Why Founders Should Pay Attention to AI Powered Analytics

Vikram Aditya
CEO & Co-founder
August 11, 2023

Ever since Open AI dropped ChatGPT on us in December, AI powered products and startups have been sprouting like mushrooms.

Research departments at behemoths like Walmart to newly incumbent start ups have been busy figuring out ways to avoid becoming obsolete. They’re contemplating  building spin-off products, or most importantly harnessing AI to become more efficient-and augment individuals’ capabilities. 

You’d have seen this ripple effect, with the increased popularity of 

  • AI powered videos for marketing (tools like Synthesia and Heygen)
  • Github Copilot for coding
  • AI powered logistics

Puzzlingly though, the integration of AI into analytics has been rather slow.

This is very interesting, considering how many imagine how AI would drastically change our lives-the images of a ‘Smart & Omniscient’ companion that can give us the answers to our question and point out ‘insights’ has been front and centre. Indeed, GPT4 shows that this hope has actually materialized to a large extent, but specific use cases like analytics still represent gaps in the market. 

Now, if you’re a founder building out SaaS products, AI enhanced analytics are likely one of the most crucial ways in which you can turbocharge your organization’s efficiency. 

Let’s have a look at how you could implement this.

IMPLEMENTING AI INTO YOUR ANALYTICS WORKFLOW

As a founder, what are the top problems that boggle your mind every single day?

  • Is my revenue growing?
  • Is the main competitor gaining an advantage?
  • Are my employees happy? Can I retain them?
  • What is my next step when it comes to my product?
  • How do I go about raising the next round of capital?

And so on and so-forth. You’d need a dedicated page just to list down all these thoughts.

However, there is one-thing that sits in the centre of all of this: Product. An understanding of what is happening inside the product and user interactions with the product is the lever that will help you unlock every other metric listed above.

This entails development of the product, marketing of the product, and feature and market expansion. 

Data is only as good as the decisions you make with it. So make sure you're using product analytics to inform your product roadmap, your marketing campaigns, and your customer support efforts." - Marc Benioff, Co-founder and CEO of Salesforce

Why can’t you as a founder, with all the power in your hand, come to this decision?  While there are many things that factor into this, one of the major reasons is a feeling of indecisiveness, which is caused by having access to the right data at the right time.

Did you know , more than 50% of the leaders even in top organizations don’t have access to the data that they want within minutes.

"Product analytics is the lifeblood of any SaaS company. It's how you understand what your customers are doing, what they're liking, and what they're not. Without product analytics, you're flying blind." - Jason Lemkin, Founder and CEO of SaaStr

How do you solve for this ?

You as the decision maker, shouldn’t get roadblocked by multiple levels of touch points inside the company and the one-source of truth - i.e how are the users behaving w.r.t your product?

At the end of the day, they never lie.

This is where A.I can be a game changer for a founder. With LLMs and Generative A.I plugged into your product, you have a second-brain- a smart co-pilot that can assist you in the decision making process.

The power of decisions remain with you, the speed enabled by A.I

No more

  1. Depending on multiple departments to stitch together the user journey and touch-points
  2. Huge founders office teams trying to pitch in theories on what to build next
  3. Numerous data analyst teams going through same piece of data and cross-check insights.

Let A.I streamline these processes.

STREAMLINING WORKFLOWS WITH AI:

1: LEAN TEAM

No one wants to over-hire, but as history tells us - founders sitting on venture capital go crazy so as not to miss out on the best talent in the market. This also stems from a fear of not having the enough people to do the heavy lifting which leads to decision making. Top heavy organizations aren’t anyone’s friend. They lead to the inevitable outcome of layoffs and founders having to pretend to care about the layoffs on LinkedIn.

If you are an early stage founder or even Series A- your goal should still be around improving and building better versions of your product. This means more efforts towards hiring the engineers and a clean distribution mechanism. Leave the data Crunching to a more evolved machine.

2: FRAUD DETECTION

This doesn’t mean intended manipulation of data. But often metrics revolving around traffic, signups and other important KPI’s see’s spikes that are caused by external factors that aren’t going to be consistent across time.

A.I will ensure that your decision making isn’t clouded by that one spike in numbers due to a LinkedIn post or the wrong event being tagged twice by the junior developer.

3: CUSTOMER EXPERIENCE

People working in customer experience-from a retail Walmart to SalesForce will tell you that, the number one rule of customer service is resolving things FAST. This can be a query, request or complaint.

In the traditional path of planning out features and sending out campaigns, you have to follow the path of the data crunchers (analysts) going through the mountain of data on pivot table and then painstakingly cross-checking it with marketing, product and then your founders office before the final data is submitted.

This then further causes a discussion amongst ownership of departments when it comes to execution.

If you can bypass this and let the trained ML model directly give you the insights (which can be validated by respective teams), then you can go 0-100 when it comes to execution.

Thus, a better customer experience.

4: OPTIMIZING DATA USE

Now you must be thinking-but my analysts and founders office people have a unique perspective that comes from understanding the product deeply and spending time in the trenches.

You are right, but what if you could have the A.I in trenches with you. Yes, your very own T-800 from Terminator, working alongside you to protect you and get you to safety.

A.I solutions in analytics currently allowed to be trained on personalized data of your own product. This would be hyper perosnalized towards your product. The more it learns, the better the insights becomes.

5: OPPORTUNITY SOLUTION TREE

The biggest decisions by founders often in early stage startups- and B2B ones often revolve around “What do we build next?” and “Why should we build this”?

This is a question that is going to be thrown back at you if you have a product manager willing to standup to you :P

Now Opportunity Solution Tree Framework and few others , created by smart folks have often found a systematic way to approach this by merging business requirements, development bandwidth and customer requests.

With A.I embedded in your decision making process- your decisions on products speeds by 10X.

The insights you wanted as validation before development commences are validated, fine tuned and accessible in your finger tips.

If you are a founder and worrying about what is the right way to integrate A.I into your day to day business, you should look no further than A.I in your analytics. With the power of fast decision making and efficient insights, you can create a smoother workflow not just for your team but for yourselves. If you agreed with whatever you read so far, drop us a DM here, we are building something very cool, that you can be part of as a beta user.

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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.
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August 11, 2023
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Why Founders Should Pay Attention to AI Powered Analytics

The integration of AI into analytics has been rather slow. Now, if you’re a founder building out SaaS products, AI enhanced analytics areone of the most crucial ways in which you can turbocharge your organization’s efficiency.

Why Founders Should Pay Attention to AI Powered Analytics

Ever since Open AI dropped ChatGPT on us in December, AI powered products and startups have been sprouting like mushrooms.

Research departments at behemoths like Walmart to newly incumbent start ups have been busy figuring out ways to avoid becoming obsolete. They’re contemplating  building spin-off products, or most importantly harnessing AI to become more efficient-and augment individuals’ capabilities. 

You’d have seen this ripple effect, with the increased popularity of 

  • AI powered videos for marketing (tools like Synthesia and Heygen)
  • Github Copilot for coding
  • AI powered logistics

Puzzlingly though, the integration of AI into analytics has been rather slow.

This is very interesting, considering how many imagine how AI would drastically change our lives-the images of a ‘Smart & Omniscient’ companion that can give us the answers to our question and point out ‘insights’ has been front and centre. Indeed, GPT4 shows that this hope has actually materialized to a large extent, but specific use cases like analytics still represent gaps in the market. 

Now, if you’re a founder building out SaaS products, AI enhanced analytics are likely one of the most crucial ways in which you can turbocharge your organization’s efficiency. 

Let’s have a look at how you could implement this.

IMPLEMENTING AI INTO YOUR ANALYTICS WORKFLOW

As a founder, what are the top problems that boggle your mind every single day?

  • Is my revenue growing?
  • Is the main competitor gaining an advantage?
  • Are my employees happy? Can I retain them?
  • What is my next step when it comes to my product?
  • How do I go about raising the next round of capital?

And so on and so-forth. You’d need a dedicated page just to list down all these thoughts.

However, there is one-thing that sits in the centre of all of this: Product. An understanding of what is happening inside the product and user interactions with the product is the lever that will help you unlock every other metric listed above.

This entails development of the product, marketing of the product, and feature and market expansion. 

