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We understand that planning for analytics can be overwhelming at times, especially without the proper guidance.
It doesn’t help when the most common requirement is “just tag everything”.
In this article, we will cover all the basics of analytics planning for a company in the banking industry.
If you are new to all these, check out our guides on Adobe Analytics and Google Analytics to get yourself familiarized first.
2. Importance of analytics planning
First, find out what are the important KPIs to the business.
Often time, it is easy to ignore this step and go straight to “just tag everything“.
The misconception is that the more data the better, but… there’s something bad about having too much data.
Having too much data compels you to generate frivolous insights that are of no value at all.
With the “just tag everything” approach, it is easy to miss out on collecting the relevant data that answers the really important business questions.
3. Website characteristics
Typically, a website for banking industry has the following characteristics:
- Consist of many webpages
- Has a customer portal that provides various online services
- Contain tons of informative contents on financial products
- Has many online forms for financial product applications
4. Important KPIs
Below is a list of important KPIs that a company in the banking industry might have:
- Interest level of different financial products
- Demographics of users interested in the financial products
- Sales conversion rate of online financial products
- Form completion rate of online application forms
- Usage of online financial tools/services
- Marketing campaign effectiveness
5. Relevant data/metrics
After understanding the business requirements, next step will be to work backwards and find out what are the data required to answer them.
Here, we will focus more on banking-specific data needs rather than the basic ones (e.g. page name, button name, etc):
- Type (card, loan, insurance, …)
- Category (credit card, debit card, personal loan, business loan, …)
- Type (pay, top-up, transfer, view, apply, …)
- Category (balance, transaction history, recipient, …)
- Type (calculator, locator, …)
- Category (mortgage, loan, branch, atm, …)
- Step (1, 2, 3, …)
- Field (first name, last name, address, …)
- Transaction ID
5. Additional tip
Every company has different business questions, hence different analytics requirements.
It is up to us to provide the foundation and to cater for such differences in our planning.
- Always seek to understand the nature of business first
- Gather a list of important KPIs and business questions
- Work backwards to figure out what are the data requirements
- Plan accordingly