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Getting from raw data to actionable insights for your product analytics has historically been pretty time consuming to get going at a company.
The engineering team first needs to set up a new table, create the schema, run migrations, and start storing events. Then the team needs to work together to create all the events that the product team wants to start tracking. And the engineer has to actually add them after creating them as well! This is a lot of back and forth.
Once the events are set up and firing, the product manager or data analyst still needs to write SQL queries to even start to glean adorable insights from the data. And if a new feature is added or a flow is changed, the engineer needs to add new events and the product manager still needs to write new queries or modify the old ones!
And that’s if you have an experienced team as well. Storing TONS of user events can mean you need to set up redis to not overload your own system and slow it down. And your product managers also still need a way to write and safely run product queries as well to glean insights!
With all that you have to do with figuring out how to get your system setup, figuring out what you need to track, and then asking teams to write a query to provide it to you, the process can be very slow. Even if you have SQL skills, building a query can be a pain. And if you have to wait for the engineering or data team to run a SQL query, this can slow down your team even more.
The back and forth and waiting is all time wasted.
That’s why we’re building MetricStory. We believe the future of product analytics is data accessibility for the entire team.
Your product analytics can be more accessible and more intuitive than ever before with MetricStory. The ability to chat with your product analytics can empower your entire team to make better decisions. Team members without a data science degree can directly interact with product data and get insights from plain English.
The traditional reliance on SQL for querying product analytics has been a barrier for many. Not anymore!
AI is playing a pivotal role in transforming product analytics in predictive insights as well. AI can find patterns and build insights while your team is sleeping.
With MetricStory’s Insight feature, AI will run your charts while you sleep and keep an eye on your data for you.
Insight will make chart suggestions based on the events you pass in and analyze your chart data for you. Your team can wake up to actionable insights based on real customer data and product analytics.
Watching individual Fullstory recordings is great, but with the ability to segment large groups of users in your app and see wide scale patterns, you can do so much more in a short time. With other traditional product analytic tools, they often require advanced technical skills and a significant amount of time to unearth insights. With MetricStory, we’ve turned weeks worth of work into instant insights.
For example, whereas previously a sales team might need to request a complex SQL query from the data team to identify feature adoption rates—pausing both teams’ workflows—MetricStory allows for immediate, natural language queries and responses. This not only streamlines workflows but also democratizes data access, enabling every team member to make data-driven decisions
And before if you had to wait for a customer to report a bug before fixing it, you could put your users at risk for churn, or deleted accounts. But if you can fix an issue before more users see it, you can keep users happier faster.
You can find deeper insights across your entire user base in seconds with MetricStory for your entire team.
Here’s an example of how your customer support team can change with MetricStory!
An e-commerce platform was waiting for users to contact customer support when they had issues and couldn’t make a purchase. This was leading to lost revenue because while many users may see an issue, very few users will go the extra mile to report a bug.
With MetricStory, the e-commerce customer support team were able to find out independently that users were starting the checkout flow, but not completing it at a higher rate than normal. They then queried their events for new errors and were able to see that the checkout screen was being hidden for some browsers.
The engineering team was then able to be alerted before a user even contacted the support team.
The future of product analytics is not just about fancier tools, or smarter algorithms. It’s about opening the doors to data exploration to your entire organization. What if your entire company was making data driven decisions in real-time?
We’re accepting beta users for spring 2024 and we’d love to have your company onboard. Sign up at app.metricstory.ai
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