April 11, 2024
Being data-driven, are we there yet?
Ambera Cruz
Editor in Chief, Planhat
Being data-driven should be standard nowadays, but many organizations still struggle with it. Every company wants to be data-driven, but putting it into practice is the tough part.
In a Harvard Business Review article from 2018, it was noted that while 99% of companies surveyed intended to be data-driven, only 20% felt they had succeeded.
Becoming data-driven requires a cultural shift. It’s not just about putting data in presentations or on websites; it’s about educating everyone in the organization about why data is important and how it will be used.
Part of this cultural shift means agreeing as an organization to be data-driven, the recognition that each individual is a data creator, not just a recipient, and ensuring that everyone is creating and acting on good data. Simply having a data team isn’t enough; it’s about integrating data into your culture.
Using data across the organization
While every single function in the business plays a part in retaining and growing customers, it’s the core objective for modern CS teams. Part of that responsibility means quarterbacking the customer journey across all the other functions within the organization ensuring that customer data reaches the right departments.
CS should communicate insights gained from customer data to suggest initiatives like advocacy programs and referral pipelines that Marketing can spin up for the benefit of Sales. Over time, this can lead to a cultural shift where everyone recognizes the value of data and knows how to use it effectively across teams.
Three ways to think about product usage data
When people think about product usage data, they’re usually thinking about dips and troughs of usage. Product usage up means good, product usage down means bad. But at a higher level, you have to think about it in three ways.
The first is understanding if the product usage of the customer is aligned with the outcomes that they want to achieve. A customer can use a product in a million different ways, but that doesn’t matter if their goals aren’t aligned with the way that they’re using the product. That’s often an oversight.
The second is a simple one. Make sure your customers are using your product the way it was designed to be used. It’s often the case that a customer starts using your product in a way that it wasn’t designed for. To an extent, that may seem great, opening up a new use case. But 9 times out of ten, that customer will churn. Make sure your customers are using your products the way that they’re intended because all products start to get wobbly at the edges.
The third thing, and arguably most important, when it comes to fighting churn and product usage is time. What you’re trying to understand is how the current state of a data point compares to a prior state and use that relationship to understand what a future state might look like. Whether you’re using a health score and see the health going up or down, or whether it’s specific usage of a feature, it’s always the current state versus the prior state and how that’s going to influence the future state.
Product usage data in action
Common use cases:
Correlations between high customer retention rates and customers who’ve completed certifications. This drives adoption across their organization leading to retention as more people become familiar with the software and use it to solve more problems.
Driving expansion through intent. Expansion where a customer using one feature a lot could mean they could find value in another complementary product.
Tactical expansion around thresholds. If a customer hits 80% of a consumption threshold or 80% of a licensing threshold, an auto email can be sent to the decision maker.
Using product analytics to white space. Break out all of the products or features within your product l, and whitespace across your portfolio to find products that have high usage across a certain cohort of customers and find opportunities with similar customers.
Last point can also be applied to better understand which cohort of customers may not be adopting a certain product and may need additional enablement.
Better collaboration between teams
You can really look at any function and say there’s a clear way of collaborating around customer data. Customers should be at the core of every business, and therefore, all teams should have access to customer data to better understand how their department can contribute to improving the customer experience.
It makes a lot of sense that product teams live in the customer success platform. Understanding customer behavior, which products are getting good adoption, and even identifying groups of users to understand why they’re using a feature in a certain way is hugely beneficial for their product strategy. Product analytics gives them that.
Customer marketing is another great way to help with retention and expansion by putting on personalized events. Product analytics can provide a ton of personalization across a huge amount of data. As an example, you can find decision makers based in New York whose team are using product X a lot, but product Y, not so much. You can invite them to an executive dinner in New York and include customers who are finding a lot of value using product Y.
Diminishing data returns
When data loses its context, it loses its value.
If you’re using data to create a predictive churn model, aiming to forecast customer behavior within the next three to six months, you have to also take into account the commercial context that can’t be found in customer data. When data loses its context, it can become worthless.
Further, inaccuracies in data erode trust over time and if influential people in your organization dismiss data, it becomes obsolete. However, there’s a common tendency to blame the system where data errors are spotted, such as a CRM platform. Make sure to trace the data back to its source to correctly solve data inaccuracies.
Remember, everyone in a data-driven organization plays a role in being both good data creators and good data recipients.
It can be overwhelming to know where to start, so simplify and focus on what’s most relevant to your objectives. Becoming data-driven isn’t about achieving instant success; it’s about using data to gain insights that help you reach your goals over time. Tying data to objectives and taking incremental steps towards improvement.
Being data-driven should be standard nowadays, but many organizations still struggle with it. Every company wants to be data-driven, but putting it into practice is the tough part.
