What are the best ways to create a user behavior model or framework?

What do you want to learn about user behavior? Who are your target users, and what context do they operate in? What outcomes and benefits are you aiming for? What are the limitations or boundaries of your research? How does this align with your organization's goals? What resources do you have, and what constraints do you face? Have you involved relevant stakeholders in this process? Clear answers to these questions help focus your research efforts and ensure practical outcomes.

Based on my recent experience working with users worldwide, the key points I consider important for building a behavior framework are: Goal definition. User identification/segmentation. Journey mapping. Utilization of a data collection model. I suggest (ToFu, MoFu, BoFu). Feedback collection. Analytical data integration. Testing and experiments. Continuous adaptation and discovery.

Clearly define the goals and objectives of the user behavior model. What specific behaviors or actions do you want to understand? What are the key outcomes you hope to achieve by analyzing user behavior?

This step involves figuring out what you want to achieve with your user behavior model or framework. What are your research questions? Who are your target users? What are the key outcomes you're hoping for? Having a clear and specific scope and purpose will help you focus your research and avoid unnecessary complexity.

It involves defining clear objectives, identifying key user personas, and conducting thorough research. Mapping user journeys, defining specific interactions, and establishing metrics provide a comprehensive view of user behavior. Behavioral segmentation and user feedback add depth to the model, ensuring a nuanced understanding of diverse user behaviors. Collaborating with stakeholders and creating visual representations enhance communication and alignment. An iterative approach, continuous monitoring, and integration of insights into the development process ensure the model remains dynamic and actionable.

There are some predefined user models that you can look into and they help them in streamlining your research. Some of them are: 1. Fogg Behavior Model by psychologist B.J. Fogg 2. CREATE (Cue, Reaction, Evaluation, Ability, Time, and Experience) Action Funnel by Stephen Wendell 3. Spectrum of Thinking Interventions 4. Dual Process Theory

From my perspective, integrating multiple models or frameworks can significantly enhance the depth and applicability of user behavior analysis. For instance, combining user journeys with personas can offer a more holistic view, not just who the users are but how they interact with your product across different stages. Moreover, incorporating user mental models into this mix can reveal discrepancies between how users think a system works versus its actual design, guiding more intuitive interface adjustments. In practice, I've found that this layered approach to modeling user behavior fosters more targeted, effective design interventions that resonate well with the end users, leading to higher satisfaction and engagement.

Choosing the right model is very important. The characteristics of the user base and context of the digital solution must be considered. Are you dealing with a B2B or B2C scenario? Is your digital solution a mobile app, a web platform or an integrated system? For example, the hook model is suitable for habit-forming products, but future work is done by frameworks that are great for understanding user motivations. The choices you make here determine the validity of your results.

Currently, the predominant global approach is design thinking. It proves to be highly effective in problem-solving and solution-finding processes. This methodology can be applied in various ways, depending on the specific requirements. In some cases, additional research may be necessary before product launch. In such instances, conducting further research can lead to the discovery of new solutions.

To understand how people behave online, it's important to gather info about them in a smart way. You can chat with them, ask them questions in surveys, watch what they do, or even let them try your product and see how they react. Once you have this info, you can organize it into groups, draw maps to understand their feelings, look for patterns in what they say, or use numbers to compare different things. For example, if you're making a new app, you might talk to people who use similar apps to find out what they like and don't like. Then, you can use all this info to make your app better and more appealing to users.

Collect relevant data on user behavior through various methods such as user interviews, surveys, usability testing, analytics, and ethnographic research. Gather both quantitative and qualitative data so as to gain a comprehensive understanding of user behaviors.

One thing I found helpful is to collect both qualitative and quantitative data and then cross analyze the results. Meaning getting a better understanding of the users attitude, and motivation through interviews/focus groups, and see how they actually interact with the artifact through user tests. This will bridge the gap between (what the user do) and (why they do it), and you'll be able to see the user in a more wholesome way. Finally, which specific method you pick for data collection is dependent upon the problem statement andor purpose of the overall project.

