It’s not hard to have spreadsheets and data tables to see rows and columns full of data. Throughout all of the sorting and filtering, it can be a tedious task for the end-user. In an endless matrix of data, insights and recommendations can help zone in on the most useful information.
If they are actionable, they can bring value to personal and professional goals, and over time they can drive the adoption and retention of your product or service.
This is a progressive and straightforward framework to enhance your users’ experiences with data.
Let’s get started.
It’s essential to gain trust and build credibility before reaching the end goal of building a recommendation. Data is at the core of this framework and the perfect place to start. It’s a record of information on an action that you or your users take.
Here are a few examples that I will discuss in this article:
If these were unreliable or inaccurate, you’d take your business elsewhere.
Insights can influence the next action a user will take based on information in the past. You can start to correlate trends based on larger data sets. This is a great opportunity to work with your engineering or data science teams to highlight what trends can be correlated.
This may look like an average of video views over a date range, the average days it takes to close a deal, or the average opens per email campaign.
One thing you’ll notice here is that we start to think about what two or more data points look like over a snapshot of time. This will allow you to compare and contrast, and identify trends in data based on what you can determine has a positive or negative impact — what’s deemed positive or negative is different per industry and problem space, so you’ll want to make sure that you have a solid understanding of this.
To elaborate on the examples in the data section, it could look like the following:
Notice that there isn’t a next action we are recommending to the user, or highlighting particular causation.
As with anything, you’ll want to prioritize what types of insights and trends are the most compelling because they can be overwhelming or create blindness if you expose too many insights to your users.
This is where it all comes together. Recommendations are where we make things actionable — we have a strong hypothesis of what leads to positive outcomes we’ve learned, and users have solid confidence in our predictions. If a user wants to learn more, we have breadcrumbs to show how we’ve determined the recommendation based on data and insights/trends.
An important thing to note is that this isn’t something that UX can solve alone. You’ll want to make sure that your data science/engineering team has an established analytics/modeling platform and that there are large enough sets of data that are accurate — this doesn’t work without it, and if inaccurate, it can be costly as far as trust and adoption goes.
Now we can use our data as a foundation, supported by the trends to create that story.
Example 1
Example 2
Example 3
These are basic examples, but you can see how we start to build a story based on past performance.
Making recommendations based on concrete data and evidence for your users lets them have something tangible, and they will be confident with it as you have based it off of actual data.
This can be a lot of information, so it’s up to you to figure out what kind of progressive disclosure model you’ll want to use.
Let’s recap on what we covered today:
Here are some additional resources to that provide more insight into this topic: