What people get wrong about platform-embedded analytics
Powered by GitBook
Powered by GitBook
Powered by GitBook
Powered by GitBook
14 Aug 2023
Toucan Toco is a customer-facing analytics platform designed for a great customer experience. And with it’s GitBook integration, you can now bring those powerful and detailed analytics right into your knowledge base or public docs. So we asked the Toucan team for their best advice when considering platform-embedded analytics, and what mistakes companies make when making their choice.
We’ve seen tremendous progress in the analytics industry recently, as more and more organizations are surfacing the value of their data. Here at Toucan Toco, we’ve had the privilege of working with some phenomenal partners — including GitBook — to help them embed platform analytics into their products. And while there have been many great successes along the way, we’ve also seen plenty of mistakes.
Platform-embedded analytics isn’t plug-and-play. Making mistakes is all too easy, and can be the difference between a successful launch and one that fails to deliver value for customers. Here are the four most common things people get wrong about platform-embedded analytics:
1. Bringing in the wrong people
Businesses often make the mistake of not realizing their own skill gaps. In practical terms, this means that they ask their existing internal team to deliver embedded analytics solutions, regardless of their level of expertise.
To successfully take an embedded application to market, your team need a variety of abilities. There’s often a lack of core platform-embedded skills at the start of projects — which are particularly important in the brainstorming and initial data analysis phases.
To create an application with valuable, engaging content, you need to understand the structure of information in your application. Being able to analyze data effectively is essential to ensure your product delivers those nuggets of gold that make your prospects say, “I want that one”.
These skills will help your technical team translate that understanding into product requirements. This helps you structure information in a way that brings value to your customers and is easy for them to understand — including useful answers and engaging dashboards.
There’s only one chance to make a good first impression with your new platform-embedded analytics solution, so make sure it stands out from the crowd. Make sure you bring in the right people with the right skills from the very beginning to increase your chances of hitting the right notes with your audience.
Source: Toucan
2. De-emphasizing good design
A lot of businesses often forget a key fact: great design attracts customers.
We’ve lost count of the times we’ve seen dashboards that include 15 reports and 30 filters to pack in as many important metrics as possible. But approaching design with an ‘everything and the kitchen sink’ mentality simply isn’t coherent. These extra bells and whistles can be overwhelming for customers — and in many cases, they won’t even use them.
You need to design your application for your target personas. And the first step is defining your personas and describing their needs. Ask yourself:
who are they? how will analytics help them achieve their goals? what will they do with the analytics? what is the frequency of use? do they have a good understanding of technology?
Another common mistake is designing your data in a way that heavily resembles the way people have historically interacted with it — tabular spreadsheet-style content is a great example. Just because people are used to seeing data in this form doesn’t necessarily mean that it’s the most effective way to convey information.
People are generally able to absorb information faster and more effectively when it’s visual. Consider the tools you have, and use their capabilities to benefit your customers.
3. Ignoring the appropriate level of integration
You can embed platform analytics into your application in a number of ways. But many teams don’t take the time to consider the right level of integration at the outset. With the right integration, you give your customers a better experience, and your sales team will find it easier to sell.
White-labeling refers to the ability to fully customize the embedded analytical application within your own platform. It has the same look and feel, and there’s only one sign-on — it’s a small task, but it makes a big difference to the customer, who thinks they are buying a single product. And that helps reduce confusion for your customer, too.
However, it white labelling does mean that you can’t rely on the underlying vendor for customer support materials.
The next step down from white labeling is grey labeling. It’s very similar to white labelling, but it clearly shows the company that’s powering your embedded solution. As an example, it might say “Powered by Toucan”. There are plenty of advantages with this approach. You have all the advantages of white labeling, as well as the ability to rely on the vendor for collateral materials, a roadmap, and training. And that means you can test the waters and get your customers excited about the features new that are coming soon.
The third option is not to integrate at all. Some analytics applications sit next to the product and have the look and feel of the embedded analytics vendor, but only use the data from your product. This may be an easier path, but you may end up creating confusion within your customer base and complicating the sales process.
Source: Toucan
4. Misunderstanding the customer’s needs
Businesses often make assumptions about what their customers want without speaking to them. To get your product right, you need to understand what will excite your customers. Many organizations create dashboards and dozens of reports, but then only use a small percentage of them!
While some businesses assume they know what their customers want from their analytics and dashboards, it’s always a good idea to talk to your customers and ask about their needs before launching a product. Consider talking to other people in the marketplace if you don’t have any customers. And when you get that feedback, use it to improve your application.
Without speaking to your customers, you’ll be going in blind. And there’s no point spending time and effort on something they ultimately won’t use.
Huge thanks to Toucan for this post. Be sure to check out the Toucan Toco GitBook integration, which brings all your analytics right into your GitBook knowledge base or public docs.