Gather data on user interactions using analytics tools, user surveys, interviews, and user testing. Quantitative data (e.g., page views, clicks, time spent) and qualitative data (e.g., user feedback, pain points, motivations) are both valuable

Collect relevant data through various research methods such as surveys, interviews, usability testing, analytics, and observational studies. Quantitative data (e.g., metrics, user interactions) and qualitative data (e.g., user feedback, observations) can provide valuable insights into user behavior.

I’ve found that Empathy Building Exercises offer great insights too, engage the team in exercises that build empathy, such as role-playing or ‘a day in the life’ simulations, to gain deeper insights into user experiences. It’s a fun bonding exercise too for the team.

Analyze the collected data to identify patterns, trends, and common behaviors among different user segments. Look for recurring themes, pain points, motivations, and barriers that influence user behavior.

After gathering and studying user info, it's time to put it together and check if it makes sense. Imagine detailed profiles of users, like their names, backgrounds, and what they want. Draw out their journeys or sketch out how they might interact. Show these to others for feedback. Maybe even try them out with real users to see if they work. Keep improving based on what you learn from them.

Validate the user behavior model through testing and feedback from users. Then iterate on the model based on new insights and data to ensure its accuracy and relevance over time.

Once you have developed your model, integrate your results into a coherent framework. This model should be clear, actionable, and directly related to the user behaviors you've identified as important. Next, we validate this model through experiments. This could be A/B testing, user feedback sessions, trial runs, etc. Validation is not a one-time event, but an ongoing process of model improvement.

Once you've built your user behavior model, it's crucial to share and use it effectively. You can do this through presentations, reports, prototypes, or guidelines. Presentations help you explain the model using slides or stories. Reports document your research process and findings. Prototypes let you test design ideas based on the model. Guidelines ensure consistency in design and development. Sharing your model this way ensures everyone understands it and can use it to make informed decisions. For instance, if you've created a model to understand why users leave a website, you might use a presentation to show key findings and propose design changes, a report to detail your research steps and outcomes

Integrate the user behavior model into the design process to inform decision-making and guide the development of user-centered solutions. Use the model to identify opportunities for optimization and innovation in the product design and user experience.

The final stage is effective communication and application of the model. This includes not only disseminating the model to the team, but also ensuring a deep understanding and proper application of it. This can be facilitated through training, workshops and regular discussions. Note that a model is only as good as its implementation. It should be deeply embedded in the product development cycle and influence decision-making at every stage.

Let's talk about how we can change the user default behavior for our benefit: Nudging - It influences user behavior without coercion like changing default options or simplifying decision-making. This guides users toward specific decisions while preserving their freedom of choice. Gamification applies game dynamics to non-game activities, offering rewards, setting goals, and providing encouraging feedback. This strategy, prevalent for the past decade, enhances engagement, motivation, learning, and user retention.

There are different frameworks to deploy, but one guiding principle needs to dominate at the current moment of practice in enterprise. Avoid overly complex diagrams and presentations on user behavior models that obfuscate instead of enlighten. They are modeled on outside consultancies that try to justify their value by "showing their work." As an enterprise designer or researcher, build trust and use appendices and documentation, keeping the insights the model produces usable and applicable. Work backward from the actionable insights to the more complex work in presentation and pre-structure your investigation so you don't collect wide-sweeping data and then struggle to structure it in the end.

Never use biases to your advantage if it is detrimental to the user. On the contrary, we promote ethics, eco-conception, and honest discussions with empathy in mind. Find ways to avoid friction and learn from your mistakes.

What about Stickiness? It measures how often users are returning to an app. It’s an important metric for providing increased context for user engagement and app retention. How to measure stickiness: Product stickiness is measured by dividing the number of daily active users by the number of monthly active users. app stickiness tells an app developer the ratio of daily active users that are becoming monthly active users.

Why the user behaviour analytics while we have the traditional analytics? The model or framework will be dynamic if we study the proper metrics from the funnel. Signup or activation, drop-off for in-app purchase are important minimal examples. Trying to get the answers of friction or conversion, or diversion using the perfect tools is a better way of modelling the user behaviour. Heat maps and on-site surveys are some of the best tools to create behaviour model.