Steve Jobs once famously said: “It’s really hard to design products by focus groups. A lot of times, people don’t know what they want until you show it to them.”
This quote applies just as strongly to web designers. Neither designers nor their customers can predict what people will respond. Your only option is to create something and test it out. This is the basis for data-based web design strategies.
Data driven web design is changing the web industry forever. The benefits of data-driven design are numerous. However, it has also created a host of new challenges that web developers must take into account.
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Why big data is revolutionizing the web development profession
There are plenty of reasons that web developers are turning to data-driven design strategies. Here are some of the key benefits:
- Estimating server capacity needs. Monitoring web traffic statistics is one of the biggest challenges companies in all industries face. One of the biggest problems is that traffic patterns vary by industry. Big data has made things much easier. Brands can focus on historical data to understand peak traffic patterns so they can invest in servers that have sufficient capacity.
- Conversion rate optimization. Design elements play a crucial role in online conversions. Some case studies have shown that changing the color of a call to action button can boost conversion rates by 80% or more. Brands that collect extensive data through split testing can significantly boost their conversion rates.
- Improving user engagement. User engagement ratios are very important elements of any digital marketing strategy. Improving the quality of your design can reduce your bounce rate and increased your returning visitor rate, which will improve the sustainability of your digital marketing campaigns.
According to Design Bundles, when used properly, data-based web design strategies can be incredibly valuable. However, there are some risks that developers must prepare for.
Consolidated Data can tell a misleading story
Most web data that you will use in your designs pertains to the actions of people visiting your website. In theory, evaluating large blocks of data can help you understand how they interact with your site.
The problem is that your user base is probably very heterogeneous. The following variables will affect their behavior:
- Age
- Gender
- Geography
- Traffic source
- Ad copy
Some users will respond better to one design, while others will prefer another. If you try to base your design decisions off all of your raw data, you may not significantly improve your marketing strategy.
Segregating your data is incredibly important. While you are split-testing, you need to break down your campaigns and factor for the variables listed above. If you run a Facebook campaign, you need to setup a different Campaign Goal in Google Analytics that tracks the demographics of the users in each campaign, as well as the ads that you used. If you are creating a campaign with AdWords, you need to track the keywords and any demographic targeting options you included in your campaign.
Data Isn’t an Objective Truth
Data can be very helpful for analyzing and optimizing campaigns, so it clearly can help you develop a more effective design. However, it isn’t infallible.
Data tells you how previous visitors interacted with your site. It can’t predict how future users will engage with it. The following factors can cause you to draw inaccurate conclusions:
- Trends can change over time. New events, fads or demographic changes can all impact the way customers interact with your site. If you are on the tail-end of a major change, then you may need to look at your data with a grain of salt.
- You may have a statistically significant amount of data to draw any conclusions.
- Your data may not always reflect the behavior of actual human users. Many of your visitors are likely bots, which may be difficult to filter from your testing experiments.
While data can be very valuable for web design, it is far from perfect. You should use it as a guide, rather than looking at it as an objective truth.