Data Design Thinking and Analytics - The Perfect Pairing

Thanks to the growing interconnectivity between brands and their customers through tools like social media, companies have become increasingly keen on showcasing how human centered their business practices are. With direct channels of communication, consumers can now offer companies feedback on a product or service on scale not seen before.

Any and all data collected from consumer feedback isn't to be taken lightly, with companies aiming to make changes as fast as people are demanding them, particularly around customer experience and user experience to enhance customer satisfaction. Analysis of how users interact with products and services is key to understanding their needs and preferences.

However, these changes can be costly, especially if the company in question doesn't collect data and employ an iterative data driven design process.

What is design thinking?

Simply put, it is a term that describes the iterative data driven approach to creative problem solving that puts users' needs at the center of everything.

A human centric system, it can be deployed in any industry, from tech to architecture to data driven design to communications, and it allows companies and brands to discover alternative and creative solutions to apparent or more ambiguous problems through data driven decision making.

Approaching design decisions this way is a non-linear problem solving process, with users doing plenty of back and forth, rather than sticking to a single, straightforward design path. In design thinking, baby steps are the way forward. Design thinking can also be adapted specifically for data products in the development of data applications for business, bridging the gap between design thinking and traditional data science.

How does design thinking work?

Design thinking requires you to step into the shoes of your customers and get a clear understanding of your target users in order to gain insight on the problem you need to solve.

This can be done through a host of data collection methods, the analytics data can be qualitative or quantitative, depending on what steps you take. Data analysts play a crucial role in evaluating new data sets and improving data quality. Through this data collection, you can clearly define the problems at hand, what your consumers' needs are, and then begin to ideate possible solutions for them. (See our quantitative and qualitative dataarticlefor more insight into this kind of research!)

As it is an iterative problem solving approach, there is no straight path to the finish line when it comes to design thinking. Any ideas you come up with will require multiple bouts of optimization and usability testing to see whether they work or not.

It simply offers new insights on alternative solutions and a new way of problem solving.

The design thinking process

The design thinking process can be broken down into five steps:

1. Empathise

2. Define

3. Ideate

4. Prototype

5. Test

Integrating data science techniques into these steps can enhance the understanding of challenges, develop robust solutions, and interact with user data in new ways.

Step 1 - Empathise

The first step is gaining a deep understanding of your users and their interactions directly. Getting different perspectives on user behavior is always a valuable insight. Who are they? What do they want from you? What do they want to accomplish with your products and services? What are the main user pain points?

This step is where you do your first lot of qualitative data collection, and is also known as an "empathy study".

Step 2 - Define

From what you've found in user interviews and your empathy studies, define and understand user problems and challenges your customers are facing by identifying patterns in the data structure.

Step 3 - Ideate

This is where innovative problem solving begins and you can start generating potential innovative solutions.

Think big, think small, and come up with as many ideas as you can. Don't worry if you think you have too many, the more ideas the better - they'll be whittled down later.

Step 4 - Prototype

The prototype stage of the design thinking process enables designers to create low fidelity tests for your ideas that can still provide you with solid, accurate new data, ultimately aiding in the development of a data product.

Remember what we said about not worrying if you have too many new ideas? During the design process, design teams may come across problems with some of the test designs, so they can cut those out to save time and energy.

Step 5 - Test and gather user feedback

This is the final stage in the design thinking process.

Once your prototypes are ready, put them in front of your customers for user feedback and record their responses. Incorporate data visualizations to create meaningful and actionable visual summaries of the feedback, improving user experiences and making data easily accessible for analysis and decision-making.

It's important to note, depending on the collective output of the last three stages, you might find yourself repeating the latter half of the design thinking process.

What does analyzing data have to do with design thinking?

As great as the design thinking approach is, it can't deliver quality all on its own.

Data collection, storage, and access is perhaps the easiest it has ever been, thanks to the Internet, as is being able to understand your customers. The thing is, it's not just about seeing the numbers go up and getting great reviews anymore - businesses are now expected to anticipate customer needs and attitudes.

Data won't lead your design thinking approach, but it is data that will fuel it. It feeds into every stage of your design thinking process, shedding light on a new solution or areas that may require more attention than others, and directing your focus.

How can Loops help with design thinking?

By not employing data driven design decisions and failing to integrate data from diverse data sources, companies run the risk of alienating their customers by not having a more complete understanding of user needs, seemingly ignoring them. This then risks a domino effect, where consumer dissatisfaction can then negatively impact sales and brand reception.

Committing to an iterative process doesn't need to be difficult, nor does collecting and analyzing data and insights.

Loopsis a powerful tool that has been designed to optimize the iterative design process, offering the most detailed insights from our 110M person-strong research base and doing all the heavy research lifting for you. We take all the hard-to-process qualitative data, and turn it into a concrete foundation to validate your design projects.

What can Loops do?

Loops allows strategists, creatives, designers and data scientists to test their creative materials and subsequently generate ideas via a global consumer audience by asking precision questions to specific groups of people in minutes.

With access to over 40 markets and 20+ demographic filters, the hassle of recruitment is taken care of. Thousands of verbatim comments can be gathered and analyzed in minutes, allowing for fast optimization and retesting - what normally take weeks to complete via traditional approaches like workshops and focus groups, Loops can do in 24 hours.

Because Loops is built for iteration, each step of your process is preserved, offering team members and stakeholders a step-by-step view of the progress that has been made alongside the data points and user research that has informed your design changes so far. This system of record for your design process is a great insurance policy against subjective curveballs from stakeholders who've only just started paying attention to the project. You can show the thinking is robust, proven by what user experience has yielded.

In the design thinking process, a tool like Loops is a powerful and valuable asset. By actively giving consumers a voice in your user testing and collecting of qualitative data, you will:

• Create output that is proven to resonate with your audience

• Create a system of record of insights coupled to iterations

• Align stakeholders throughout the creative journey and ensure their buy-in