In a Harvard Business Review article from 2018, it was noted that while 99% of companies surveyed intended to be data-driven, only 20% felt they had succeeded.
Becoming data-driven requires a cultural shift. It’s not just about putting data in presentations or on websites; it’s about educating everyone in the organization about why data is important and how it will be used.
Part of this cultural shift means agreeing as an organization to be data-driven, the recognition that each individual is a data creator, not just a recipient, and ensuring that everyone is creating and acting on good data. Simply having a data team isn’t enough; it’s about integrating data into your culture.
Using data across the organization
While every single function in the business plays a part in retaining and growing customers, it’s the core objective for modern CS teams. Part of that responsibility means quarterbacking the customer journey across all the other functions within the organization ensuring that customer data reaches the right departments.
CS should communicate insights gained from customer data to suggest initiatives like advocacy programs and referral pipelines that Marketing can spin up for the benefit of Sales. Over time, this can lead to a cultural shift where everyone recognizes the value of data and knows how to use it effectively across teams.
Three ways to think about product usage data
When people think about product usage data, they’re usually thinking about dips and troughs of usage. Product usage up means good, product usage down means bad. But at a higher level, you have to think about it in three ways.
The first is understanding if the product usage of the customer is aligned with the outcomes that they want to achieve. A customer can use a product in a million different ways, but that doesn’t matter if their goals aren’t aligned with the way that they’re using the product. That’s often an oversight.
The second is a simple one. Make sure your customers are using your product the way it was designed to be used. It’s often the case that a customer starts using your product in a way that it wasn’t designed for. To an extent, that may seem great, opening up a new use case. But 9 times out of ten, that customer will churn. Make sure your customers are using your products the way that they’re intended because all products start to get wobbly at the edges.
The third thing, and arguably most important, when it comes to fighting churn and product usage is time. What you’re trying to understand is how the current state of a data point compares to a prior state and use that relationship to understand what a future state might look like. Whether you’re using a health score and see the health going up or down, or whether it’s specific usage of a feature, it’s always the current state versus the prior state and how that’s going to influence the future state.
Product usage data in action
Common use cases:
Correlations between high customer retention rates and customers who’ve completed certifications. This drives adoption across their organization leading to retention as more people become familiar with the software and use it to solve more problems.
Driving expansion through intent. Expansion where a customer using one feature a lot could mean they could find value in another complementary product.
Tactical expansion around thresholds. If a customer hits 80% of a consumption threshold or 80% of a licensing threshold, an auto email can be sent to the decision maker.
Using product analytics to white space. Break out all of the products or features within your product l, and whitespace across your portfolio to find products that have high usage across a certain cohort of customers and find opportunities with similar customers.
Last point can also be applied to better understand which cohort of customers may not be adopting a certain product and may need additional enablement.
Better collaboration between teams
You can really look at any function and say there’s a clear way of collaborating around customer data. Customers should be at the core of every business, and therefore, all teams should have access to customer data to better understand how their department can contribute to improving the customer experience.
It makes a lot of sense that product teams live in the customer success platform. Understanding customer behavior, which products are getting good adoption, and even identifying groups of users to understand why they’re using a feature in a certain way is hugely beneficial for their product strategy. Product analytics gives them that.
Customer marketing is another great way to help with retention and expansion by putting on personalized events. Product analytics can provide a ton of personalization across a huge amount of data. As an example, you can find decision makers based in New York whose team are using product X a lot, but product Y, not so much. You can invite them to an executive dinner in New York and include customers who are finding a lot of value using product Y.
Diminishing data returns
When data loses its context, it loses its value.
If you’re using data to create a predictive churn model, aiming to forecast customer behavior within the next three to six months, you have to also take into account the commercial context that can’t be found in customer data. When data loses its context, it can become worthless.
Further, inaccuracies in data erode trust over time and if influential people in your organization dismiss data, it becomes obsolete. However, there’s a common tendency to blame the system where data errors are spotted, such as a CRM platform. Make sure to trace the data back to its source to correctly solve data inaccuracies.
Remember, everyone in a data-driven organization plays a role in being both good data creators and good data recipients.
It can be overwhelming to know where to start, so simplify and focus on what’s most relevant to your objectives. Becoming data-driven isn’t about achieving instant success; it’s about using data to gain insights that help you reach your goals over time. Tying data to objectives and taking incremental steps towards improvement.
Being data-driven should be standard nowadays, but many organizations still struggle with it. Every company wants to be data-driven, but putting it into practice is the tough part.
In a Harvard Business Review article from 2018, it was noted that while 99% of companies surveyed intended to be data-driven, only 20% felt they had succeeded.
Becoming data-driven requires a cultural shift. It’s not just about putting data in presentations or on websites; it’s about educating everyone in the organization about why data is important and how it will be used.
