Data Driven Design

Data is everywhere these days. From shopping preferences, product usage to weather information - data is just pervasive and plentiful. Businesses collect data about their users to study and understand user behavior. Knowledge about the users help them make strategic product decisions. For Designers like me, nuggets of user information help me make the right design decisions.

In product development, any piece of information about user's context, attitude and behavior is considered a good data.

Context - circumstances under which the users are using the product defines their context. This includes when and where the product is used, user's emotional state while using the product, is anyone helping them to use it, how is the product accessed i.e. what devices are used and do they have any accessibility concerns etc.

Contextual data help us build empathy and allows us to think beyond just addressing the basic needs. For example, a mobile app for a shop floor technician should consider context such as oily hands, big gloves, surrounding noise etc. before deciding what type of features to incorporate.

Attitude - a person's notion of value defines how they say they would use a product. User research methods such as user interviews, focus groups and diary study etc. give users an opportunity to share what they think works or does not work in a product.

Behavior - the actual way in which a person acts towards something defines their behavior. The key objective of user research is to identify patterns of behavior across multiple users as this guides what need should be prioritized for improvement and validates the teams assumptions. Understanding user behavior is critical to ideation.

How to collect data - different methods

User research constitutes more than half of product design lifecycle. Different user research methods can be employed at different stages of product development as each of them exposes different facets of user experience. Nielson Norman Group provides a 3-dimensional framework of when to use what type of user research https://www.nngroup.com/articles/which-ux-research-methods/. Though it is not possible to use all the methods listed in the framework in one project, it is valuable to combine more than one method to derive maximum insight.

At a high level, user research can be either generative or evaluative in nature.

Generative: During the early stages of a project, the team usually tries to generate information about the problem space. Also called ‘explorative’ research, Designers dive deeper to gain greater understanding of users. Open ended research studies such as user interviews, contextual inquiry (where user actions are observed while they execute a task in their own setting) etc. help uncover insights about user goals, tasks, reasons for executing a task, unmet needs, context, attitude and motivations. They help build empathy towards the users and highlight opportunities for innovation.

Designers take the insight from such studies and narrate user’s story using tools such as User Personas, User Journey Maps, Empathy Maps etc.

Evaluative: The focus of evaluative research is to validate if an existing product, a design concept or MVP meets the user’s needs. Formative evaluation methods help Designers understand whether or not a design is working as expected. For example, Designers often test wireframes, mockups or prototypes with users. Feedback collected from each iteration of testing helps them understand the user behavior and why certain aspect of design did not work. Designers are then able to steer the design to address user needs and expectations.

Summative evaluation methods help teams to evaluate the product as a whole to understand gaps existing in the system. For example, competitive analysis help understand how well is the product performing compared to its competitors. A go/no-go product decision help summarize if the product is ready and good to be released to the customers.

Summary

Product and User experience designers spend considerable time talking to the users, understanding their context, their wants and needs, their thoughts and feelings, attitude and behavior, and empathizing with their pain-points. The problem space starts to get much clearer when we develop deep understanding of the users and their experiences. Understanding the problem space clearly is still only one side of the coin. With validated assumptions, Designers are better able to design solutions addressing user’s needs and goals. These solutions are iteratively tested to generate more user data which is then used to refine the product. Data driven approach is thus a systematic collection, synthesize and interpretation of user data to formulate the right solution. It helps us gain insights about the users which may otherwise not be so obvious.