through 45 (of 45 total)
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• #46028

Mélanie
Participant

Any ideas?
I have a customer satisfaction survey results for the question How well does Company […] meet your needs?  The scale is from 1  10, with 10 being the best. What test do I use to determine if the actual answers meet the expected values? ( for example for Score 1, the expected was 1,000, but the actual was only 10 and so on for the other scores.  The expected counts range from 1000 to 8000).

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#151562

Jim Shelor
Participant

Dear Melanie,
I am not sure what you mean by a “test”, but here is how I would do the comparison of the data.
Since you said you expected the number of responses of 1 to be 1000, I assume you made some assumption about how many responses you would get.  If you got the number of responses you expected, good, but if not, you need to use the percentage method shown below.
First, turn your expected values into % of responses, that will account for the fact that the number of responses you got is not equal to the number you expected.
Now, do a histogram on the responses you got.
Take the number of responses in each bin of the histogram and turn it into % of responses.
Now you can do a direct comparison of the % of responses expected to be a 1 against the actual % of “1” responses.
I’ll have to do more research on the “test” question.  Do you mean a test to see if the actual is statistically different from the expected?
I’ll get back to you if I find anything.
Sincere regards,
Jim Shelor

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#151613

Mélanie
Participant

Jim:
The data set looks like this:
Score — Expected — Actual
1
I followed your advice, and I now have 10 percentages, one per score 1-10.  I charted that in a bar chart and it looks like it has a normal distribution.

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#151614

Mélanie
Participant

Jim:
The data set looks like this:
Score — Expected — Actual

I followed your advice, and I now have 10 percentages, one per score 1-10.  I charted that in a bar chart and it looks like it has a normal distribution.

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#151615

Mélanie
Participant

Jim:
The data set looks like this:
Score — Expected — Actual
1       —  1000       — 10
2       — 2000       — 30
3 etc…
etc.
10 etc.
I followed your advice, and I now have 10 percentages (calculated as Actual/Expected), one per score 1-10.  I charted that in a bar chart and it looks like it has a normal distribution.
I also did a histogram (or Descriptive Stats) for the Actual counts.  Are you saying to turn that into % of responses now?  I am not sure how to do that in Minitab.  And even so, wouldn’t it change the form of the histogram?
For the test – yes, that is what I mean — to see if the actual is statistically different from the expected.
Thanks,
Melania

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#151617

Ken Feldman
Participant

Melanie, this looks like you are barking up the wrong tree. What are you trying to prove? If you are trying to show that 10 is statistically different than 1000, that’s a no brainer. You are taking percentages and treating them as continuous data by using histograms and mentioning normality. You have counts, which are discrete. We don’t know enough yet if it can be treated as continuous. Again, what is your problem? If you are trying to show that the proportion of actual responses is the same/different than the proportion of expected, then you use proportion tests or chi square. You have a bigger problem to deal with because it looks like you have a very small response rate given you expected 1000 and got 10. Jim, don’t send her off on any more wild goose chases until she clarifies what the heck she is trying to do…unless you have some mindmeld that makes it perfectly clear what she means. I for one haven’t got a clue.

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#151620

Jim Shelor
Participant

Dear Melanie,
I think I may not have been clear about what I was suggesting.
If you expect 1000 responses grading you as a 1, how many total responses did you expect to get?
If you got 10 responses grading you as a 1, how many total responses did you get?
If you are going to compare expected versus actual, you need to make sure expected and actual are in the same units.
If you expected 100,000 responses with 1000 graded as a 1, but you got 1000 responses with 10 of them graded as a 1, you expected 1000 1’s but only got 10 1’s.  Sounds really good!  Except in both cases 1% of the responses were graded as a 1, meaning you got what you expected.  That is why I suggsted you work with % responses rather than the straight numbers.
To get your % responses you can copy your data column into Excel and use the COUNTIF function to gather the number of responses that were 1,2,3,…etc.  Now divide by the number of responses and multiply by 100 to get the % of each grade in the responses.
Do the same for your expected.
Now copy the expected and actual %responses back into Minitab.  Do an outline and groups histogram to produce a graph showing actual versus expected for each grade.
As far as being statistically different, the only thing that makes sense to me is whether the mean of the actual is statistically significantly different from the mean of the expected.  I would simply do a T test on the data collected for the % response.  Whether or not each specific grade is statistically different is visually determinable and I can see no usefulness for that determination.
Sincere regards,
Jim Shelor