Part of this cultural shift means agreeing as an organization to be data-driven, the recognition that each individual is a data creator, not just a recipient, and ensuring that everyone is creating and acting on good data. Simply having a data team isn’t enough; it’s about integrating data into your culture.
Using data across the organization
While every single function in the business plays a part in retaining and growing customers, it’s the core objective for modern CS teams. Part of that responsibility means quarterbacking the customer journey across all the other functions within the organization ensuring that customer data reaches the right departments.
CS should communicate insights gained from customer data to suggest initiatives like advocacy programs and referral pipelines that Marketing can spin up for the benefit of Sales. Over time, this can lead to a cultural shift where everyone recognizes the value of data and knows how to use it effectively across teams.
Three ways to think about product usage data
When people think about product usage data, they’re usually thinking about dips and troughs of usage. Product usage up means good, product usage down means bad. But at a higher level, you have to think about it in three ways.
The first is understanding if the product usage of the customer is aligned with the outcomes that they want to achieve. A customer can use a product in a million different ways, but that doesn’t matter if their goals aren’t aligned with the way that they’re using the product. That’s often an oversight.
The second is a simple one. Make sure your customers are using your product the way it was designed to be used. It’s often the case that a customer starts using your product in a way that it wasn’t designed for. To an extent, that may seem great, opening up a new use case. But 9 times out of ten, that customer will churn. Make sure your customers are using your products the way that they’re intended because all products start to get wobbly at the edges.
The third thing, and arguably most important, when it comes to fighting churn and product usage is time. What you’re trying to understand is how the current state of a data point compares to a prior state and use that relationship to understand what a future state might look like. Whether you’re using a health score and see the health going up or down, or whether it’s specific usage of a feature, it’s always the current state versus the prior state and how that’s going to influence the future state.
Product usage data in action
Common use cases:
Correlations between high customer retention rates and customers who’ve completed certifications. This drives adoption across their organization leading to retention as more people become familiar with the software and use it to solve more problems.
Driving expansion through intent. Expansion where a customer using one feature a lot could mean they could find value in another complementary product.
Tactical expansion around thresholds. If a customer hits 80% of a consumption threshold or 80% of a licensing threshold, an auto email can be sent to the decision maker.
Using product analytics to white space. Break out all of the products or features within your product l, and whitespace across your portfolio to find products that have high usage across a certain cohort of customers and find opportunities with similar customers.
Last point can also be applied to better understand which cohort of customers may not be adopting a certain product and may need additional enablement.
Better collaboration between teams
You can really look at any function and say there’s a clear way of collaborating around customer data. Customers should be at the core of every business, and therefore, all teams should have access to customer data to better understand how their department can contribute to improving the customer experience.
It makes a lot of sense that product teams live in the customer success platform. Understanding customer behavior, which products are getting good adoption, and even identifying groups of users to understand why they’re using a feature in a certain way is hugely beneficial for their product strategy. Product analytics gives them that.
Customer marketing is another great way to help with retention and expansion by putting on personalized events. Product analytics can provide a ton of personalization across a huge amount of data. As an example, you can find decision makers based in New York whose team are using product X a lot, but product Y, not so much. You can invite them to an executive dinner in New York and include customers who are finding a lot of value using product Y.
Diminishing data returns
When data loses its context, it loses its value.
If you’re using data to create a predictive churn model, aiming to forecast customer behavior within the next three to six months, you have to also take into account the commercial context that can’t be found in customer data. When data loses its context, it can become worthless.
Further, inaccuracies in data erode trust over time and if influential people in your organization dismiss data, it becomes obsolete. However, there’s a common tendency to blame the system where data errors are spotted, such as a CRM platform. Make sure to trace the data back to its source to correctly solve data inaccuracies.
Remember, everyone in a data-driven organization plays a role in being both good data creators and good data recipients.
It can be overwhelming to know where to start, so simplify and focus on what’s most relevant to your objectives. Becoming data-driven isn’t about achieving instant success; it’s about using data to gain insights that help you reach your goals over time. Tying data to objectives and taking incremental steps towards improvement.
Being data-driven should be standard nowadays, but many organizations still struggle with it. Every company wants to be data-driven, but putting it into practice is the tough part.
In a Harvard Business Review article from 2018, it was noted that while 99% of companies surveyed intended to be data-driven, only 20% felt they had succeeded.
Becoming data-driven requires a cultural shift. It’s not just about putting data in presentations or on websites; it’s about educating everyone in the organization about why data is important and how it will be used.
Part of this cultural shift means agreeing as an organization to be data-driven, the recognition that each individual is a data creator, not just a recipient, and ensuring that everyone is creating and acting on good data. Simply having a data team isn’t enough; it’s about integrating data into your culture.