Toucan Toco is a customer-facing analytics platform designed for a great customer experience. And with it’s GitBook integration, you can now bring those powerful and detailed analytics right into your knowledge base or public docs. So we asked the Toucan team for their best advice when considering platform-embedded analytics, and what mistakes companies make when making their choice.
We’ve seen tremendous progress in the analytics industry recently, as more and more organizations are surfacing the value of their data. Here at Toucan Toco, we’ve had the privilege of working with some phenomenal partners — including GitBook — to help them embed platform analytics into their products. And while there have been many great successes along the way, we’ve also seen plenty of mistakes.
Platform-embedded analytics isn’t plug-and-play. Making mistakes is all too easy, and can be the difference between a successful launch and one that fails to deliver value for customers. Here are the four most common things people get wrong about platform-embedded analytics:
1. Bringing in the wrong people
Businesses often make the mistake of not realizing their own skill gaps. In practical terms, this means that they ask their existing internal team to deliver embedded analytics solutions, regardless of their level of expertise.
To successfully take an embedded application to market, your team need a variety of abilities. There’s often a lack of core platform-embedded skills at the start of projects — which are particularly important in the brainstorming and initial data analysis phases.
To create an application with valuable, engaging content, you need to understand the structure of information in your application. Being able to analyze data effectively is essential to ensure your product delivers those nuggets of gold that make your prospects say, “I want that one”.
These skills will help your technical team translate that understanding into product requirements. This helps you structure information in a way that brings value to your customers and is easy for them to understand — including useful answers and engaging dashboards.
There’s only one chance to make a good first impression with your new platform-embedded analytics solution, so make sure it stands out from the crowd. Make sure you bring in the right people with the right skills from the very beginning to increase your chances of hitting the right notes with your audience.
Source: Toucan
2. De-emphasizing good design
A lot of businesses often forget a key fact: great design attracts customers.
We’ve lost count of the times we’ve seen dashboards that include 15 reports and 30 filters to pack in as many important metrics as possible. But approaching design with an ‘everything and the kitchen sink’ mentality simply isn’t coherent. These extra bells and whistles can be overwhelming for customers — and in many cases, they won’t even use them.
You need to design your application for your target personas. And the first step is defining your personas and describing their needs. Ask yourself:
who are they? how will analytics help them achieve their goals? what will they do with the analytics? what is the frequency of use? do they have a good understanding of technology?
Another common mistake is designing your data in a way that heavily resembles the way people have historically interacted with it — tabular spreadsheet-style content is a great example. Just because people are used to seeing data in this form doesn’t necessarily mean that it’s the most effective way to convey information.
People are generally able to absorb information faster and more effectively when it’s visual. Consider the tools you have, and use their capabilities to benefit your customers.
3. Ignoring the appropriate level of integration
You can embed platform analytics into your application in a number of ways. But many teams don’t take the time to consider the right level of integration at the outset. With the right integration, you give your customers a better experience, and your sales team will find it easier to sell.
White-labeling refers to the ability to fully customize the embedded analytical application within your own platform. It has the same look and feel, and there’s only one sign-on — it’s a small task, but it makes a big difference to the customer, who thinks they are buying a single product. And that helps reduce confusion for your customer, too.
However, it white labelling does mean that you can’t rely on the underlying vendor for customer support materials.
The next step down from white labeling is grey labeling. It’s very similar to white labelling, but it clearly shows the company that’s powering your embedded solution. As an example, it might say “Powered by Toucan”. There are plenty of advantages with this approach. You have all the advantages of white labeling, as well as the ability to rely on the vendor for collateral materials, a roadmap, and training. And that means you can test the waters and get your customers excited about the features new that are coming soon.
The third option is not to integrate at all. Some analytics applications sit next to the product and have the look and feel of the embedded analytics vendor, but only use the data from your product. This may be an easier path, but you may end up creating confusion within your customer base and complicating the sales process.
Source: Toucan
4. Misunderstanding the customer’s needs
Businesses often make assumptions about what their customers want without speaking to them. To get your product right, you need to understand what will excite your customers. Many organizations create dashboards and dozens of reports, but then only use a small percentage of them!
While some businesses assume they know what their customers want from their analytics and dashboards, it’s always a good idea to talk to your customers and ask about their needs before launching a product. Consider talking to other people in the marketplace if you don’t have any customers. And when you get that feedback, use it to improve your application.
Without speaking to your customers, you’ll be going in blind. And there’s no point spending time and effort on something they ultimately won’t use.
Huge thanks to Toucan for this post. Be sure to check out the Toucan Toco GitBook integration, which brings all your analytics right into your GitBook knowledge base or public docs.