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#151622

Ken Feldman
Participant

Sorry Jim, I have a problem with you recommending all this proportion and percentage stuff and recommending she do a T test. What happened to using tests for proportions? Melanie still doesn’t understand what she is trying to accomplish.

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#151625

Emperor
Participant

jim, you would do yourself and this site a huge favor by reading up on the very basic literature of survey research and how to analyze this kind of data before giving advice on a subject matter that is obviously way beyond your skill. it is sad enough that the original question demonstrates a complete lack of knowledge of survey research that can only be compounded by advice that demonstrates a similar lack of knowledge. stay with your pmp and ssbb credentials, but don’t parade as the emperor with no clothes.

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#151628

Jim Shelor
Participant

Dear Darth and Emperor,
I think I know what Melanie is trying to accomplish with her analysis.  If I am wrong, Melanie is capable of telling me that.  I do not believe Melanie is devoid of knowledge, as you two have suggested.  I believe she is trying to confirm here knowledge and seeking help from this forum to learn.  There is no more appropriate use of this forum than trying to learn and it deserves you respect, not your snide remarks.
I believe the ordinal data Melanie has collected lends itself to the method of analysis I have recommended to answer the questions she really wants to know.
The company has predicted the number of responses they expect to get for each category of answer (# of 1’s, # of 2’s,…etc).  These numbers are obviously based on a total number of survey responses (just add up the numbers) and a predicted distribution of the responses.
Converting the expected response data into something that can be compared to data from an actual survey that may or may not have the number of responses expected, demands that the data be converted to something that can be directly compared (e.g., % of 1’s or proportation of 1’s, which is the same thing in my opinion).
She wants to compare the grades of 1 expected to the grades of 1 actually received in the survey.  Secondly, she wants to know if the actual survey results indicated the company is performing statistically significantly better or worse than expected.
Having converted each grade category into proportions (% of total), a direct comparison can be made for each grade level.
Plotting a histogram with fit of the porportions of exected versus can graphically show the expected versus actual results.
She stated that the actual results looked like a normal distribution.  Assuming the expected results are also a normal distribution, a T test is an appropriate way to determine if the company’s actual overall performance is statistically significantly better or worse than the expected performance.
The overall performance is the only statistical difference I would be concerned about because the tells me something where a statistical difference between each grade doesn’t mean that much to me.
Obviously, since the counts of 1’s through 10’s are as high as Melanie says for the expected data, the ordinal data can be reasonably assumed to be continuous data and this method can produce the answers they are looking for.
That having been said, please provide some value added input on why this approach is wrong and your suggestions on how to proceed to produce the correct the method and desired answers.  Telling Melanie and I we are incapable of performing this analysis correctly because we are insufficiently educated to know the difference between what is right and what is wrong does not add value to the discussion.
If the suggested approach will work and will produce the desired answers, what difference can it make that a different approach would also work but would also produce essentially the same result?
Have a good day,
Respectfully
Jim Shelor

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#151633

Ken Feldman
Participant

Jim, I have already made my suggestions to you a few times. Basically stop treating it as continuous data and use proportional statistics. Because she said a histogram looks normal is a far cry from it actually being normal. Given the small data sets, everything will likely look normal. Plus a histogram provides a clue but certainly not evidence of “normality”. I think you and she are overlooking the major issue which is the low response rate. This creates a major non response bias that I haven’t seen mentioned or cautioned about yet. Having the actual responses in the same proportion as the expected responses doesn’t, by itself, provide useful information. The resulting responses must be representative of the population. And frankly, her expected responses are hypothetical while all she really has are the actual responses. So, comparing those are meaningless as well. An important contribution of this forum is not only the direct answering of questions but also the pointing out of fallacies in approach if appropriate. That is what I have been trying to do with Melanie. And yes, I will have to agree on some level with Emperor that Melanie has not stated her problem clearly.

