Why You Should Use Crunch

Vikram Aditya
CEO & Co-founder
August 22, 2023

In a nutshell, product analytics is the process through which product builders interact with their users’ behaviour to guide the users’ behaviour better to meet business objectives. 

Given how obviously (and increasingly) crucial this is with the increasing amount of data yielded per user, the options you’d have as a builder are dizzying. You obviously have your staples like Mixpanel and Amplitude, and relatively new choices like June and Heap. 

Brilliant and useful as these tools may be, they’re not perfect yet, and leave some fruit to be plucked-both low and high. This point is especially underscored if you take into account the frankly insane use cases that the recent boom in generative AI enables within this context. 

So let’s have a look at why you’d benefit from trying out Crunch for your team’s needs.

1: AUTOTRACK (or how Crunch does it)

You must’ve read a bunch about why autotrack is not an optimal solution to product analytics. Why? And more importantly, how do we change it? 

Too Much Data 

Obviously, if you just note down every single tidbit of information carte blanche, you’re going to have way, way too much data. While the painstaking menial work of tagging everything might not be the developers’ headache anymore, someone will still have to label the broad mass of data in order to only tag what is relevant. From the perspective of your team or organization as a whole, the amount of effort and man hours doesn’t really decrease. 

This is where our use of generative AI comes in. 

When it boils down to it, the reason autotrack doesn’t work is because an interpretive layer of intelligence is required to digest data before it is placed in front of any analyst’s eyes. 

This is obviously a low hanging fruit for LLMs to solve. Using our model, all the data that has been collected through autotrack will not be placed in front of you as an inscrutable blob of data-it’ll be instantly parsed through, analyzed, and then presented in a manner useful to your product. 

We’ve spoken more about our version of autotrack over here

2: EASY SETUP

Given that selecting all the data to either track (as is usually the case) or then tag (as would be the case when autotrack is employed) is the bulk of the hassle with setup, you can imagine how setting up Crunch would be substantially less time consuming. 

If you’ve set up any analytics tool for your organization, you’d probably be acquainted with just how much of a pain simply setting up the tool can be. Setting up Mixpanel takes between 10 and 30 hours, and let’s not forget, that includes dev hours. 


Because we have autotrack enabled, the manual intervention required 

With Crunch, the basic setup takes 2 minutes-yes, literally. 

Even just to be able to see the magic that Crunch enables, you’d be good to go within a day-shouldn’t take more than a few hours to get enough data for us to be able to give you the insights you need!

The only thing you’d have to worry about is setting up your widgets-but we’ve made that easy too! 

3: MAGIC BOARD 

Often, within products (or even entire product categories), we see that the nature of the UX isn’t exactly built in tandem with what may be optimal for the users. We think that analytics is one of those domains. Currently, the way that widgets and dashboards are made is as discrete blocks of information. 

Analysts sequentially look at one widget, then the next, after which they come up with insights. 

Of course, this isn’t how analysts think (or for that matter, anyone dealing with any complex set of information). 

Data analysis, like most structured analysis, happens in thought trees. You have one thought, which narrows down to the next viable thoughts or decisions, and so on. 

For kickstarting such a ‘tree’ though, recommendations can be invaluable. Just think about the rabbit holes Spotify or YouTube can send you down because of their recommendations! 

With Magic Board, that’s exactly what we’re doing. 

We’re revamping how you interact with your data. 

Static dashboards are a thing of the past. Much like with every other realm within knowledge work, Generative AI enables copilots for analytics.

Magic Board is our take on that. 

First, we’ll recommend what we may think might be relevant to you. This would be based upon current data points or variables that may have drastically changed, the data you’ve been perusing, or the questions you’ve been asking!

Let’s say you went with the ‘weekly trend of user engagement’ prompt.

Of course, you can ask anything you want about the data, but we’ve given some particular suggestions as well!

By combining a contextual understanding of user interactions with an understanding of the data, Crunch’s custom model mimics the way an adept analyst might think-with a procedural flow of information.

4: MODULAR STACK 

Because of our modular architecture, you will be able to query data from any tool used by any team within your organization. 

For example, whether you want Crunch to get data from Salesforce or from Snowflake, our model will be able to get you the insights that you need. 

Other than sheer ease of use, there’s a few pretty significant benefits to this: 

A: UNIFIED DATA ACCESS

The modular architecture allows querying data from various tools used across different teams. This unification makes information retrieval seamless, regardless of the source, whether Salesforce, Snowflake, or any other platform.

B: FRICTION REDUCTION

The flexibility and adaptability of a modular stack reduce friction by streamlining processes. This can lead to quicker decision-making and can save valuable time, particularly in a fast-paced startup environment.

C: CROSS POLLINATION

This is perhaps one of the most intriguing aspects. By enabling data sharing across platforms and teams, the modular architecture facilitates the cross-pollination of data. 

Heard of the ‘Medici Effect’? It’s the notion that knowledge from disparate sources of information can spark creativity. It’s credited with a lot of Steve Jobs’ success, and there is no reason that it can’t apply to your team too. 

E: SCALABILITY

A modular architecture is more adaptable to changes and growth, almost by definition. If you can pick and play with your stack’s lego pieces as you please without affecting your analytics tooling, you have one less thing to worry about. 

F: COST EFFICIENCY

Perhaps it goes without saying, but the ability to leverage existing tools across different teams can reduce the need for additional software purchases, which can obviously shave costs. 

Ready to get valuable Product Insights?

If you are looking to make better use of your product data, gain insights faster and improve decision making across teams, Crunch can help you get there.
Book a demo
August 22, 2023
Growth

Why You Should Use Crunch

Generative AI has changed the nature of knowledge work in every domain. Utilizing it within analytics requires more than simply tacking on a text based interface though. Let’s see how Crunch changes the game by baking in Generative AI at the heart of the analytics experience.

