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E health

Last updated on 16.05.2020

Why Identify end users?

End users Identification allow you to study the patterns that differentiate your end users. Here are some valuable things you can do after the analysis process is done:

• Identify the most and least profitable customers

• Create profiles

• Better focus their needs

• Improve customer service

• Build loyal relationships

• Price products differently

• Develop better features

Typically this is done using a combo of an existing customer database, meetings, surveys, 3rd party reports and services and sometimes Internet searching. Identifying customers can seem scary: where do you start and how do you Identify and classify these end users? While the answer will rely on your goals and products, the 5 W’s–and an H (who, what, where, when, why and how) can be helpful during the identification and segmenting process.


You should know the demographics of your end users at an early stage. Who can be the end user can be understood by focusing on the below-given points:

• Gender

• Age

• Income

• Education level

• Occupation

• Marital Status

• Number and age of children

Knowing these basic demographics is an essential part for the who part of the question. For example, if 80% of the end users are aged 45-65 then this helps with building effective features for an electronic-Health system according to the needs of this age group hence increasing the chances of practical usage of the platform from this segment.

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Knowing where end users live is not just to identify their geographical presence. Instead, it’s about learning the geographic diversity or its influence on the needs of your end users. Geographic attributes include:

• Rural vs. urban

• International vs. Domestic

• City names and market size

• States and Regions

• Zip codes


Study about the past experiences of the end users and what are the problems they are facing. Similarly, the present solutions to those problems must also be identified and cross-check how our solution can be better the current solution. Moreover, understand what future problems can also be solved from the electronic-Health system that we are providing. Has to be carefully studied and those patterns can be used as building blocks for electronic-Health Sytems.

What they’ve done

What have end users done that distinguishes them? The easiest way to start in the past. If helpful, think in terms of how the problem is being solved, frequency and monetary value for the end user.

• Product experience (new vs. repeat end users)

• Years of experience

• Total revenue

• Total profit

• Most recent purchase

• Total number of transactions

• Time spent with support

• Number of end users they’ve referred

What they do

Learning the basic goals and motivations of end users help identify gaps in product features and open up chances for improvement.

• Motivations

• Experience level

• Goals

• Tasks

What they think

Understanding the attitudes and psychographics of end users, that differentiates the usage of the platform.

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• Lifestyles: traveller vs. homebody

• Values: frugal vs. spendthrift

• Technology: early adopter vs. tech laggard

• Personalities: risk seeking vs. risk averse

• Overall product satisfaction: low vs. high

• Active vs. occasional investor

What they are likely to do

Understanding the terms like long-term relationships and the lifetime value of an end user through a number of surveys and analysis of their past behaviour you can estimate:

• Likelihood to use the platform regularly

• Likelihood to recommend the platform to others.


Identify differences in the type of end users you have, based on when you measure.

• Seasons

• Weekends vs. weekdays

• Life Events: after a baby, a certain age, or accident

• Daytime vs. evening

• Periodic activities: checkups or visits


How do end users make use of electronic-Health Systems?

• Online vs. in-clinics

• Phone vs. in-person

• Through a referral of expert vs. direct

Now What?

It’s not important to collect every one of these attributes for your end users, many won’t apply and some won’t make a great difference. After you’ve collected the data, our research says you should do these next things.

1. Estimate the size of each segment

2. Estimate the value of each segment

3. Find the patterns seen in the analysis

4. Build the system accordingly

Learning through smaller surveys with the open-ended type of questions and conducting interviews can also provide a lot of significant insights into what questions to ask. Finally, a deep focus is required on the patterns seen in the analysis. These patterns must be considered at the time of implementation.

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