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#151638

Jim Shelor
Participant

Dear Melanie,
From the expected numbers you have stated, the expected number of responses your numbers are based on must be in the 15,000 to 20,000 range.  Is this true?
How many actual responses have you received?  I was assuming you have at least somewhere on the order of 10,000.
Sincerely,
Jim Shelor

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#151642

Expectation
Participant

Unfortunately, research objective is so poorly thought through that it’s only worth pointing out the two most obvious fallacies:
1. When reviewing response rates (!) the unit of analysis should be the survey (unless you do a missing value analysis per item). Running a statistical test of “expected response per category” shows that there is no understanding of the purpose of investigating response rate, i.e. an assessement of non-response bias on the validity of the findings. Instead distribution fitting is conducted which is not an appropriate test in this context.
2. Measuring changes in response levels is accomplished by assessing the statistical effect of the change . This cannot be accomplished via an exercise in fit of distribution. Also, given the sample size, the main concern should to be with the effect size statistic, not the probability value.
In summary, a statitstical test developed for a very different purpose (distribution fitting) is misused to answer two questions that are not even remotely related to the purpose of this type of test. Before proceeding with any further statistical suggestions the underlying questions need to asked. After that it is worth diving into statistics.
P.S. The tone of your response is quite professional. “Chapeau” as they say in France.

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#151675

Mélanie
Participant

Gentlemen,

I greatly appreciate the 5 plus pages of discussions and take the blame for not being clearer in my question.  You are right that I am here to learn, like many of us.

The expectation set my management was that we would receive over 20,000 responses. They also went a step further in estimating that over 50% of the customers would put us in the 7-8 score area (10 being the best).  The extremes (1, 2, 9, 10) were estimated to receive under 5% each.

When the actuals came in, the response rate is low  under 10%, so this makes it clear that a non-response bias analysis is a must via a follow up survey.  But before doing that, we tried to determine if the actual responses met the expected values.  At a glance, by simply comparing the percentages of 1s, 2s, 3s etc into the total actual count, shows that the estimations were a little far from reality.  The extremes (1, 2, 9, 10) are not around 5% but around 2%.  The bulk (75%) seems to be between 6, 7, and 8.

For some scores we obtained a higher percentage than we thought, and viceversa.  Before I do the follow-up survey, is there a way to report any statistical results on these results, understandly that the follow-up survey is a must.

In a nutshell, Jim, you put it very well:
She wants to compare the grades of 1 expected to the grades of 1 actually received in the survey.  Secondly, she wants to know if the actual survey results indicated the company is performing statistically significantly better or worse than expected.
From your suggestions, thanks again I tried the following (good exercise):

Normality  none of the actual or expected percentages (or normal counts) are normal distributions.  The Anderson Darling P values are low.
Darths Two Proportion test for the two columns of percentages (expected and actual).  Not sure why I am getting this error.  Looking into it – Test and CI for Two Proportions: Expected, Actual  * ERROR * Column contains more than two distinct values.
Darths Chi Square test  expected versus actual counts by score (total 20 values, 10 for actual, 10 for expected by score 1-10).  P-value is 0.000.  Now what?
Jims Outline and groups histogram  with percentages it is just a big block with no bar on 20 and 30.  Not sure how to interpret.
Another one I tried was the 1-Proportion Test to calculate the upper and lower confidence interval for the proportion of respondents who gave each score.  Then what?

