Data Analysis in Improved Product Development

Data analysis is one of the least sexy terms in tech. But we believe it is the secret ingredient to smart product development processes.

Say you have an idea for a product. You might want to rush to designing the UX and getting it into people’s hands. We have found that that is a good way to waste a lot of time and money.

It is far more efficient to use data analysis at each stage of development to optimize for release as well as plan improvements for future demand.

Let’s take a look at how data analysis helps improve processes during each stage of product development using our 5D Methodology:

Data analysis allows you to sift through terabytes of information and drill down to the crucial variables so you can effectively outline and understand the problem. It allows you to ignore all the “noisy” data and focus on the most impactful business levers, which will in turn help drive KPIs.

In this stage, we take the idea out of the creator’s mind and ask how someone with no relation to the experience or industry can understand the product and why they need it. Discovery data allows a business to filter through demand data and focus on the key variables that will differentiate your product in the marketplace.

Now it’s time to think about appearance. While this stage focuses primarily on visual aesthetics, it also includes how the natural flow (or user experience) of a product encourages the user to want to use it.

Consider Netflix. In April 2017, after analysis of the vast data it had on subscribers, Netflix determined too few users used their star rating system, which meant they had less information on which to base recommendations.

By implementing a thumbs up or down rating system, they significantly improved subscriber engagement which in turn enabled more homepage customization to cater to each subscriber’s interests.

As an added benefit, user retention rose from 82% to 93%.

The development stage means it’s time for engineering to finalize compilation and stress test your product, using template checklists for each assessment category.

Users will inevitably not use your product the way you imagined; they weren’t in the room when you worked all that out. In any case, you don’t want them to need training to use your app, you want to know your app is user-friendly.

Take time during development to gather and analyze data on how your app performs according to your use cases. You can discover potential problems with the product and fix them before it goes to market.

This phase means the product is ready for market! Data analysis here means you gather feedback from initial users on how to improve the product and what changes to make. User data is the gold standard of data analysis, so this stage can be fun and exciting.

Before Amazon had an option to save items in a cart, they couldn’t act on user search information. So, they deployed various algorithms to allow the “Save to Cart” icon to gather more information about consumers.

It then tied that data into other products using its new “1-Click Checkout” option that streamlined product purchasing. Amazon uses the power of suggestion to encourage more purchases while lowering the barriers to purchase. After this implementation, it accounted for 35% of the company’s sales annually.

Data analysis during product development improves the product. In the right person’s hands, at least. And if you’re not improving, you’re falling behind.

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