Powerful Use Cases

Our log data is relational.

As a business leader, this enables you to know which part of the code was run, when using which feature, when using which product, in which product version, and by which customer; opening up many powerful use-cases.

This allows you to answer important business questions.

For example,

1. How are customers in general using Product 'X'?

2. Which features of Product 'Y' are most used?

3. Which code blocks have to be made more effective in Product 'Z'?

4. How are the sales of Product 'X', Product 'Y' and Product 'Z' comparing with each other?

5. How has the usage of the new product, Product "New" increased over the past 2 months?

And other insightful questions, at any combination of levels.

Here are 3 other powerful use cases,

• Understand each of your customers uniquely

Netflix and Amazon changed and lead the entertainment and e-commerce industry respectively by providing deep personalization of products when no-one else did. B2B companies can adopt this methodology too to provide an avant-garde experience to their business customers.

Deeply understanding how customers use your product opens up new areas of growth like dynamic pricing based on product usage, recommending products and features based on similar customers, and identifying outlier customers.

• Understand which APIs or Queries consume more time and money to optimize code effectively

APIs and Queries are two of the most time consuming and expensive parts of your code.

We help you prioritize code optimization(time) by understanding which APIs and Queries are bottlenecks and how frequent these are used. Deciding which code to optimize based on these 2 pieces of information (bottlenecks and usage frequency) will ensure that you have the best returns for your optimization efforts.

You can also track API/Query costs and include that to your optimization strategy (if cost information is available).

• Track user-click rate during A/B testing to understand which updates are helping your business

Introduced a new feature to market and want to know if this is actually improving your product source?

Conduct an A/B testing by deploying two versions of your product from two different branches.

As our framework tracks branch(version) information, you can compare user usage across the two different product version. Comparison will conclusively tell you when your feature has a positive lift in usage and you can deploy the new version to all your customers confidently.