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#151689

Expectation
Participant

I am sitting here in awe about the level of ignorance that prompted your “managers”, not only to waste the resources to obtain 20,000 “expected’ responses, to waste their time estimating “expected” proportion of responses on a 10-scale satisfaction item, and now contemplating a follow up survey to understand “non-response bias”. If anybody in your company did some basic research or had a minimal understanding of the purpose of the exercise, they would have found out the following:
1. Currently, a 10% response rate is actually quite good (the reason why we now have these low response rates is because managers like yours have inundated their clients with surveys without an understanding of what surveys are supposed to do, and of course with no follow up actions). If you want to identify response bias and not aggravate your clients even more, review if follow up surveys showed differential response levels from non-follow up surveys … given the information that you gave, I doubt that anybody even thought about tracking responses by wave of follow up, or if you if you even noted when you received the survey, so that you could compare early respondents with late respondents … garbage in, garbage out).
2. You can be glad that the data is not normal, because if it was, you’d be out of business soon. One of the silly misconceptions about satisfaction surveys is that they should follow a normal distribution. If your average ranking is let’s say 7 or 8 with a typical standard deviation of 2 – 2.5 for a 10 point scale, you can imagine how many dissatisfied customers you have.
3. Rather than wasting time on estimating if your obviously clueless managers were correct in their estimations (what a joke of an exercise!) correlate the satisfaction score against a measure of loyalty, retention or share of wallet and include moderating variables that allow you to estimate where your satisfaction thresholds are. The fact that you have most respondents in the 6,7 and 8 categories tells me that most of your customers are not wowed and that you should not expect a high level of loyalty relative to your competitors. (Obviously, the mediocrity of the administration of the the survey reflects the mediocrity of the perceived satisfaction with the services provided by the company).
Unfortunately, I cannot be much gentler in my response as this whole project is miserably planned and executed and now you are dealing with garbage in, garbage out. Maybe Jim can help you with recovering some of the garbage. The statistical hocus pocus of running frantic t-tests, proportions tests and chi-square tests will not help you out of the mess of a project that obviously started with no clear strategy in mind. I’ll be amused to see what type of statistical voodoo this site will come up with to help you out of this mess.

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#151690

Ken Feldman
Participant

Melanie, thanks for the clarification. Expectation put it quite clearly that organizations luv to do their surveys without sufficient planning ahead of time. Most are fishing expeditions. To summarize what I understand to date. First, you got a 10% response rate so the issue of non response bias might be a concern. As Expectation pointed out, if you have historical data and it leads you to the conclusion that response and nonresponse have not varied significantly in the past, then maybe you don’t have to do a followup survey. If you don’t have that data, then you will have to consider the followup survey. Second, the tests that you have run so far seem to indicate that there is a difference between the proportions you expected and what you received. Ok, so what? Since the actuals represent reality and the expected represent management wet dreams you go with the actual data and then figure out what the hell management was thinking. Third, there is no magical statistical solution at this point. You report the proportions that you actually received and they are what they are. Figure out why they may be lower than you want and start planning on how you will improve customer perceptions. If you have really good operational definitions of what the questions were searching for you might have some direction. If the questions were not worded well then you will still be unsure what to do with the results. Finally, I would stop worrying about any differences between actual versus expected. The only time I might consider it is if the expected was based upon another survey or source of customer perception and the inconsistency might need to be explained. If it is just management expectation, stop wasting your time.

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#151693

Jim Shelor
Participant

Dear Melanie,
I hope everybody is sitting down!
In this case I agree with Darth and Expectation.
Given the, good for surveys, but extremely low response data you have.  There are no good statistical tests you can use to explain anything.
Further, since you are comparing to a guess by your managers, a comparison would be meaningless anyway.  A comparison to the results of a previous survey would be of some value.
The only thing you can do at this point is for your mangement team to recognize just how bad that rating is and start doing something about it.
This is like calculating a sigma based on 3 points and expecting you know what the sigma will be after 30 points.
Sorry for leading you astray based on my assumption that you had responses that were at least half the number the management guess were based on.
Nothing works for this survey except for management to take some improvement actions for customer satisfaction and using a survey later (after the improvement actions have taken effect) to see if you are doing better.
Sincere regards,
Jim Shelor

