UX Study & Design for customer feedback platform

11 min readOct 12, 2021

In this case study, we’ll cover User experience study and designing for Customer Engagement and Experience Platforms.

Problem statement

Prashant recently joined as a product manager in Foodie. He wanted to get some user feedback on the food ordering experience inside the Foodie app. Foodie already has Netcore CEE integrated into its app. Prashant would log in for the first time in Netcore CEE to create a survey campaign for getting this feedback after a user places an order. So we have to design the user experience for Prashant to create a Survey campaign with Netcore CEE which would go live on the Foodie app.

Solution approach

We’ll be diving into a step-by-step solution approach to achieve a great UX for the product. Let’s start

Study & understand the problem statement. Applying the divide and conquer approach to solve the problem.
User Interview: We’ll conduct some assumed user interviews of some product managers and try to understand that their first experience over such platforms
Persona: We are already having a persona (Prashant) for this problem and it is mentioned in the problem statement. We’ll give a face and assumed background to him so that we can match our understandings with real scenarios.
Affinity Diagram: An affinity diagram will help us to ideate the behavior of the situation.
Empathy mapping: Empathy mapping will help us to understand the true user behavior regarding the problem situation.
Task flow: We’ll present our UX design through task flow.
BTS: Behind the study

1. Study & understand the problem statement

We’ll study problem statements and take out important data for User experience design and study

Prashant recently joined as a product manager in Foodie: This line is showing that Prashant is already a product manager or very new in this field but he has a piece of prior knowledge.
Prashant would log in for the first time in Netcore CEE to create a survey campaign for getting this feedback after a user places an order: By this statement, we get to know that we have to make our platform easy that users will not have to struggle in any part of it, either it’s on-boarding or in the task flow.

2. User Interviews

In this case, our users are the product managers. We’ll ask a common question to multiple product managers to get to know about the experience and struggle they face while creating a feedback survey for their product.

From the above interview, we have collected some struggling points that we’ll cover in our user experience design to try to provide the best solution for these.

3. Persona

Persona indicates our user group So keeping a focus on persona while working on user experience design will help us a lot to achieve our goal.

4. Affinity Diagram

After getting good information about User background, Now we’ll study user behavior and an affinity diagram will help us to conduct this. We’ll be exploring user behavior for the product.

5. Empathy mapping

Empathy mapping helps us to understand the true user behavior regarding the problem situation. As we are already familiar with the situation where users say something else and do something else while having something else in mind. We can take out a core solution with empathy mapping for the user if we’ll be able to catch the correct behavior.

6. Task flow

After all user study and behavior research, We’ll move towards the User experience design for our product. We have a quite amount of data and problematic points that we have to tackle while designing the task flow to achieve a smooth and hustle-free user experience.

We’ll explain the task flow journey by putting Prashant's character in the middle of the whole experience. Assuming Prashant is logging in for the first time and also interacting with the product the first time.

The following details are not showing the wireframe of any UI module, We are using some graphical representations only So that viewers can understand the flow better.

Let’s start

6.1 ) Easy Onboarding

Product is new for Prashant as well as Prashant is new for the product too, So at their first interaction they must know each other without any hustle, that’s why we’ll keep a quick and easy Login system which will be done via Gmail, Facebook, etc. As Prashant is an Employee of the foodie app and our CCE product is already integrated into the foodie app then He just needs to click on login and our product will verify Prashant from his company id and will let him enter in the application.

as an example

6.2 ) Tell us about you, So we can serve you better.

Our product will ask this from Prashant and the answer is NO. This is the first interaction but why is the product is asking this from Prashant. The reason for this is, Our product wants to know Prashant in terms of his position in the foodie app.

In the manner to provide a great user experience, the product should know who is the user is so that further processes in the product can be aligned respectively. In this case, Prashant needs to specify that he is the product manager.

6.3 ) Mention your purpose

As we mentioned in the above flow that product will behave according to the data fed in it, So in this case when Prashant marked himself as a product manager, Our product will show him tasks that a product manager performs.

Product will show all the possible tasks that a product manager can perform and this is our product inbuilt capability. As per our problem statement that Prashant needs to create a survey then Prashant will choose the survey option.

6.4 ) Specify the user set.

We have the task, we have a purpose now we need the people who will be involved in this survey. In this stage, the product knows that Prashant is a product manager and he wants to conduct a user survey for the overall experience, So now Product will show the user what the foodie app already has, and not only that, the User set is divided into some defined way.

