Metrics in Data
- A good problem statement should be focused on making it not too vague to understand and also descriptive.
- A bad problem statement can lead us to focus on the wrong analysis
- After having a good problem statement we need to make sure that we have a good source of data. Because with the concept of Garbage In Garbage Out, we can’t have a good analysis if we use wrong, or dirty data during our analysis.
- Tracking wrong business metrics can lead us astray. But for some cases, it will be easier to use irrelevant business metrics because it’s easier to track.
- Metrics are measures of components of your business. It captures the data. After having a metric, we can analyze it to answer why the number because what it is.
- The most important metric in a company is called KPI.
- A metric used to be a ratio of a minimum of two variables.
- By using metrics we can measure what is good and what’s not within our company. We can compare different divisions, products, competitors, and different times.
- Good Metric Criteria
Understandable: A good metric is easy to understand by the user of the metric. the Meric might be used by the CEO, and another line of managers, so We need to make it easy to grasp. Even with those who are not seeing the data much.
Comparative: A good metric should make us able to compare our data with the previous time, competitors, or different lines of product.
Ratio / Rate: A good metric should use Ratio/rate because ratio and rate are easy to track and compare. We can easily see differences and act on the business.
Behavior Changing: By looking at the data we should easily understand what to do. Why do we need to change our strategy? Do we need to add more products? More bonus for employees?
- One Metric That Matter is a concept in the Lean Analytics book. It tells us that startups should focus on one metric to be able to track their growth. Because too many metrics can be bad for our focus.
- Vanity vs Actionable Metric. Vanity Metric is the metric where it shows a wide overview of our business. It’s good to use to track a larger business and to know if we are scaling up from a bird's eye point of view. We also have actionable metrics. This metric is more useful for startups and easier to take action. For example, We have pageviews for vanity metric and Bounce rate for Actionable Metric. With page views, we might have a lot of views. But when we look deeper at to bounce rate, we can see that many people exit our page after they open it.
AARRR Pirate Metric
- Pirate Metric is the categorization of metrics based on the AARRR process.
- In each step of AARRR(Acquisition, Activation, Retention, Revenue, Referral) We can track metrics and rate each of our steps based on their metrics.
- Funnel Analysis is the categorization of customer steps from the start to reaching the end of our desired outcome in using our product or website.
- By doing funnel analysis we can track which part of the funnel is our customers decreasing. Why does it happen? And what to do with it? Do we need to improve the experience? Do we need a better banner?
- The important thing to note when we make a funnel is to make sure that the steps are correct.
That’s my study notes from the lecture last night. I’m currently studying in Revou. If you want to study Data Analytics you can start by joining this 2 weeks free course by Revou. It’s totally free, click here.