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#151695

Expectation
Participant

It looks like we are finally over the fenciful statistical stuff, and can move on to what can be done given the quality of the given data.
1. Create simple bar charts of response frequency by category 1 – 10.
2. Review if the categories 9 and 10, and the categories 1 – 4 respectively exceed 5% of responses.
If 9 and 10 have > 5% of responses, good for you, because these responses typically result in loyalty. If the categories 1 – 4 exceed 5% then you need to review that because these are clients at risk. Clients in the categories 5 – 8 are relatively satisfied and the correlations to actions/behaviors are typically low (they have multiple suppliers and switch and/or distribute volumes = share of wallet based on price or convenience factors).
3. Finally, satisfaction scores in B2B settings have lower predictive validity than satisfactation scores in C2B settings. The main reason is that in many C2B settings the relationship between customer and vendor/supplier is based on simple transactions and switching behavior has low costs. Customers “hedge” by having multiple suppliers and decisions are driven by considerations such as price, convenience of location or other factors. You will need a very high level of satisfaction to induce loyalty or create bundled services and work on contractual relationships.
4. If you want to identify key drivers of satisfaction, correlate the attribute satisfaction scores with the overall satisfaction scores.
5. To get a rough idea of the relationship between level of satisfaction and loyalty, correlate the overall satisfaction score with a scale measuring “recommendation”, “likelihood to continue using the service” and/or “likelihood to purchase other products”.
6. Please go to amazon and buy a basic introductory text on conducting customer surveys. Any of the available books will be helpful to you and your company!

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#151715

Mélanie
Participant

Guys,
Thank you.  This has been a good learning opportunity.

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#151721

Mélanie
Participant

Jim,
You are making me curious – how would you perform a comparison of actuals to the results of a previous survey?
Thanks,
Mel

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#151722

Mélanie
Participant

Darth,
“The only time I might consider it is if the expected was based upon another survey or source of customer perception and the inconsistency might need to be explained”…
I am still curious as to how I would compare two sets of data.  Can we assume the expected are last year’s or another survey – how would I compare the data sets then?

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#151725

Jim Shelor
Participant

Mel,

The method would be essentially the same one derived here.
But if you have two actual results for essentially the same survey that are 6 months to a year apart, that comparison will tell you if you are getting better or worse.
Sincere regards
Jim

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#151750

Ken Feldman
Participant

As Jim said, assuming that the surveys were comparable, you can do a “before and after” using the same tools as before- 2 proportion or chi square.  If the surveys are apples and oranges and get at different issues then a comparison doesn’t tell you much.  One shot surveys are useless because they only give you a snapshot in time.  Ongoing monitoring of critical customer requirements is important to spot change.  Then again, there should be adequate internal metrics that are leading predictors of customer satisfaction.  You don’t wait until a customer survey to find out that you suck.  You might use them to check perception and whether it is in alignment with how you think you are doing relative to what is important to the customer.

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#151759

Expectation
Participant

“One shot surveys are useless because they only give you a snapshot in time” … at some point these “useless” surveys had a name: “cross-sectional”. It’s almost too ironic how confused even experienced six sigma professionals are about the measurement of their most critical dependent variable:  customer satisfaction. So much time spent on statitistics and so little knowledge about interpreting changes in those highly revered “satisfaction scores”. There is, after all, a healthy dose of pop culture in this all too scientific enterprise: Six Sigma.

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#151763

Jim Shelor
Participant

Dear Expectation,
I agree that some Six Sigma professionals have lost sight of the fact that customer satisfaction if the end goal of improvement efforts.
I, for one, have not.
A properly designed Six Sigma program has a strategic plan with numerous improvement projects planned and scheduled in a manner that provides the most improvement at the least cost.  The schedule is developed in a method that evaluates the effect on customer satisfaction as well as ensuring that the improvements are conducted in the most efficient, effective manner.
If I were one of those guys that has lost sight of the primary purpose of improving quality and cost, I would probably be insulted by some of your comments.  As it is, water off a duck’s back.
Best regards,
Jim Shelor

