The Net Promoter Score (NPS) methodology uses a 10 point Likert scale to calculate the all important metric. I am not sure whether any research was done before a 10 point scale was chosen, because the scale certainly can influence the metric. In general, this caused me to wonder how to decide whether a Likert scale survey question should have a 5 or 10 or any other point scale.

A quick google search pointed to some academic research. While there are no rules of thumb, here are a few guidelines to consider when you are deciding on the scale.

1. Respondent knowledge of subject matter:
1. If the respondents are not very familiar with the subject matter – they tend to abuse the endpoints of a longer scale.
2. Respondent frames of reference:
1. More response options introduce error when respondent group has very different frames of reference.

So if you are asking about a tax filing software and the respondents have just used the product to do their taxes, then their knowledge of the software and their frame of reference will be relatively similar – so a 10 point scale can provide more nuanced results.

However, if you are doing a survey to determine whether the respondents will sign up for a new wireless phone service – where wireless usage and needs of the group might significantly differ and also their knowledge about various services – a shorter 5 point scale might be the better option

Update: NPS does have a whitepaper on the topic where they justify their scale

If you want to develop an analytics capability within your organization – should you be talking with business intelligence software vendors or data analytics service providers?

Jeff Kaplan in a recent article in DM Review makes a case for the latter.

Delivering predictive analytics is not a trivial exercise. It requires the skills of being able to map the marketing goals to the appropriate predictive algorithms, perform data hygiene and transformations, build models and test the results.

Moreover, implementing predictive analytics requires the combination of three distinct skill sets:

1. database technology
2. data mining and
3. marketing domain knowledge

He goes on to identify executive support and expertise of the partner (in understanding data, statistical modeling expertise and database skills) as the two success factors of such initiatives.

My take: The principle of service before software in analytics makes sense because of the complexities that Jeff identifies (see related posts on the topic here and here). A service first approach also helps to rapidly identify the specific areas within the organization where analytics will be a quick hit due to data availability, quality, and acceptance of analytics within the group.

Another aspect which we have found to be critical in the success of analytics in the organization is being able to generate the right set of hypotheses for the analysis. The hypothesis driven approach helps an organization to focus on the problems that matter. A service provider, from prior experience, can help to generate a long list of hypotheses. However, in our experience it is critical to identify and recruit savvy individuals from within the organization, who have deep understanding of the day-to-day business operations and are passionate about improving the status quo with better analysis of data. They are able to pick out the specific hypothesis and analysis that can help the group to make better decisions. We call them ‘insight managers’. Normally, they become the analytics champion within the group and are vital to its broader acceptance.

Diamond has developed a model to deploy analytics within an organization and ‘insight managers’ play a major role in it.

Steve Shu had quoted his wife (Suzanne Shu, a Professor of Marketing at Southern Methodist University) on the issue of indefinitely delaying reward redemptions.

Shu’s comment is interesting from a behavioral model for rewards redemption point of view. A credit card holder will be interested in those redemptions that suit his choice and utility function. True- a rewards program in itself manages to alter the choice function available for the credit card holder.

The linked article and another US News article highlight some interesting attributes of rewards redemption by consumers

1. Incentives –
1. Leading to purchase acceleration
2. Guilt/Excitement of opting for something that they would not otherwise
2. Disincentives –
1. Indefinitely delaying an aspirational redemption for a special occasion
3. The Cost Benefit Analysis -
1. Cost of Card
2. Benefits from Reward
3. Cost of payments/credit card usage

The closest we can come to creating a consumer choice/utility function in the database driven world is by collecting and integrating 5 different sources of information –

1. Demographic Data – Age, Gender, Ethnic Background, Education level, etc. Available through census, application forms filled by customers, etc.
2. Economic Background – Income levels, saving and investment data. Available through application forms, relationship level view of a customer, etc.
3. Behavioral Data – attitude, interests, etc. Can be collected through surveys and lifestyle data bureaus.
4. Bureau Data – e.g. risk bureaus such as Experian, FICO score, or lifestyle data from Claritas.
5. Transaction Data – prior credit card usage data, payment data, etc.

Demographic and behavioral data together would be ideal to define a person’s wants, while to build a choice model, it would be a good idea to include Economic and Bureau data as well. The transaction data can be used for validation and refinement of the choice model.

Back to the articles, a choice model can help us explain and manage all three aspects–

1. Incentives – through a greater understanding of choice limitations and utility functions, we can customize offerings that maximize the utility for a customer at the lowest possible cost
2. Disincentives – any alternative outside the “want” area and significantly beyond the choice area suffers from possibility of being delayed, as pointed out by Suzanne Shu.
3. Cost-Benefit Analysis – an economic model of choice can bake in the tangible costs of a reward program to optimize profitability.

Some of our other posts on credit card analytics are here

Firing customers is not something companies prefer to do for profitability. Fierce wireless reports that Sprint recently fired some customers because of their frequent calls to customer care.

Ever wonder how many calls to care can actually get one fired from the wireless company, now that the precedent has been set? We did some back of the envelope analysis to understand it better.

Assuming that the fired customer paid an average bill of $60 per month and that Sprint has 30% margin and average cost per call of $4, it will take 5 calls in a month to make one a negative value customer. However the trick of all lifetime value calculations is to estimate future value of customers because you can be unprofitable in one month but go on to upgrade to a highest plan down the road. Since there is no reliable way to predict it, we will be conservative giving the benefit of the doubt to the customer. So to get fired, the customers must be making way more than 5 calls a month (probably more than 10-15) and that too for a few consecutive months; so that Sprint had no hope of making a profit in future from these customers.

While it is certainly a sound financial decision, it will be useful to determine whether these customers had anything in common (type of problem they were calling for, expectation setting at sales channel etc.) which led them to call more often.

Photo Credit: Pappalicious

A recent paper in the Journal of Marketing reports on a study done to examine the Net Promoter Score (NPS) and firm’s revenue growth and found that NPS is not the best measure to predict growth as claimed by its proponents.

NPS methodology recommends conducting the customer satisfaction survey with one question: How likely are you to recommend company or product X to your friend or colleague and the answers are measured on a 10 point scale. People selecting 9 or 10 are called promoters and those selecting between 0-6 are called detractors. The net promoter score is the difference between the two numbers and claims to predict firm growth.

The major issue with NPS is not that it does not predict growth, but the fact is that it is not actionable. NPS does not link to any specific business metrics which the company executive can influence.

As Larry Freed points out:

What you measure will determine what you do
The results of your measurement will determine where you decide to allocate resources. If you’re asking survey questions about the wrong area, then you’re going to invest in improvements to the wrong area without improving what you set out to improve.

Customer satisfaction is a complex metric based on multiple dimensions along which the customer interacts with the company. A carefully designed survey questionnaire based on hypotheses which link to clear business action is the first step towards understanding root causes of customer pain points and identifying actions which will ameliorate them.

Photo credit: Zoom Zoom