In our day-to-day work as a firm that conducts customer satisfaction and experience projects and surveys, we often come across the same dilemma:

• We want to ask and find out lots of details about our customers…
• But we can’t ask everything we’d like to because the survey would be too long (which would lead to other problems such as abandonment, poor quality responses and increased costs, among others)

Electronic customer surveys.

In this post, we outline three basic strategies that we use in customer surveys to solve this problem at least in part and achieve what may otherwise seem impossible: ask more with fewer questions.

Three basic strategies for asking more with fewer questions in electronic surveys

1. Pre-segmentation

This strategy could alternatively be called “Never ask what you already know”. Although it may seem obvious, many customer surveys and forms do not heed this rule and ask customers about aspects that are already in the company databases. This should never happen. A good survey should pre-load this data to make it available during the survey and results analysis and not pass on to customers the internal data management shortcomings of the company.

Generally, two typologies of customer are surveyed:

A. “Known” customers (or B2B customers, on whom we already have information in the CRM or other data sources).

These customers are in the company database and we probably already have information on aspects such as:

• The name of the customer or their address
• The area where they live or the store where they usually buy
• The type of products or services that the customer purchases
• Their annual revenue or classification in the customer ABC
• The sales channel they use
• … and many others

B. “Unknown” customers (or B2C customers, anonymous customers, such as customers who go to a restaurant chain or visit a website).

Although these customers are not in the databases, it is also possible to pre-segment them using information that we may already have, such as the geographical area or establishment where the survey is conducted. We can also obtain automatic information on customers through other means, including the use of social WiFi (the customer gets free wireless access in exchange for sharing details of their social profile).

2. Conditional questions and answers

We could also call this strategy “Don’t ask what doesn’t concern you”. The aim is to make sure – based on the responses and customer data we already have – that all the questions asked of a customer are relevant and can be answered.

To adapt the survey to the customer profile, we need to use conditional questions and answers, hiding and showing whole parts of the customer satisfaction survey based on the information we already have. For example, if we have preloaded customer data, we can ask only about the specific product or service that we know they use.

The ultimate aim is to ensure that all of our questions are relevant and can be answered by the customer and that we are not wasting our “shots” (or questions) unnecessarily.

3. Random questions

This strategy could also be called “Divide and conquer” or “Jigsaw survey”. The idea is to divide the survey into parts as small as we would like so that each customer only answers a few questions. The questions will be different for each customer.

This system allows us to obtain aggregate results (responses from all customers) with data on answers to lots of questions, but with partial samples for each. In other words, we can design a survey that is as complex as we like provided that our sample is large enough to be divided into small parts that can be selected at random to minimize any bias (most survey software incorporate this option).

Conclusion

Typically, a good satisfaction survey design gives us far more information on customers – both their characteristics and their opinions – without having to answer to tiresome questionnaires.

Three possible strategies are:

• pre-segmentation
• conditional questions
• random questions

With these strategies we can achieve the seemingly impossible: ask more with fewer questions.