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#151764

Jim Shelor
Participant

Dear Melanie,
Surveys that cover the same or similiar projects can be used to compute a performance index, such as the mean of the survey responses (only for the similiar questions).
Now, instead of just looking at the data, you can test to see if the change is significant or just random noise.
Say the mean of the new survey is higher than the mean of the last survey.  Conclusion, we are getting better.  Maybe, Maybe not.
If you run a simple T test, you can determine is the means at statistically, significantly different.  If the means are not significantly different, you have insufficient evidence to conclude that you are getting better.  Accordingly, you have insignifican evidence that the improvement actions you have done so far have done you any good and you need to work harder on improvement.
Sincere regards,
Jim Shelor

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#151777

Ken Feldman
Participant

Expectation, not sure the point of your last post.  Where you just offering an opinion or did you have a problem with my statement about the value of one shot surveys.  Surely, you don’t advocate doing a cust. sat survey once and then assuming that everything will be OK going forward?  Given how fickle and quickly customers can change perceptions due to a slippage in customer service or a competitor that’s finally got it together, any one time survey has little value except for a specific window of time.  Your statement, “……so little knowledge about interpreting changes…..” seems to indicate that you support my comment that one shot surveys have only fleeting value.

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#151780

Expectation
Participant

I’ll leave it up to you and every six sigma professional to dig into the strand of literature that started with Cadoza’s article in the Journal of Marketing (1965), and read up on the last 42 years of literature on the antecedents, determinants and consequences of satisfaction. It is just amazing to what degree quality and six sigma have missed this whole strand of literature and still religiously repeat Kano’s conceptual model from the 1950s. I hope that on its way to six sigmatizing marketing, six sigma may learn a thing or two from an adjacent strand of business research. What a heretic idea that six sigma may actually benefit from learning something about its most cherished assumptions. I assume that we agree and disagree.

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#151785

Ken Feldman
Participant

OK, now I understand what I am dealing with. I ask a straightforward question and get a rambling pontification citing old articles and a mandate to go read about everything that I don’t know. Guess you are the type of poster that will never contribute to a learning experience beyond pretending you know it all, everyone else is ignorant but determined not to share that self assessed depth of knowledge. OK, let’s try it again, regardless of what Cadoza said in 1965, do you ascribe to the policy of doing one shot surveys or do you feel that multiple surveys are needed to monitor changing customer perceptions? Not sure what that has to do with Kano. I would suggest that you read a transcript of Dr. Deming’s 1950 speech to Japanese Management in which he discusses market surveys. I believe his credentials, publications and knowledge of surveys exceeds your beloved Cadoza. Oh, by the way, I assume you meant all five pages of CARDOZA’s article…BFD!!!R.N. Cardoza, An experimental study of consumer effort, expectation and satisfaction, Journal of Marketing Research 2 (8), 1965, pp. 244-249.Now you definitely look like an arrogant fool. If you are going to cite someone, at least spell his friggin name correctly and cite the correct publication.

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#151786

luke skywalker
Participant

Glad to see you’re still yourself.

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#151787

Ken Feldman
Participant

And you’re just a chip off the old block. I hate these pedantic know it alls. At least you and I do know it all :-). Did my wedding invitation get lost in the mail??????? Hope all is good.

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#151788

Expectation
Participant

Darth,
That was really not one of your most witty responses (… sometimes you show your emotions too obviously, which only underlines your lack of knowledge in the subject-matter). That little turn back to Deming is even more amusing. By citing Deming you show that your knowledge has not increased since 1950! A great position to lecture this site on the issue!
Aside from that, I’ll just site the two statements that most blatantly show your lack of more current knowledge of the topic. Again, I’ll leave it up to you and the rest of the site to see what’s so funny about these two statements. As you know it all, you’ll figure it out.
One shot surveys are useless because they only give you a snapshot in time

Given how fickle and quickly customers can change perceptions due to a slippage in customer service or a competitor that’s finally got it together, any one time survey has little value except for a specific window of time

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#151798

luke skywalker
Participant

No, your invitation did not get lost  – I have to protect my bride form the dark side as long as possible. Should be a fun party, so watch your mailbox…
We’re exploring the deeper levels of chaos theory here these days – How’s the placebo business treating you?

