Welcome to Hackle's Data Analytics platform.
Hackle's Data Analytics platform is a product analytics tool that allows any user to create their own metrics based on key business KPIs in order to track and understand the customer journey. The metrics track customers' actions or events across your App and Web products and this integrated data allows you to easily understand each and every customer.
Using Hackle's Data Analytics platform is not dependent on the A/B testing platform on the Hackle dashboard. Hence, regardless of whether or not you are running any A/B tests, you are able to visualize and analyze the behavior logs of all users who visit your App or Web products.
One benefit of Hackle's Data Analysis platform is that anyone can set the duration and detail conditions to find hidden insights in the data without using queries.
For example, the following data analytics is possible:
- Analysis of user inflow/visit (split by OS, route, region, etc.)
- Analysis of all conversion rate trends (membership registration, product search, purchase, etc.)
- Business KPI trends (sales, average purchase amount, number of subscribers, etc.)
Data analytics is not only useful for product teams, but for all teams. When all teams are able to view their customers' data in the same light, decision-making will be accelerated and customer experience will be maximized.
- Product Managers/Product Owners who want to understand the customer journey and maximize product growth via data-driven decision-making processes
- Marketers who want to assess data regarding customer acquisition from various channels and their behavior across web and app products
- Data analysts who want to minimize their tedious and repetitive analysis tasks and focus on the important advanced analysis
- Executives who want to see the status of business KPIs at a glance and introduce a data-driven culture within their firm
There are times when it is difficult to get the insight you want with just a simple data trend. With Hackle's Insights chart, you can break down user events into different segments. You can also analyze user retention rates in depth through a retention chart. Major service funnel can also be analyzed by various criteria.
vs Google Analytics
Google Analytics is a great website traffic analytics tool. However, Google Analytics only provides page URLs and session-based analysis, making it very difficult to integrate and analyze the cross-platform behavior of a specific user.
In particular, Google Analytics samples data when the amount of data surpasses a certain amount, making it difficult to fully trust the results.
In addition, Google Analytics doesn't give you the freedom to choose the data to analyze and instead offers a preset amount of data, making it difficult to meet the various analytics needs of different organizations.
Amplitude is a well-known Product Data Analytics tool. Amplitude provides powerful analysis, but it takes a lot of time to get used to the dashboard. If you select multiple items in the Event Segmentation analytics, you must select items within the same criteria, which means that there is a possibility that you may not be able to view sales and orders in a single chart.
In addition, the Amplitude Analytics platforms, A/B test, and feature flag platforms are all separately charged meaning that you can be charged twice each time an event gets triggered.
Mixpanel is also a widely used Product Data Analytics tool. You can easily utilize various analytics functions via Mixpanel. However, unlike Hackle, they do not provide experimental platform functions such as A/B testing or feature flags.
Hackle Data Analytics Platform
Hackle is different. By integrating the behavior log of your Web and App products, you can analyze the entire data in real-time without sampling data.
Hackle is dedicated to providing the all-in-one tool related to product growth, including data analytics and experimentation. Even if a single event is used for three different purposes, A/B testing, feature flags, and data analysis, we only charge you once per triggered event.
Updated over 1 year ago