Toucan Toco is a customer-facing analytics platform designed for a great customer experience. And with it’s GitBook integration, you can now bring those powerful and detailed analytics right into your knowledge base or public docs. So we asked the Toucan team for their best advice when considering platform-embedded analytics, and what mistakes companies make when making their choice.
We’ve seen tremendous progress in the analytics industry recently, as more and more organizations are surfacing the value of their data. Here at Toucan Toco, we’ve had the privilege of working with some phenomenal partners — including GitBook — to help them embed platform analytics into their products. And while there have been many great successes along the way, we’ve also seen plenty of mistakes.
Platform-embedded analytics isn’t plug-and-play. Making mistakes is all too easy, and can be the difference between a successful launch and one that fails to deliver value for customers. Here are the four most common things people get wrong about platform-embedded analytics:
1. Bringing in the wrong people
Businesses often make the mistake of not realizing their own skill gaps. In practical terms, this means that they ask their existing internal team to deliver embedded analytics solutions, regardless of their level of expertise.
To successfully take an embedded application to market, your team need a variety of abilities. There’s often a lack of core platform-embedded skills at the start of projects — which are particularly important in the brainstorming and initial data analysis phases.
To create an application with valuable, engaging content, you need to understand the structure of information in your application. Being able to analyze data effectively is essential to ensure your product delivers those nuggets of gold that make your prospects say, “I want that one”.
These skills will help your technical team translate that understanding into product requirements. This helps you structure information in a way that brings value to your customers and is easy for them to understand — including useful answers and engaging dashboards.
There’s only one chance to make a good first impression with your new platform-embedded analytics solution, so make sure it stands out from the crowd. Make sure you bring in the right people with the right skills from the very beginning to increase your chances of hitting the right notes with your audience.
Source: Toucan
2. De-emphasizing good design
A lot of businesses often forget a key fact: great design attracts customers.
We’ve lost count of the times we’ve seen dashboards that include 15 reports and 30 filters to pack in as many important metrics as possible. But approaching design with an ‘everything and the kitchen sink’ mentality simply isn’t coherent. These extra bells and whistles can be overwhelming for customers — and in many cases, they won’t even use them.
You need to design your application for your target personas. And the first step is defining your personas and describing their needs. Ask yourself:
who are they? how will analytics help them achieve their goals? what will they do with the analytics? what is the frequency of use? do they have a good understanding of technology?
Another common mistake is designing your data in a way that heavily resembles the way people have historically interacted with it — tabular spreadsheet-style content is a great example. Just because people are used to seeing data in this form doesn’t necessarily mean that it’s the most effective way to convey information.
People are generally able to absorb information faster and more effectively when it’s visual. Consider the tools you have, and use their capabilities to benefit your customers.
3. Ignoring the appropriate level of integration
You can embed platform analytics into your application in a number of ways. But many teams don’t take the time to consider the right level of integration at the outset. With the right integration, you give your customers a better experience, and your sales team will find it easier to sell.
White-labeling refers to the ability to fully customize the embedded analytical application within your own platform. It has the same look and feel, and there’s only one sign-on — it’s a small task, but it makes a big difference to the customer, who thinks they are buying a single product. And that helps reduce confusion for your customer, too.
However, it white labelling does mean that you can’t rely on the underlying vendor for customer support materials.
The next step down from white labeling is grey labeling. It’s very similar to white labelling, but it clearly shows the company that’s powering your embedded solution. As an example, it might say “Powered by Toucan”. There are plenty of advantages with this approach. You have all the advantages of white labeling, as well as the ability to rely on the vendor for collateral materials, a roadmap, and training. And that means you can test the waters and get your customers excited about the features new that are coming soon.
The third option is not to integrate at all. Some analytics applications sit next to the product and have the look and feel of the embedded analytics vendor, but only use the data from your product. This may be an easier path, but you may end up creating confusion within your customer base and complicating the sales process.
Source: Toucan
4. Misunderstanding the customer’s needs
Businesses often make assumptions about what their customers want without speaking to them. To get your product right, you need to understand what will excite your customers. Many organizations create dashboards and dozens of reports, but then only use a small percentage of them!
While some businesses assume they know what their customers want from their analytics and dashboards, it’s always a good idea to talk to your customers and ask about their needs before launching a product. Consider talking to other people in the marketplace if you don’t have any customers. And when you get that feedback, use it to improve your application.
Without speaking to your customers, you’ll be going in blind. And there’s no point spending time and effort on something they ultimately won’t use.
Huge thanks to Toucan for this post. Be sure to check out the Toucan Toco GitBook integration, which brings all your analytics right into your GitBook knowledge base or public docs.
Toucan Toco is a customer-facing analytics platform designed for a great customer experience. And with it’s GitBook integration, you can now bring those powerful and detailed analytics right into your knowledge base or public docs. So we asked the Toucan team for their best advice when considering platform-embedded analytics, and what mistakes companies make when making their choice.
