In this article, I share insights into what are pain points and how can personas — and more specifically the Automatic Persona Generation (APG) system that was built by our team help identify customer pain points.
Expressing customers with pain points as customer personas can contribute to developing better products and services, ultimately leading to a delightful UX for the end-user.
What is a pain point?
A pain point refers to a very specific problem that customers of your particular product or service are experiencing. You can think of pain points as problems – plain and simple!
For example, if a customer is using an e-commerce platform, and if the checkout and payment process is rather lengthy, then they might just quit and jump to another e-commerce provider. This is a pain point, which if a product team is able to identify and correct, can boost their conversions.
Another example of a common UX pain point is the payment issue that users face when they want to pay for a product or service online. There is evidence that nearly 68% of users abandon their shopping cart when trying to make a purchase. Reasons for this could be numerous such as site flow not being properly designed, long and complicated check out process, etc.
Take the following example from a major British multinational retailer that specializes in selling clothing, home products, and food products.
One weird thing about their checkout process is that the option for guest checkout is not prominent whatsoever on the site and if you are like me and do not like the user interface to interrupt your flow, you might need to spend time out to figure out where this option is! This is a pain point with service that can lead to customers abandoning their checkouts!
Identifying this pain point requires user research, user focus, and usability tests — basically, trying to identify what it is about your product or service which is frustrating for the user!
Value of personas in communicating customer pain points
Personas are fictional persons representing a group of similar users or customers of a product or service. These could be created either manually or automatically (more on that later) based upon your research to represent different customer types that use or might use your product or service.
An example of a persona is presented below:
As you can see from the figure above, a persona profile contains details about the customer such as demographics, needs and goals, motivations, and also pain points.
In the above example, notice the pain points that this persona has.
For instance, reading her pain points makes me think of Alex as an active and methodical person, who is busy and needs one place to check weather conditions for different activities and nearby locations. But, the current apps that she is using are not attractive (i.e. have a bad user interface), inaccurate, and overall have created a bad UX for her.
Now, what does this pain point tell us? For UX practitioners, this tells us that there is a potential market here for the development of a good weather application that is accurate (perhaps draws and displays information from multiple sources and updated regularly) and has a modern, clean interface! Essentially, these pain points informed the need for the design and development of a dedicated application for a user segment that matches Alex!
An added value of using personas is addressing customer pain points often means the involvement of many teams, managers, executives, and others. All of these teams must communicate with each other while keeping the customer in mind. Personas are great for this! They add the human touch to what would otherwise be cold facts from your research. People can relate to personas more easily, as the portrayal of real people in them is designed to minimize reliance on personal viewpoints when discussing customers’ experiences, perceptions, and mental processes.
It is safe to say that personas play a crucial role in helping identify customer pain points, which in turn governs design decisions for a product or service, which if done correctly can lead to an overall good customer experience.
Use of personas to express customer pain points – A personal example
During my doctoral research work at Tallinn University, I developed a course on trust in computing. The main assignment for the students was to use a scale (fancy word for an online survey) that I developed to measure trust in technology and apply it to their projects. One consistent feedback that I received throughout the course was that the application of the scale is exceedingly difficult, especially for students who have no background in statistics.
Since the class in which the course was taught also included practitioners, such as UX researchers, UI designers, etc., and some of them had to measure trust in actual industrial projects that they were a part of, they provided important feedback of having an online tool which can be developed to measure trust, results of which are automated and can be visualized easily.
I took these comments seriously and wanted to dig a bit deeper in understanding the frustrations and pain points of two particular sets of users:
- Students taking the course.
- Practitioners such as UX researchers taking the course.
I had to conduct extensive user research with both the above types of users and finally, was able to understand their actual pain points and frustrations when taking the trust in computing course and their need to have an online tool.
I took the liberty of summarizing my results with the above two user segments in the form of two personas – a student persona and persona of a UX researcher. These are depicted as follows:
As you can see from the above two persona profiles, I was able to identify two main points for two customer segments:
- Both the personas needed a simple easy to use online tool for creating surveys to measure trust.
