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How Non-Analysts Can Take Advantage of Mode

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Chioma Dunkley, Technical Content Writer

May 27, 2022 NaN minute read

Data analysis is never a one-person job. In reality, great analysis is a conversation between stakeholders and analysts. And often it’s an ongoing conversation; the stakeholder has a request from an analyst, the analyst provides a first round of analysis, the stakeholder has more questions, and so on. It’s an interactive process that requires active collaboration.

Here at Mode, we believe stakeholders are just as important and needed in the data process as the analyst. They have the domain expertise—the context—that analysts need to investigate a question. Most importantly, they are the ones making the decisions based on that data.  Our goal is that those who are not on the data team can use Mode comfortably and get the answers they need daily without waiting for the data team. With the right context and ability to explore vetted data, stakeholders can leverage Mode just as much as the data team.

How non-analysts and stakeholders can get more out of Mode

Beginning with Visual Explorer, we’re going to walk you through ways everyone else outside of the data team can use Mode to explore reports that data analysts have created. Consider this a "mini" guide that’s stacked with links to useful resources for you to continue your education on how to get the most out of Mode.  

Use Visual Explorer to see richer context with an array of visualizations

Before we talk about specific features, we want to talk about Visual Explorer which is not a feature but an entire visualization system that opens up endless possibilities for flexibly exploring and visualizing data. It breaks the mold of traditional data reporting and visualizations which are often static and limited, and provides unlimited visualizations and unlimited opportunities for you to drill down on your data.  

For stakeholders, Visual Explorer offers the opportunity to receive customized visualizations that emphasize essential data points for easy reference, consumption, and sharing.

Visual Explorer makes it easy to create both familiar and much more complex visualizations, including pivot tables, faceting/small multiples, combo charts, grouped bars, dual axes with multiple measures, funnel charts, and more. 

“This past quarter, I found Visual Explorer very helpful. It helped me really discover some insights into different customer segmentations and come up with an ideal customer profile (ICP) personas for our SMB segment.”
PMM at Mode

Use Explorations to save data views that are important to you

In most software products you may see a dashboard of static health metrics, but have no way of adjusting those charts further. For example, you may be able to count monthly active users but cannot cohort these active users by the number of times they visited your help site. The data views in most software tools for go-to-market teams are limited by pre-set parameters.  In Mode, you can explore data without being limited by parameters (or being limited by them by choice) and dig deeper in a chart to get more answers about what’s going on.

Explorations are a code-free path for stakeholders to adjust visualizations on existing reports made by their data analyst. We built this so that you can personalize and save slices of data that are most important or relevant to you. 

Use case: Let’s say a data analyst just sent you a report showing your company’s quarterly sales by region (Midwest, Northeast, Southeast, and West), and you want to see how many sales your lowest performing region is doing. To investigate this, you can simply navigate to the “Explore” button on the top right corner of a chart, hover your pointer over the midwest region line right above the latest quarter (in this example Q1 2022), and click “Drill down by month.”

You will get a new visualization, which doesn’t affect the original visualization, and from there can “Save Exploration” which you can later go back to or share with your other team members. Explorations built off of reports will automatically update whenever the original report is updated or changed by the data analyst. This means you will continue to see the most up-to-date data. 

“I use Explorations to understand which channels (organic, paid social, paid display, paid search, direct) are funneling MQLs to our whitepapers.”
Sr. Content Marketing Manager at Mode

Use Filters to quickly see the values you care about in a Report

Filters are a great way to do just that—filter a report to see a specific segment of your data without the hassle of rerunning a query or contacting a data analyst. They are a powerful and easy way to see different cuts of your data.

There are two types of filters in Mode: report-level and visualization-level filters. We’ll focus on the first and most common one you’ll see and want to interact with as a stakeholder – report-level filters.

Use case: Let’s say you’re looking at that same sales dashboard from earlier and see the chart: New Subscribers by Region and Channel. Let’s say you really only want to know how organic is affecting new Subscription rate. You can simply uncheck the “(Select All)” under “Channel,” then check “Organic” and hit the “Apply” button. Once applied, you will see relevant data showing only the impact of “Organic.”

“I use filters when I’m investigating sales opportunities.”
BDR Team Lead at Mode

Use Calculated Fields to zoom in

Calculated Fields are fields that allow you to create new data from an existing SQL query by applying additional logic. With Calculated Fields, you can easily generate new fields and drag and drop them right into your charts and reports. Simply put, Calculated Fields are basic equations you can do on your own that allow you to create a new metric for an existing report, similar to something you might do or see in Excel. 

Use case: Let’s say you have a chart that shows customers and their NPS scores, (Net Promoter Scores). For those of you who may be going “What’s an NPS score?” It’s simply a score commonly used to gauge a customer's satisfaction by asking them how likely they would be to recommend your product to a friend. 

In the column of NPS scores are values ranging from 0 -10, 0=being not at all likely, and 10= being extremely likely. Let’s say your team already has a way to bucket users: those with a score of 0-6 being “detractors,” those with a score of 7-8 being “passive” users, those with a score of 9-10 being “promoters.” Looking at the column may be useful but you’d have to remember the metric to determine what score type each user falls into. What would make this simpler is having a column that identifies their “score type.” Something like this could be easily created using Calculated Fields. 

Watch the video below to see Calculated Fields being used in this use case.

“As a product manager, I found calculated fields helpful in creating calculations to help me understand user behavior. I was able to take two columns of data to create a ratio to understand the percentage of users who shared reports.”
Product Manager at Mode

We're here to help!

Explorations, Calculated Fields, Filters, and Visual Explorer are just some of the features that can help non-analysts explore data in Mode without the help of the data team. If you have more specific questions, contact us live!

For async resources, see our Help page, or use Mode University if you’re already a Mode customer.

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