We’ve seen tremendous progress in the analytics industry recently, as more and more organizations are surfacing the value of their data. Here at Toucan Toco, we’ve had the privilege of working with some phenomenal partners — including GitBook — to help them embed platform analytics into their products. And while there have been many great successes along the way, we’ve also seen plenty of mistakes.
Platform-embedded analytics isn’t plug-and-play. Making mistakes is all too easy, and can be the difference between a successful launch and one that fails to deliver value for customers. Here are the four most common things people get wrong about platform-embedded analytics:
1. Bringing in the wrong people
Businesses often make the mistake of not realizing their own skill gaps. In practical terms, this means that they ask their existing internal team to deliver embedded analytics solutions, regardless of their level of expertise.
To successfully take an embedded application to market, your team need a variety of abilities. There’s often a lack of core platform-embedded skills at the start of projects — which are particularly important in the brainstorming and initial data analysis phases.
To create an application with valuable, engaging content, you need to understand the structure of information in your application. Being able to analyze data effectively is essential to ensure your product delivers those nuggets of gold that make your prospects say, “I want that one”.
These skills will help your technical team translate that understanding into product requirements. This helps you structure information in a way that brings value to your customers and is easy for them to understand — including useful answers and engaging dashboards.
There’s only one chance to make a good first impression with your new platform-embedded analytics solution, so make sure it stands out from the crowd. Make sure you bring in the right people with the right skills from the very beginning to increase your chances of hitting the right notes with your audience.
Source: Toucan
2. De-emphasizing good design
A lot of businesses often forget a key fact: great design attracts customers.
We’ve lost count of the times we’ve seen dashboards that include 15 reports and 30 filters to pack in as many important metrics as possible. But approaching design with an ‘everything and the kitchen sink’ mentality simply isn’t coherent. These extra bells and whistles can be overwhelming for customers — and in many cases, they won’t even use them.
You need to design your application for your target personas. And the first step is defining your personas and describing their needs. Ask yourself:
who are they? how will analytics help them achieve their goals? what will they do with the analytics? what is the frequency of use? do they have a good understanding of technology?
Another common mistake is designing your data in a way that heavily resembles the way people have historically interacted with it — tabular spreadsheet-style content is a great example. Just because people are used to seeing data in this form doesn’t necessarily mean that it’s the most effective way to convey information.
People are generally able to absorb information faster and more effectively when it’s visual. Consider the tools you have, and use their capabilities to benefit your customers.
3. Ignoring the appropriate level of integration
You can embed platform analytics into your application in a number of ways. But many teams don’t take the time to consider the right level of integration at the outset. With the right integration, you give your customers a better experience, and your sales team will find it easier to sell.
White-labeling refers to the ability to fully customize the embedded analytical application within your own platform. It has the same look and feel, and there’s only one sign-on — it’s a small task, but it makes a big difference to the customer, who thinks they are buying a single product. And that helps reduce confusion for your customer, too.
However, it white labelling does mean that you can’t rely on the underlying vendor for customer support materials.
The next step down from white labeling is grey labeling. It’s very similar to white labelling, but it clearly shows the company that’s powering your embedded solution. As an example, it might say “Powered by Toucan”. There are plenty of advantages with this approach. You have all the advantages of white labeling, as well as the ability to rely on the vendor for collateral materials, a roadmap, and training. And that means you can test the waters and get your customers excited about the features new that are coming soon.
The third option is not to integrate at all. Some analytics applications sit next to the product and have the look and feel of the embedded analytics vendor, but only use the data from your product. This may be an easier path, but you may end up creating confusion within your customer base and complicating the sales process.
Source: Toucan
4. Misunderstanding the customer’s needs
Businesses often make assumptions about what their customers want without speaking to them. To get your product right, you need to understand what will excite your customers. Many organizations create dashboards and dozens of reports, but then only use a small percentage of them!
While some businesses assume they know what their customers want from their analytics and dashboards, it’s always a good idea to talk to your customers and ask about their needs before launching a product. Consider talking to other people in the marketplace if you don’t have any customers. And when you get that feedback, use it to improve your application.
Without speaking to your customers, you’ll be going in blind. And there’s no point spending time and effort on something they ultimately won’t use.
Huge thanks to Toucan for this post. Be sure to check out the Toucan Toco GitBook integration, which brings all your analytics right into your GitBook knowledge base or public docs.
Get the GitBook newsletter
Get the latest product news, useful resources and more in your inbox. 130k+ people read it every month.
Similar posts
Create, search and manage your knowledge at scale. Effortlessly.
Create, search and manage your knowledge at scale. Effortlessly.
Create, search and manage your knowledge at scale. Effortlessly.
Create, search and manage your knowledge at scale. Effortlessly.