- Both the personas wanted the results of the survey to be displayed in a nice visual way, without the need for manual computation or running complex statistical analysis!
However, the creation of these persona profiles took a lot of time. I had to first wait to get an appointment to carry out the research work with different people, following which I had to analyze all the data and, finally, I was able to create these personas! I kept Leena and Kristiina as I revamped my scale.
In my case, since the sample size was relatively smaller (I had around 10 people to interview), this process, although difficult, was still doable.
Now imagine if there is an organization that has a huge customer base of say around 1000 people, and they wish to understand the pain points of all (or most) of these customers with their products or service offerings and represent these in the form of personas. Doing so manually would be a herculean task – will be expensive and extremely time-consuming.
This is where the Automatic Persona Generation (APG) system can help.
Automatic Persona Generation System (APG)
The APG team at the Qatar Computing Research Institute has developed an Automatic Persona Generation system, which is both a methodology and a system for automatically creating data-driven personas from online analytics data! Say, for instance, your organization has a large and diverse customer base and collects digital information on them. Using APG would enable you to better understand all of them!
APG uses the online analytics data which your organization has to identify customer behaviors, generate customer segments, and then enriches these customer segments with gender, age, and nationality-appropriate names and pictures. Customer loyalty rating, customer interests, product interactions, brand sentiment, and segment sizes are represented by the personas, and all of this is done in a privacy-preserving process using only aggregated data. Not only are the data-driven personas generated using APG more detailed than a traditional persona presented above, but the entire process is automated!
Let’s take a look at a sample persona generated using APG to understand this further:
In the above example, APG was able to generate a customer persona for a fictitious person called Mamdouh from the online analytics data for a fictitious organization “X”. Notice the label “sentiment for content” on the persona. Depending on whether the sentiment of a customer towards an organization (in this case X) is positive or negative, this feature provides insights into possible pain points, as even loyal customers (like those represented by this persona) can have frustrations with a system, service, or product. Once these pain points are identified, actionable decisions can then be undertaken.
If one were to filter (and feel free to contact the APG team for an actual demonstration of APG on how to do this) the viewed conversations of this individual on social media, we can further nail down why this particular persona is happy/unhappy with the organization and its product/service offerings.
Let me explain another example of a persona generated using APG that is a bit more concrete and actionably tells us the pain points of the persona, Chris. Now, in this example, there is an organization that wants to understand how people perceive their travel content on their social media pages! The persona generated is as follows:
In the above example, social media comments from the persona’s most-viewed content are filtered using a specific topic (“Aviation & Travel”) and sentiment (negative). Using different combinations of these sentiment and topic filters, people employing the use of APG can investigate the personas’ attitudes and opinions about specific issues. In the background, APG has collected a large number of social media comments, associated them with a given persona, and classified each comment for both topic and sentiment.
We can then explore the pain points of Chris by filtering his most-viewed content’s comments using the topic and sentiment filters. For example, doing so shows that some customers have issues with video translations (“Always amazes me how inaccurate the translations are on these videos”). Once these pain points have been identified, in this case, they can be fed back to the team developing the aviation and travel-related content so that these can be further looked at, perhaps rewritten and translated properly so that the customers have an overall good customer experience.
So, in the above example, APG was:
- Able to automatically generate a customer persona.
- Able to assign a sentiment score for content for the identified persona.
- Able to identify concrete and actionable pain points which can then be used to improve product/service offerings.
The bottom line is “Pain points are essential in understanding issues which customers are facing with your product or service”. Identifying pain points early on in the design process is crucial in developing customer-centric products and services which have both good UX and acceptability.
One of the main tools at your disposal to understand customer pain points is to use personas! Thankfully, due to technological developments in the field of machine learning, artificial intelligence, and online analytics, you no longer need to invest time in making these personas manually, a process that is both expensive and time-consuming.
As I have shown in this article, you or your organization can make use of APG to automatically generate data-driven persons which are both feature and content-rich and gives you lots of information about your customers and their pain points. Get cracking!
Would you like to learn more?
If this article got you intrigued, read our persona analytics research for more in-depth knowledge on persona development.