Make better marketing, product, and business decisions, and keep your customers top-of-mind while doing so.
Outline your goals and plan your approach by defining what you need to know, who you will survey, and how you will reach them.
You’ll need to have a certain level of user knowledge to do this. Using simple tools like Excel and Intercom to trace common behaviors can give you a better idea of which types of customers are worth the most money to you.
Keep the survey short enough that people would actually take it. Data collected by SurveyMonkey shows that the longer a survey is, the less time respondents spend answering each question, meaning they’re more likely to rush through it or abandon it altogether. Use a mix of categorical questions:
- Multiple choice like, Which best describes your company?
- Scale questions like, When purchasing [product], how important are the following factors on a scale of 1-5?
- Open-ended questions like, What’s the most challenging skill used in your job?
Use a tool like Typeform or SurveyMonkey to create and send the survey to your customers or audience.
Who you send the survey to depends on your company stage and industry. For example, a startup may have few users, let alone engaged users, and therefore needs to find non-users who are representative of their target customer. A public SaaS company may have dozens of different personas and may need to segment them by what they’re trying to learn in this research project. This is a question a UX researcher needs to determine with the key business stakeholder for the product. When in doubt, try to get at least 300 responses from engaged product users or nonusers who are representative of that target persona.
300-1,000 survey responses make for good data, but there’s no magic number. Factors like the survey quality, audience targeting, and your own data analysis skills all matter more than pure sample size.
- Organize your data into rows and columns in Excel, R, or your preferred statistical tool.
- Add survey variables as columns and responses or observations as rows, and get rid of any blank cells (N/A values).
Your data should look something like this:
Look out for factors that predict how people answer certain questions. There are many ways to carry out exploratory factor analysis in R, but the most commonly used method is the out-the-box factional() function. Another popular way is to use the psych package. Consider using principal component analysis, which is similar to and often confused with factor analysis, if exploratory factor analysis doesn’t reveal much.
Make sure you scale and center your data if you have certain variables that are much larger than others. For example, scale and center your data if you have both answers on a scale of one to five and answers that go into the thousands.
Here’s an example of how scaled and centered data can be created in R and organized into hierarchical clustering to produce a dendrogram, which is a tree diagram used to illustrate the arrangement of the clusters produced by hierarchical clustering:
Alternatively, you can use [k-means clustering](https://en.wikipedia.org/wiki/K-means_clustering) to pick the number of clusters you’d like before you do the analysis. Here’s an example showing clusters for three personas with lots of overlap caused by common responses: ![K-means clustering example showing data overlap for three clusters due to common responses. ](https://lh3.googleusercontent.com/cZdz3SdUjANhUvFd1Vy5Ka1jZfOpzpvcCDhoIpNFP_qzenz3AkO8AyP2_hdpMQaQpKeqqOdTnw6OMwtgzzemm-rYciVCW4nHVETr6pvfO8N6x7Z9iWraPQzAU9otEhNckP3CmioP) ## Use pivot tables in Excel to interactively explore persona data from different angles. Pivot tables allow you to place different variables in different columns/rows and analyze factors like averages, standard deviations, and sums. There’s no fixed order process for analyzing data using pivot tables. Spend time exploring the data, looking for surprises and things you didn’t expect. For example, you can then set up different worksheets with the respondents that fell into each cluster from your cluster analysis to see how they answered the various survey questions. This allows you to compare the means of different variables to find key differences and common data. You can also create pivot tables that contain more than one-row variable. Here’s an example of one that shows the average rating of how important cost is by type of company and seniority of employee: ![Example of a pivot table showing the average rating of how important cost is by type of company and seniority of employee](https://lh5.googleusercontent.com/zqCtYuKESn_jOHZhnptlcujSs6w-dpMKEjg8ubxHDgcK5xqEUM33Kv_yMHKsqb_EQpqtuP78rPBgzOl2_25DqKBAkCWglTAYyfcNzHScdSaq42hxLv1Rh3mGF6WTbHpUjheWj4sp) ## Clean up the qualitative data from open-ended responses and use it to determine qualitative differences between your personas. Codify responses to help quantify information. For example, responses like *Adobe Analytics* and *Google Analytics* can both go under an ***Analytics*** category. Word clouds built using qualitative variables or categories can help you get a high-level view of what your audience is saying. At this point, you should be able to fully segment your audience based on the information you asked for in your survey and the responses you received. ## Conduct 2-5 one-on-one interviews for each persona to find emotional triggers that can help you with messaging strategy, emotional targeting, and design. Take a journalist’s attitude and go into the conversation with a sense of curiosity and exploration. The point is to make it feel unlike an interview and more like a conversation with a close friend. Aim for detailed answers rather than surface-level answers you can look up in product analytics. Ask deliberate, open-ended questions designed to spur emotion and depth. Some examples include: - What’s your role at your company? Explain at a high level and day to day. - What is your career background? - What’s the single biggest challenge at your job? - What are you motivated by? What keeps you up at night? - Whose advice do you trust? Who do you follow or ask when you have a challenge? ## Use a tool like the [Buyer Persona Toolkit](https://buyerpersona.com/) to organize your data and design and communicate your buyer personas. Get creative and design and communicate your buyer personas, so they actually get shared with your team and used in decision-making. There’s no one way to do this, the important thing is to include actionable information that can be effectively communicated without further questions being raised by teammates who haven’t seen all the raw persona data. Here’s an example template from [buyerpersona.com](https://buyerpersona.com/) that provides granular and actionable information segmented into tabs: ![Example template from buyerpersona.com that provides granular and actionable information segmented into tabs](https://lh5.googleusercontent.com/zHKq-G4pLJIOkR7o8cTLMlh2GwKn9iVj5gAKAKnWdP9uDiatv7ewCRWLoPvunf_RXsyrAktx7BZnMq_9IuTVjfmcTQs9OmNFYvay0Mtj_WRGSv3rAgzxrk7aJ3ahzijIWnP26h6Y) Alternatively, you can map out your buyer personas on [GE / McKinsey Matrix](http://www.quickmba.com/strategy/matrix/ge-mckinsey/) to gain a clear understanding of how ideal each buyer persona is compared to how strong your business case is for them, allowing you to visualize where the best opportunities are for future marketing efforts. Here’s an example of what a GE / McKinsey Matrix would look like with 3 personas. The top left persona ***Persona 3*** is the most ideal and attractive customer, yet the business is least effective at selling to them, while the bottom right persona ***Persona 1*** is the least ideal for the business, but the business is selling to them the best. Therefore, the opportunity lies in ***Personas 2*** and ***3***. ![Example of GE/McKinsey's Matrix showcasing business effectiveness vs. attractiveness for 3 buyer personas. ](https://lh5.googleusercontent.com/jqNpDpq1Gx8nW4zNJHzZ-lLt7iQySpKRF2cpYgY31HdxJfBr6QjG1kPWHxnlNx4ca3YQrB-sLIkV6p9hmPkhf0s0CAIzm-nQq3307nTzPfo2gVO3cKAGw2yc5x_0QtiQLBx1slD_)