It is very important to be part of the survey. The product is showing some divided user sets that can involve and participate to provide true data that can be used by the product team to analyze. Only the right user set can serve the purpose of true analytical data.

6.4A ) Specify is any special requirement in a user group.

After the selection of user set, we’ll ask for any special requirements from the selected user settings like only males in to be selected for the survey, only particular age group people can see this survey when it goes live, etc. and we’ll also show current overall analytical data with predefined filters to Prashant so he can take better decision concerning user settings. As an example

This step is optional so that if Prashant doesn’t want to apply any extra filter on the existing selection of the regular user set then he can skip this part and move forward. The good thing here is that Prashant will know this feature for the future that he has more capabilities and freedom to select particular people for a particular survey.

6.5 ) Mention the type of survey.

The survey can be of many types and for many reasons like

Survey for a new Feature that was recently introduced
Survey for the overall experience
Survey for a new feature that is in working etc.

So in this step of task flow, we’ll ask Prashant to mention the type of survey.

As per the problem statement, Prashant will choose Survey for the overall experience here.

6.6 ) Define a flow (Optional).

Define a flow is the task step where Prashant will define a proper flow of the task that he wants the user to give feedback on. As an example, if a product wants the user to get to know about a feature in-app and there are many ways to do that but users are not following the path that product expect to follow to reach that particular feature then there must be some sort of lack may misleading to all the users. So in this step, Prashant needs to mention the whole food ordering flow that the foodie app expects users to follow.

The advantage of this part is that Prashant will get the data of people who are not following the desired path to achieve goals.

6.7 ) Survey AI.

This is something great in terms of user experience. The idea of Survey AI is to save a lot of money, resources, and time for the companies. There is a lot of time we expect to get a lot of useful data from the survey but didn’t work because of many problems we never thought of before. Survey AI will indicate the chances of getting feedback over the survey with the help of AI. AI will analyze the similar types of surveys that have happened before and will give a result based on it, like 6 out 10 People didn’t respond to similar surveys or surveys with similar requirements. Product AI will also help you to make surveys better so that it can collect a lot of useful data.

6.8 ) TIPs for better results on surveys from AI.

Product AI will not only tell users that about the survey but will also guide them to get better results. The solutions would be well proven and had true results, as an example Prashant is conducting this survey at the end of food ordering So it’s very obvious that people will more likely to skip rather than to stay for survey because their purpose has been fulfilled. How can we make people stay even after they have completed their tasks?

The above image is an example of our product AI suggestion for a better amount of feedback results for the survey.

6.9 ) Heading & Description.

Now product will ask Prashant to give some inputs, We can make this automated too but organizations keep their data with their categorization methods like Foodie save its survey Name: 022021FS, which denotes this survey happens in the 2nd month of 202. That’s the reason Prashant have to put the heading of the survey and description along with it

6.10 ) Questions for the survey.

It’s time for questions, We have two options here, Either AI does all the work for questioning and put all the questions related to the similar surveys Or Prashant can customize the questions by himself. While generating questions by Prashant, Product AI will help him for better questions which will result in getting good user feedback.

At the end of this process, Prashant will have a no. of quality questions that will not misguide users and they can attempt the survey easily. Our product will also help Prashant to decide the number of questions and time that One question should consume etc.

6.11 ) Analyzing questions

After completing the questions, Our product AI will analyze all the questions for some quality check which includes

Question quality (Easy to understand)

Also in case of any important question, Prashant can ask for a bot reply to see how the response can be for that.

At the end of this process, questions are ready for the survey and now the survey is also getting complete.

6.12 ) Dummy run to understand the insights.

As we have completed the survey but it would not be a good idea to just public it even though Product AI has already checked everything.

In this step we’ll ask Prashant to conduct a dummy run on the survey, Dummy run here indicates that the survey will get public in a fake environment that is specially made for such scenarios, So it’ll get public and show results for the survey in just one minute. The advantage here for Prashant is that he’ll get to see the real insights of a fake environment which is totally matching with the real environment too in terms of user interface and other insights.

Our product also provides features that if Prashant wants to test it with real people in small amount then he can go for it too. As an example Prashant will get a User Interface like the above image after the survey then he can go through it and can read it with actual understandings. When Prashant is already been familiar with the upcoming results then he’ll be also confident when the real results get out.

Now everything is sorted in terms of product and Prashant successfully published his survey without any breakpoint in between. Our product took care of almost every things with our integrated AI system.

7. BTS (Behind the study)

I love to use pencil paper research where you put all your ideas on the paper and create a mental mapping, thinking about every possible outcome, putting every idea even those who you think are dumb ones.