Data is only as good as the decisions you make with it. So make sure you're using product analytics to inform your product roadmap, your marketing campaigns, and your customer support efforts." - Marc Benioff, Co-founder and CEO of Salesforce

Why can’t you as a founder, with all the power in your hand, come to this decision?  While there are many things that factor into this, one of the major reasons is a feeling of indecisiveness, which is caused by having access to the right data at the right time.

Did you know , more than 50% of the leaders even in top organizations don’t have access to the data that they want within minutes.

"Product analytics is the lifeblood of any SaaS company. It's how you understand what your customers are doing, what they're liking, and what they're not. Without product analytics, you're flying blind." - Jason Lemkin, Founder and CEO of SaaStr

How do you solve for this ?

You as the decision maker, shouldn’t get roadblocked by multiple levels of touch points inside the company and the one-source of truth - i.e how are the users behaving w.r.t your product?

At the end of the day, they never lie.

This is where A.I can be a game changer for a founder. With LLMs and Generative A.I plugged into your product, you have a second-brain- a smart co-pilot that can assist you in the decision making process.

The power of decisions remain with you, the speed enabled by A.I

No more

  1. Depending on multiple departments to stitch together the user journey and touch-points
  2. Huge founders office teams trying to pitch in theories on what to build next
  3. Numerous data analyst teams going through same piece of data and cross-check insights.

Let A.I streamline these processes.

STREAMLINING WORKFLOWS WITH AI:

1: LEAN TEAM

No one wants to over-hire, but as history tells us - founders sitting on venture capital go crazy so as not to miss out on the best talent in the market. This also stems from a fear of not having the enough people to do the heavy lifting which leads to decision making. Top heavy organizations aren’t anyone’s friend. They lead to the inevitable outcome of layoffs and founders having to pretend to care about the layoffs on LinkedIn.

If you are an early stage founder or even Series A- your goal should still be around improving and building better versions of your product. This means more efforts towards hiring the engineers and a clean distribution mechanism. Leave the data Crunching to a more evolved machine.

2: FRAUD DETECTION

This doesn’t mean intended manipulation of data. But often metrics revolving around traffic, signups and other important KPI’s see’s spikes that are caused by external factors that aren’t going to be consistent across time.

A.I will ensure that your decision making isn’t clouded by that one spike in numbers due to a LinkedIn post or the wrong event being tagged twice by the junior developer.

3: CUSTOMER EXPERIENCE

People working in customer experience-from a retail Walmart to SalesForce will tell you that, the number one rule of customer service is resolving things FAST. This can be a query, request or complaint.

In the traditional path of planning out features and sending out campaigns, you have to follow the path of the data crunchers (analysts) going through the mountain of data on pivot table and then painstakingly cross-checking it with marketing, product and then your founders office before the final data is submitted.

This then further causes a discussion amongst ownership of departments when it comes to execution.

If you can bypass this and let the trained ML model directly give you the insights (which can be validated by respective teams), then you can go 0-100 when it comes to execution.

Thus, a better customer experience.

4: OPTIMIZING DATA USE

Now you must be thinking-but my analysts and founders office people have a unique perspective that comes from understanding the product deeply and spending time in the trenches.

You are right, but what if you could have the A.I in trenches with you. Yes, your very own T-800 from Terminator, working alongside you to protect you and get you to safety.

A.I solutions in analytics currently allowed to be trained on personalized data of your own product. This would be hyper perosnalized towards your product. The more it learns, the better the insights becomes.

5: OPPORTUNITY SOLUTION TREE

The biggest decisions by founders often in early stage startups- and B2B ones often revolve around “What do we build next?” and “Why should we build this”?

This is a question that is going to be thrown back at you if you have a product manager willing to standup to you :P

Now Opportunity Solution Tree Framework and few others , created by smart folks have often found a systematic way to approach this by merging business requirements, development bandwidth and customer requests.

With A.I embedded in your decision making process- your decisions on products speeds by 10X.

The insights you wanted as validation before development commences are validated, fine tuned and accessible in your finger tips.

If you are a founder and worrying about what is the right way to integrate A.I into your day to day business, you should look no further than A.I in your analytics. With the power of fast decision making and efficient insights, you can create a smoother workflow not just for your team but for yourselves. If you agreed with whatever you read so far, drop us a DM here, we are building something very cool, that you can be part of as a beta user.

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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