What questions should I ask about data analytics?

Data analytics involves analyzing data to make conclusions. But if you don’t ask the right questions, you’ll meander and waste time. 

The right questions are specific and focused. With anything different than that, you’ll be reporting without a target, and you’ll be more likely to miss your ROI targets.

Examples of the Right and Wrong Analytics Questions

To help you get value out of your analytics, consider this break-down of bad, better and best types of data questions.

Bad: How many people read my blog?

• This question has no objective, isn’t based on revenue, lacks timeframe
• Does have a metric

Better: What is the average number of people that read my blog? 

• The question has a solid metric
• Depth and definition are missing

Best: 

Asking bad data questions about your website or product leads to vague answers. By consequence, you might not know how to better cater to the user’s needs. This can lead to customer churn and missed growth opportunities. It’s notorious in product analytics, but also in classic analytics tools such as Google Analytics. There’s so much data that if you don’t enter the tool with a specific and actionable question in mind, you’ll either get lost or you’ll keep wandering.

Here are more examples of the best types of questions you can ask as you dive into your analytics:

• I want to find out what our users like and dislike about our product, so I market and communicate things that users don’t know and overcome objections.
  

• How can we optimize our product’s feature X for long-term retention?
  

• During the first 14 days of a new signup’s lifetime, what are the actions that they do in their first 24 hours that correlate to their purchasing?
  

• Do users who connect a website account in the first 30 days purchase more frequently?
  

4 Essential Principles for Asking Questions in Analytics

#1: Think about the Journey, Not about Isolated Touchpoints

In Effective Data Storytelling, the author Brent Dayeks introduces a framework for how to derive audience-driven questions. Using a GPS analogy, he states:

You start by understanding the audience’s starting point (their problem or current state), and then you seek to learn what their intended destination is (their desired outcome or future state). You examine their route and mode of transportation (actions or activities) and then evaluate the progress toward their goal (measures or key metrics). This simple formula ensures you don’t get lost in the data labyrinth and positions you to ask the right questions of the data.

• Brent Dayeks
• Autor
• Effective Data Storytelling

#2: Incorporate Business Context

We respect the words of Avinash Kaushik, the analytics evangelist for Google. He gives his take on business goals and context to cater to, and says:

Before you provide the data, ask the requestor what is the business question they are trying to answer. Then fulfill that need.

• Avinash Kaushik
• Analytics Evangelist
• Google

He gives a point of view on data enablement, and says that “you need business questions because rather than being told what metrics or dimensions to deliver, you want business context: What’s driving the request for that data? What is the answer the requestor is looking for?”

#3: Avoid Vague and Open-ended Questions

Vague questions get you vague answers. Open-ended questions only lead to more questions. Avoid the following: 

Q: “How many visitors did I get?” 

A:  Probably a lot, maybe.

#4: Ask Specific Questions that Include Revenue-based Objectives, Timeframes, Segments, Metrics, and Dimensions

Bad: “How many sales did we make?”

• No objective, not based on revenue, no timeframe, but has a metric.

Better: “What is the average revenue per unit?”

• Has a solid metric but depth and definition are missing

Best:

Here is another example:

Bad: “How many visitors did I get?”

• No objective, not based on revenue, no timeframe
• At least it has a metric

Better: “What is my conversion rate on my paid media channels?”

• Depth and definition are missing
• Has a solid metric and a semi-defined target group

Best:

Real-Life Questions from Two Product Companies  that Grew Thanks to Asking the Right Questions

Exploratory or open-ended questions have a purpose if you ask them when you’re setting your strategy, before you dive into data in the analytics tool. We invite you to start strategizing with questions such as:

• How can we optimize our product for long term retention?

• How can we improve our reporting and analytics strategies by asking the right questions?

• How can we evaluate if our marketing budget is yielding the right return on Investment from the amount of users that sign up and use our product in each quarter?

• How can we improve our product to adhere with the world’s best practices and become the number 1 product in the market by Q3?

• How can we track the number of users that visited our website and purchased our product at least twice within the first quarter of this year?

• How can we improve the user interface of our product to retain more users and boost sales by 70% in Q2?

• How can we optimize our product for long-term retention?

But once you do dive into the data, distill the exploration into specific questions. That’s what our CEO did when looking to grow our sister company UTM.io, an analytics tool for building and managing UTM tags consistently across the whole team. Dan asked:

• During the first 14 days of a new signup’s lifetime, what are the actions that they take in those first 24 hours that correlate to their purchasing?

The question helped him find out that if users signed up and copied 3 links with UTM tags in their first 15 days, they were likely to stick around and renew the subscription. He found this out by getting the frequency impact report in Amplitude, and tying the product analytics data to marketing analytics.

Next, Dan analyzed what was keeping people from copying the 3 links. This enabled his team to run a sprint targeted at focusing the most impactful hurdles.

In another example, we’ve consulted the marketing team of a company with a product for digital signage. They got a good grasp of what to ask when going into their Google Analytics as well as Amplitude Analytics, and started asking marketing questions such as:

• During the first 14 days of a new signup’s lifetime, what are the actions that the users do in their first 24 hours that correlate to their purchasing? Do users who connect a display for digital signage purchase premium subscriptions more frequently?

• What is the impact of the features X, Y, and Z on the conversion rate during a trial period?

• Do we know the persona of the user when they sign up? Should we use progressive profiling in the first email to go from only understanding business types to understanding the user’s role?

Next, they started asking questions that helped them decide what to work on in their product development sprints:

• Which of the features X, Y, and Z are more highly correlated with purchasing a subscription?

• What should we incite the users to do when they first sign-up, so they feel the smallest possible barrier to using us daily?

Finally, the marketing team at the digital signage company also started asking the right questions to help the sales flow:

• What is the impact of the features on the renewal rate based on the age/value of the account?

• What activities identify a high value MQL and make them convert, so that we can move them to SQL status, and so we find the point at which sales should jump in to assist?

Benefits of Asking Questions like a Data Analyst

Answers to the right data questions help all companies. They helped Walmart when they implemented barcodes in the 80’s as a way to help get insight into what exactly is selling and why, and they’ll help you when you’re growing your product or campaign. Here’s how:

• Saving time and money by cutting out questions that would not yield results

• Getting the team on the same page by setting clear objectives

• Accelerating growth by uncovering real issues and by discovering growth opportunities

• Building a data-driven culture by always evaluating how insights will be acted on

Over to You

If you’ve read this far, you know the framework to use in order to ask the right questions and extract the insights you need to optimize your business performance. To level up your analytics knowledge further, check out our conversation with analytics experts about the trends that are happening right now.

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