For too many, improving the customer experience means relying on an occasional static survey instrument or quickly analyzing customer data in the context of an emergency. But designing a successful Customer Experience (CX) program requires constant iteration and delivery – and should be a seasonal process that frequently tests new initiatives and guides decision-making.
This seasonal process should consist of seven closely related stages. So far in this series, we’ve talked about the first two: designing a customer experience program (creating an overall ‘big picture’ plan to improve the customer experience over time) and designing a customer experience. customer experience project (taking the overall general principles and objectives of your program and applying them to specific actions).
The third step in this process is the design of the sample.
What is the sample design?
Sample design is the framework you use to involve the right customers in your CX program and projects. Every time you design a sample for a project, you are laying an essential foundation for creating reliable and functional information for your organization.
If you can easily answer questions like “Which clients should I select for my project?” “” What is the addressable market for my product or service? “And” What criteria did I apply? – then you are ahead of the game in CX. You need to know how many customers you have, who you are marketing to, and have a clear idea of the natural customer segments. If you have the numbers “out there” or need some time to put them together, it’s time to prioritize a broad internal socialization of these metrics.
Most executives and other stakeholders will make sure you’ve selected the right customer group. Before writing a study, make sure you know who your sample will be. The type of study you undertake and the actions you plan to take should inform your sampling methodology.
7 Considerations for sample design
1. Chance is everything
Whether you are a biologist or a social scientist, the way you select your sample is what makes your research reproducible. The only way to get close enough to say with confidence: “It is! Is to make sure that your sample is “really random”. Unintentional criteria or unexamined periodicity in samples can impact the analysis and lead to assigning meaning where there is none or where an influencing condition is missing. (Note that other probability-based or non-probability sampling methods can also be very useful in moving a CX program forward. It is important that you understand what metrics your stakeholders are looking for).
Improving CX design is more important than discussing statistical theory internally. Checking your sample for randomness or using clear criteria to take the sample will quickly answer any questions, getting you back on track to meet customer needs. Be able to quickly show the selection criteria and confirm that the sample was selected entirely at random from this group.
2. Plan and avoid sample errors
Ensuring randomness is more difficult than it looks. Some customers are more likely to respond online. Some never respond. Most only respond when they need an answer. Because this is your reality, you must have a clearly defined and socialized sample plan before setting up a CX project. Consider the sampling plan for each project individually. Good CX programs build this into the project design movement, because there is no such thing as an “example program.”
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3. Use time criteria
Suppose you want to survey all the customers who called your Dallas call center from January through March. You want to know which calls have and don’t have contact information, and you want to look at the percentage of customers who had their issue resolved in one call, as well as have a plan for responding to those who didn’t. have not. Select your sample carefully to match the objectives of each study, especially in a transactional study.
4. Do not oversample
If randomly selected from any population, 384 people constitute a sufficient sample. Your results will be within 10% 95% of the time. A higher level of trust becomes much more expensive at this point. Remember that you can sample 500 randomly selected respondents from a population and they will accurately reflect the actual population 95% of the time. It bears repeating: the secret to success is random selection. Do not overload the sampling design. A methodology sample should match the population under study.
5. Confront small sample sizes and response rates
If you try to make a sample the source of many abstract questions, your program will suffer. If you focus on the right sample, you can ask a focused question at the right time. Your response rates and completion rates will increase as your customers begin to see that communicating with you is low-key, easy, and beneficial to them.
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6. The account mode
Match your survey method to the sample you select (and vice versa). Who are your current customers? Do they have different characteristics or preferences from your potential market? If you sample grandparents, don’t expect responses from a QR code. A mixed method approach can help drive action in different customer segments.
7. Action is the goal
Too many CX programs run in perpetuity, ignoring annoying comments. If I share with you a particularly bad experience I have had with your business, an incoming phone call from the CEO or local manager can make your business a customer for life. Use actual numbers and people by default whenever possible. At some point, everyone needs to be reminded that the goal of customer experience is to increase behaviors that generate revenue by creating great experiences.
A great customer experience often means that the customer was treated like a person rather than a number. Use a well-selected sample that follows the assumptions of your quantitative method to make those interactions with customers real. Careful selection of your sample will prepare you well for questions from your leaders and stakeholders and, more importantly, allow you to better understand the actions of individuals.
Editor’s Note: This is the third article in a seven-part series on customer experience design. Check back soon for the next episode.
Eddie Accomando, XM Scientist at Qualtrics, is an applied anthropologist with 25 years of experience designing, deploying and maintaining enterprise-wide CX programs. A strong methodological focus can be placed on real world programs, and it applies both qualitative and quantitative research techniques to reveal ideas that lead to action within organizations.