Data Analytics and Product Design: A match you must make to succeed.

Data Analytics and Product Design: A match you must make to succeed.

Trusting only your gut and not tapping into data analytics for real-life feedback can be a dangerous approach. 

Effective use of data gives a nuance of how real people use various products and drives your business towards a product design that resonates with target users all around.

Data is good, but can yet be a calamity when you get your priorities wrong.

Mark Twain said:

“Data is like garbage. You’d better know what you are going to do with it before you collect it.”

It’s one thing to collect data in mass, It’s another to use it effectively towards a clear purpose or a business goal. 

In this blog, we’ll walk you through the best approach to data analytics, how to leverage it to be on-point with user needs, and ultimately design products that are relevant and timely.

Starting with, 

1. How is data used to enhance user experience today?

2. The effective Process of data analytic usage.

Let’s begin with,

  1. How is data used to enhance user experience today?
  • Inform requirements and uncover user pain points

Data can be a great way to find out what’s happening with your experience and help guide a design direction that aligns with users’ mental models. 

  • Gauge user interest

Prioritizing and being specific is one way to hit the nail on the head when addressing users' pain points with your product features.

Naturally, there are going to be times when you’re not certain whether a particular initiative is worth the time and/or resource investment. Data is one of the most efficient ways to take bias out of the process and let the features speak for themselves. 

  • Iterate and improve the design with confidence

In addition to helping inform what to build, data can also help guide how to build something. Before starting design explorations, we always make sure the team is collecting the appropriate metrics to help us design confidently.

This was particularly relevant during a recent project that involved iterating on our in-app marketplace where users shop for financial products (credit cards, bank accounts, loans, etc.).

Our KPI was to increase revenue — but if we focused on that metric alone, we might have ended up with bright, oversized CTAs everywhere.

 We examine clicks by page. This exercise revealed that users who drill down into specific categories (for example, travel rewards credit cards) are more likely to apply for products — and most users prefer to read our unbiased reviews before making a decision.

This data-informed approach helped guide subsequent iterations of our product cards in the app — ensuring we align the business goal (revenue) with users’ intent. 

  1. The effective Process of data analytic usage:

We’ll be looking at three things specifically here,

  • Data Collection
  • Insights and Patterns
  • Decision Making

First things first,

i) Data Collection

Data about your company and how it relates to the world outside is important when it comes to getting a grasp of what really influences the ecosystem. For every product, there are three categories of data one should source (your company, the users’ and your industry’). After sourcing, you can identify intersections between these three sets and then arrive at opportunity areas for your product.

  • Collecting the user’s data

The first step towards designing a product that is both functional and delightful is understanding your users; their needs, pain points, and unique use cases. There are various ways to collect user data. We have the use of surveys, user interviews, contextual inquiry, etc. Analyze your product goal and choose which collection method would work best for you.

  • Collecting your company’s data

Getting a hold of your company’s core principles is crucial because your solution needs to align with the business goals. In understanding the company, here are a few good questions to ask:

- What are the company’s long-term goals? 

- What are the priorities and expectations? 

- What direction does your company want to take?

- What metrics does your organization use to define success?

- What should we not do?

  • Collecting your industry’s data

You should understand the ecosystem you and your competitors operate in. 

Recognizing the competitor’s target users, differentiators, how they pitch and talk about their service, and what’s not working for them through customer feedback is essential in effective product design. These will help you benchmark your product and company, and help create a unique value proposition.

ii) Insights and Patterns

Once you have all your raw data, it’s time to make sense of it— that is, find patterns, intersections, and common themes. Get your teams together along with whiteboards and post-it notes and start brainstorming.

Steps to organizing the data:

  • Identify user attributes & personas: What are the different kinds of users? What are some prominent behaviors across different users? Simply Map all the deviations and exceptions — it all adds up.
  • Identify the user journey: Get a sense of where the user is, where they want to go, what their needs are, and the barriers and pain points in this journey.
  • Document your journey: Write down all your findings and make them accessible to your stakeholders since you might need to reference the information later.
  • Start with How Might We's: Look at your user research and the pain points of the user. Try to paraphrase them into a question, a challenge to solve.
  • Cluster/affinity mapping: Find the common thread between the “How Might We’s” and cluster them according to their similarities.

After you have our affinity mapping done and the user journey exclusively mapped out, you can then move on to the phase where we decide what to design.

iii) Decision Making

Once you realize all the opportunities and have a clear idea of the possible areas that are achievable, get the decision-makers on board. Align with business goals and narrow down the most important opportunities that will drive the needed outcomes. Here's how:

  • Put the user at the center: Map out where these opportunities fit in for your user. If the business goal is to increase retention, prioritize opportunities that enhance the existing user experience. If you want to increase your market share, ensure options that increase awareness and adoption for new customers.
  • Unpack your competitor(s): Communicate what they have explored and succeeded or failed at, and what are the unchartered waters. Now you know what is a safe bet, what might not work unless done very differently and where it might be high risk but high reward.
  • Align with goals: Show stakeholders how the opportunities will add value to the business.
  • Think frameworks: Mapping the themes to a framework is useful to use as a starting point for the facilitation of decision-making. (AARRR is a great example!)
  • Present: Use storytelling skills to present a complete picture and close the deal! (Storytelling for designers is a good reference)

All in all, 

Data analysis may not provide all the answers, but it’s a critical piece of the puzzle. It’ll help you and your team take the data-inclined approach which will in the long run shed more light on what’s happening with your product and give you the confidence to iterate intentionally, conduct user research strategically, speak with stakeholders, track your progress, and create better designs that align with your user's mental models. 

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