Chapter 12

Finding metrics for Clear Bank

By Anna Tomalik

At the beginning of this course, we established a tracking plan and set up segments for Clear Bank. Now that you are familiar with all sorts of reports and metrics, we can show you where the metrics for Clear Bank’s tracking plan can be found.

Before we start, here’s a reminder of the segments that we’ve built for Clear Bank:

  • Bank’s offer
  • Login first step
  • Login last step
  • Signup first step
  • Signup last step

All of these segments allow us to see sessions and other metrics for selected group of visitors.

Now let’s look at the bank’s goals and KPIs.

Note: Methods that we show you here base on core reports and segments. That’s one of the ways of finding metrics. However, you can also use other routes, for example, create a custom report, set up a funnel, or design a user flow report.

Goal one

The first goal was to inform people well about the bank’s offer. The bank’s offer is presented on several pages, therefore we are using the Bank’s offer segment that picks out visitors who viewed any of those pages. In the tracking plan, we’ve defined a few metrics that will help us judge if the goal is being achieved. Here’s where we can find them:

  • The number of new visitors on pages with the offer.
  1. Pick a segment (Bank’s offer).
  2. Select date range.
  3. Go to Reports > Engagement, and select the following metric: new visitors. For example, 15000 new visitors in May 2018.
  • The number of pages with the offer viewed in one session.
  1. Pick a segment (Bank’s offer).
  2. Select date range.
  3. Go to Reports > Engagement > Sessions, and select the following metrics: page views and sessions.
  4. Calculate pages per session = page views /  sessions. For example, 50000 page views / 20000 sessions = 2.5 pages per session.
  • The bounce rate for pages with the offer.
  1. Pick a segment (Bank’s offer).
  2. Select date range.
  3. Go to Reports > Engagement, and select the following metric: bounce rate. For example, 55% bounce rate.

Goal two

The second goal was to invite people to open an account. We’ve decided that a metric counted as the number of people who started a signup process / the number of people who visited pages with the offer will show us how successful we are at attracting people to the signup. To calculate this metric we have to:

  1. Select date range.
  2. Pick a segment (Bank’s offer), and pick a segment (Signup first step).
  3. Go to Reports > Engagement , and select the following metric: visitors. 
  4. Calculate the metric = visitors for Signup first step / visitors for Bank’s offer. For example, 5000 visitors for Signup first step / 20000 visitors for Bank’s offer = 0,25 (25%).

Goal three

The third goal was to guide people through opening an account smoothly. We’ve agreed that the metric calculated as the number of people who finished a signup process / the number of people who started a signup process will tell us if our signup process is working fine. To count this metric we need to:

  1. Select date range.
  2. Pick a segment (Signup first step), and pick a segment (Signup last step).
  3. Go to Reports > Engagement, select the following metric: visitors. 
  4. Calculate the metric = visitors for Signup last step / visitors for Signup first step. For example, 2500 visitors for Signup last step / 5000 visitors for Signup first step = 0,5 (50%).

Goal four

The fourth goal was to redirect customers to a web app easily. We’ve decided to use the following metric to see if we are successful at guiding people: the number of people who finished a login process / the number of people who started a login process. To calculate this metric we need to:

  1. Select date range.
  2. Pick a segment (Login first step), and pick a segment (Login last step).
  3. Go to Reports > Engagement, select the following metric: visitors. 
  4. Calculate the metric = visitors for Login last step / visitors for Login first step. For example, 5000 visitors for Login last step / 5500 visitors for Login first step = 0,91 (91%).

Goal five

The fifth goal was to help customers who have questions quickly. We’ve agreed that a few metrics will help us understand if we’re doing a good job with our help center. Here’s where we can find them:

  • The time spent in the help center.
  1. Pick a website (Help Center).
  2. Select date range.
  3. Go to Reports > Audience overview, and select the following metric: average session time. For example, 3 minutes 36 seconds average session time.
  • The number of pages viewed in the help center.
  1. Pick a website (Help Center).
  2. Select date range.
  3. Go to Reports > Audience overview, and select the following metric: page views. For example, 15000 page views in May 2018.
  • The number of people who contacted the support through the website / the number of people who visited the help center.

To calculate this metric we would have to use events and Tag Manager, so we will explain this in future courses.

Goal six

Lastly, the sixth goal was to give people helpful knowledge in blog posts. For this purpose, we’ve decided to use the following metrics:

  • The time spent on the blog
  1. Pick a website (Blog).
  2. Select date range.
  3. Go to Reports > Audience overview, and select the following metric: average session time. For example, 4 minutes 36 seconds average session time.
  • The number of pages viewed on the blog.
  1. Pick a website (Blog).
  2. Select date range.
  3. Go to Reports > Audience overview, and select the following metric: page views. For example, 10000 page views in May 2018.

Summary

As you can see every metric we’ve picked as bank KPIs can be found somewhere in a report or calculated from other metrics. In practice, for our monthly reports, we work with customized dashboards, custom reports, and a spreadsheet. As time goes by, we develop better ways of organizing and keeping data, and so will you. Experiment with various routes and come up with ones that suit you the most. 

To finish off, we see Analytics like a combine-harvester on the farmland chewing through grain to keep only the tastiest bits. It’s powerful, pulls in heaps of data and spits out useful metrics and reports. Once you get familiar with the machine, you’ll be able to do more and more with it. And enjoy the mightiness of the tool.