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#151799

James Considine
Participant

The one thing I’d add to the discussion picks up on the earlier comment about the 9’s and 10’s. Harvard Business Review ran an article a couple of years back about using something called the Net Promoter score to gauge customer satisfaction.Basically, you ask customers if they would recommend your firm to others. Take the % of 9’s and 10’s, subtract the % of 1, 2, 3’s and track THAT number over time. Some firms actually measure managers’ performance using this metric (Enterprise Car Rental, for example)The other thing I always emphasized to our management when I analyzed surveys were the verbatim comments. Often these were far more rich than the scores themselves.Good luck – sounds like your managers may be wanting some “validation” of whatever it is they’re doing, and the survey was chosen as the weapon of choice.

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#151801

Ken Feldman
Participant

Hey Luke, glad things are moving along on the personal front.  The placebo business, as you put it, has allowed me to give up anti-depressants because things are so calm and stress free.  In this case, one door closed and a better one opened.  Did see TB at the FLL airport a few weeks ago.  He was heading back to CLT after being on a cruise.  We caught up a bit.  I’ll go check the mailbox again.

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#151807

Proxy
Participant

The net promoter score was a nice tool years ago. It suffers from the same problem that it tried to remedy: method-method artifacts. The area has moved on to measure retention and share of wallet. Today, the net promoter score is a proxy when a company cannot or will not tie the score to actual behavior.

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#151835

Anonymous
Participant

Expectation,
You seem the embodiment of the current trend of poster – spewing plenty of accusations, citing obscure documents and contributing little or nothing with your cyber graffiti. You appear the fool particularly with a comment such as: “Again, I’ll leave it up to you and the rest of the site to see what’s so funny about these two statements. As you know it all, you’ll figure it out.”
It does appear you could not figure it out so you portray it as if there was something to figure out, you know what it is and only those as intelligent as yourself will be able to figure it out as well. What a pathetic position.
Make a contribution other than insinuations and innuendo.

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#151840

Expectation
Participant

if it is was worth my time, i would give an explanation. however, 1 + 1 = 2. what is there to explain when a complete novice calculates it as 1 + 1 = 3? … isn’t that the standard answer that you give to those novices who ask those very basic questions about stats? i can’t even disagree with you because why answer the obvious? cheers, it’s a beautiful day where i am now. keep up the good work. you all seem to be enlightening each other quite happily.

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#151861

Anonymous
Participant

More cyber graffeti. Your post bears a strong semblance to the Turner Networks cartoon character making an obscene jesture with the difference being that regarless of the outcome they managed to deliver their result. At this point you have delivered nothing other than noise.

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#151870

Expectation
Participant

have a wonderful day!

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#151895

Heebeegeebee BB
Participant

ALL YOU BASE ARE BELONG TO US.

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#152052

Murphy
Participant

Darth,
So, glad you responded. I had the same thought (minus the “wet dream”). Why do we try to overcomplicate things? What difference does the expected response make to the actual response? All that is shows is how out of touch with reality managment may be regarding it’s customers.
Thanks for the reality check.

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#152055

eric
Participant

The test you want is a Chi square test.  Perhaps other replies have indicated this.  This is a rather low lwvel test and does not utilize the real power of the data that you have.  The scale is an interval level scale which supports much more robust tests.  The Chi Square test is an ordinal/nominal level test that approximates a more robust correlation.

0
#152934

BTDT
Participant

Melanie:I am in the midst of defining some rigour around the analysis of NPS. I am very interested in talking with you.Cheers, BTDT6SigmaGuru(at)gmail(dot)com

0
#187629

Kulkarni
Member

Guys,
Till today Iam using Customer Satisfaction index to assess the satisfaction level. Pl. let me know when should I use Net Promoters Index.
What are the main differences between these two.
Kindly let me know.
Regards
Vidya

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