Why You Should Use Crunch

In a nutshell, product analytics is the process through which product builders interact with their users’ behaviour to guide the users’ behaviour better to meet business objectives. 

Given how obviously (and increasingly) crucial this is with the increasing amount of data yielded per user, the options you’d have as a builder are dizzying. You obviously have your staples like Mixpanel and Amplitude, and relatively new choices like June and Heap. 

Brilliant and useful as these tools may be, they’re not perfect yet, and leave some fruit to be plucked-both low and high. This point is especially underscored if you take into account the frankly insane use cases that the recent boom in generative AI enables within this context. 

So let’s have a look at why you’d benefit from trying out Crunch for your team’s needs.

1: AUTOTRACK (or how Crunch does it)

You must’ve read a bunch about why autotrack is not an optimal solution to product analytics. Why? And more importantly, how do we change it? 

Too Much Data 

Obviously, if you just note down every single tidbit of information carte blanche, you’re going to have way, way too much data. While the painstaking menial work of tagging everything might not be the developers’ headache anymore, someone will still have to label the broad mass of data in order to only tag what is relevant. From the perspective of your team or organization as a whole, the amount of effort and man hours doesn’t really decrease. 

This is where our use of generative AI comes in. 

When it boils down to it, the reason autotrack doesn’t work is because an interpretive layer of intelligence is required to digest data before it is placed in front of any analyst’s eyes. 

This is obviously a low hanging fruit for LLMs to solve. Using our model, all the data that has been collected through autotrack will not be placed in front of you as an inscrutable blob of data-it’ll be instantly parsed through, analyzed, and then presented in a manner useful to your product. 

We’ve spoken more about our version of autotrack over here

2: EASY SETUP

Given that selecting all the data to either track (as is usually the case) or then tag (as would be the case when autotrack is employed) is the bulk of the hassle with setup, you can imagine how setting up Crunch would be substantially less time consuming. 

If you’ve set up any analytics tool for your organization, you’d probably be acquainted with just how much of a pain simply setting up the tool can be. Setting up Mixpanel takes between 10 and 30 hours, and let’s not forget, that includes dev hours. 


Because we have autotrack enabled, the manual intervention required 

With Crunch, the basic setup takes 2 minutes-yes, literally. 

Even just to be able to see the magic that Crunch enables, you’d be good to go within a day-shouldn’t take more than a few hours to get enough data for us to be able to give you the insights you need!

The only thing you’d have to worry about is setting up your widgets-but we’ve made that easy too! 

3: MAGIC BOARD 

Often, within products (or even entire product categories), we see that the nature of the UX isn’t exactly built in tandem with what may be optimal for the users. We think that analytics is one of those domains. Currently, the way that widgets and dashboards are made is as discrete blocks of information. 

Analysts sequentially look at one widget, then the next, after which they come up with insights. 

Of course, this isn’t how analysts think (or for that matter, anyone dealing with any complex set of information). 

Data analysis, like most structured analysis, happens in thought trees. You have one thought, which narrows down to the next viable thoughts or decisions, and so on. 

For kickstarting such a ‘tree’ though, recommendations can be invaluable. Just think about the rabbit holes Spotify or YouTube can send you down because of their recommendations! 

With Magic Board, that’s exactly what we’re doing. 

We’re revamping how you interact with your data. 

Static dashboards are a thing of the past. Much like with every other realm within knowledge work, Generative AI enables copilots for analytics.

Magic Board is our take on that. 

First, we’ll recommend what we may think might be relevant to you. This would be based upon current data points or variables that may have drastically changed, the data you’ve been perusing, or the questions you’ve been asking!

Let’s say you went with the ‘weekly trend of user engagement’ prompt.

Of course, you can ask anything you want about the data, but we’ve given some particular suggestions as well!

By combining a contextual understanding of user interactions with an understanding of the data, Crunch’s custom model mimics the way an adept analyst might think-with a procedural flow of information.

4: MODULAR STACK 

Because of our modular architecture, you will be able to query data from any tool used by any team within your organization. 

For example, whether you want Crunch to get data from Salesforce or from Snowflake, our model will be able to get you the insights that you need. 

Other than sheer ease of use, there’s a few pretty significant benefits to this: 

A: UNIFIED DATA ACCESS

The modular architecture allows querying data from various tools used across different teams. This unification makes information retrieval seamless, regardless of the source, whether Salesforce, Snowflake, or any other platform.

B: FRICTION REDUCTION

The flexibility and adaptability of a modular stack reduce friction by streamlining processes. This can lead to quicker decision-making and can save valuable time, particularly in a fast-paced startup environment.

C: CROSS POLLINATION

This is perhaps one of the most intriguing aspects. By enabling data sharing across platforms and teams, the modular architecture facilitates the cross-pollination of data. 

Heard of the ‘Medici Effect’? It’s the notion that knowledge from disparate sources of information can spark creativity. It’s credited with a lot of Steve Jobs’ success, and there is no reason that it can’t apply to your team too. 

E: SCALABILITY

A modular architecture is more adaptable to changes and growth, almost by definition. If you can pick and play with your stack’s lego pieces as you please without affecting your analytics tooling, you have one less thing to worry about. 

F: COST EFFICIENCY

Perhaps it goes without saying, but the ability to leverage existing tools across different teams can reduce the need for additional software purchases, which can obviously shave costs. 

Customer retention is the key

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What are the most relevant factors to consider?

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Don’t overspend on growth marketing without good retention rates

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What’s the ideal customer retention rate?

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Next steps to increase your customer retention

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