Using data across the organization
While every single function in the business plays a part in retaining and growing customers, it’s the core objective for modern CS teams. Part of that responsibility means quarterbacking the customer journey across all the other functions within the organization ensuring that customer data reaches the right departments.
CS should communicate insights gained from customer data to suggest initiatives like advocacy programs and referral pipelines that Marketing can spin up for the benefit of Sales. Over time, this can lead to a cultural shift where everyone recognizes the value of data and knows how to use it effectively across teams.
Three ways to think about product usage data
When people think about product usage data, they’re usually thinking about dips and troughs of usage. Product usage up means good, product usage down means bad. But at a higher level, you have to think about it in three ways.
The first is understanding if the product usage of the customer is aligned with the outcomes that they want to achieve. A customer can use a product in a million different ways, but that doesn’t matter if their goals aren’t aligned with the way that they’re using the product. That’s often an oversight.
The second is a simple one. Make sure your customers are using your product the way it was designed to be used. It’s often the case that a customer starts using your product in a way that it wasn’t designed for. To an extent, that may seem great, opening up a new use case. But 9 times out of ten, that customer will churn. Make sure your customers are using your products the way that they’re intended because all products start to get wobbly at the edges.
The third thing, and arguably most important, when it comes to fighting churn and product usage is time. What you’re trying to understand is how the current state of a data point compares to a prior state and use that relationship to understand what a future state might look like. Whether you’re using a health score and see the health going up or down, or whether it’s specific usage of a feature, it’s always the current state versus the prior state and how that’s going to influence the future state.
Product usage data in action
Common use cases:
Correlations between high customer retention rates and customers who’ve completed certifications. This drives adoption across their organization leading to retention as more people become familiar with the software and use it to solve more problems.
Driving expansion through intent. Expansion where a customer using one feature a lot could mean they could find value in another complementary product.
Tactical expansion around thresholds. If a customer hits 80% of a consumption threshold or 80% of a licensing threshold, an auto email can be sent to the decision maker.
Using product analytics to white space. Break out all of the products or features within your product l, and whitespace across your portfolio to find products that have high usage across a certain cohort of customers and find opportunities with similar customers.
Last point can also be applied to better understand which cohort of customers may not be adopting a certain product and may need additional enablement.
Better collaboration between teams
You can really look at any function and say there’s a clear way of collaborating around customer data. Customers should be at the core of every business, and therefore, all teams should have access to customer data to better understand how their department can contribute to improving the customer experience.
It makes a lot of sense that product teams live in the customer success platform. Understanding customer behavior, which products are getting good adoption, and even identifying groups of users to understand why they’re using a feature in a certain way is hugely beneficial for their product strategy. Product analytics gives them that.
Customer marketing is another great way to help with retention and expansion by putting on personalized events. Product analytics can provide a ton of personalization across a huge amount of data. As an example, you can find decision makers based in New York whose team are using product X a lot, but product Y, not so much. You can invite them to an executive dinner in New York and include customers who are finding a lot of value using product Y.
Diminishing data returns
When data loses its context, it loses its value.
If you’re using data to create a predictive churn model, aiming to forecast customer behavior within the next three to six months, you have to also take into account the commercial context that can’t be found in customer data. When data loses its context, it can become worthless.
Further, inaccuracies in data erode trust over time and if influential people in your organization dismiss data, it becomes obsolete. However, there’s a common tendency to blame the system where data errors are spotted, such as a CRM platform. Make sure to trace the data back to its source to correctly solve data inaccuracies.
Remember, everyone in a data-driven organization plays a role in being both good data creators and good data recipients.
It can be overwhelming to know where to start, so simplify and focus on what’s most relevant to your objectives. Becoming data-driven isn’t about achieving instant success; it’s about using data to gain insights that help you reach your goals over time. Tying data to objectives and taking incremental steps towards improvement.
Ambera Cruz
•
Editor in Chief, Planhat
Amberā Pualani Cruz has spent the last 12 years building and scaling high-performing marketing teams across the globe. Today she leads the marketing team at Planhat, driving demand and helping customers unlock sustainable growth through their successful customers.
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© 2024 Planhat AB
Thought-leading customer-centric content, direct to your inbox every month.
By submitting this form I agree that Planhat may collect, process and retain my data pursuant to its Privacy Policy.
Customers
© 2024 Planhat AB
Thought-leading customer-centric content, direct to your inbox every month.
By submitting this form I agree that Planhat may collect, process and retain my data pursuant to its Privacy Policy.
Customers
© 2024 Planhat AB
Thought-leading customer-centric content, direct to your inbox every month.
By submitting this form I agree that Planhat may collect, process and retain my data pursuant to its Privacy Policy.
Customers
© 2024 Planhat AB