What is predictive intelligence?

What is predictive intelligence?

Predictive intelligence uses data to create unique customer experiences based on specific behavior and preferences.

Tooba Durraze
Tooba Durraze
Apple Podcast LinkGoogle Podcast LinkSpotify Podcast Link
Apple Podcast LinkGoogle Podcast LinkSpotify Podcast Link


It’s well known the B2B sales field is not immune to the trends around Artificial Intelligence (AI). The primary use cases involve using AI to translate vast amounts of data into information, and ultimately insights in order to develop effective sales strategies and tactics. But that is just one segment of use. 

Another emerging segment is around predictive intelligence — which is typically considered a black box. Predictive Intelligence sounds a lot like science fiction, but in actuality, it’s a straightforward premise. Learn more about what predictive intelligence is and how you can use it.

What is Predictive Intelligence?

Predictive intelligence uses data to create unique customer experiences based on specific behavior and preferences. It is basically about anticipating what needs to happen, with whom, and when, to get the best possible result in a sales cycle — and using AI to help get those insights.

How is Predictive Intelligence Changing B2B Marketing?

Predictive Intelligence is especially helpful in high-growth companies where there are thousands of potential customers, all in various stages of the sales cycle.

Using historical data, it allows you to identify customers who are at the point of purchase, allowing sales teams to run more efficient and focused sales cycles with the highest yield.

Additionally, predictive intelligence is an extremely helpful tool for marketers to be able to create personalized interactions with their customers. According to this Salesforce Marketing Intelligence Report, a data-driven approach to marketing continues to be top-of-mind for most marketers with technologies like AI (58%) and marketing analytics platforms (57%) leading the charge.

How to Get Clean Predictive Intelligence Data

Like all AI applications, predictive intelligence is only as good as the data that goes into it. While the data points may vary with each company or use case, they can broadly be bucketed into three different categories:

  1. Ideal Customer Profile: the first part in predictive intelligence is the ability to focus on the top of the sales funnel. This is done by having a set of criteria to define what the Ideal Customer Profile (ICP) is for the business. A multitude of factors can go into defining this, including tech stack, department with business needs, location, etc. The factors mostly range around the demographic, firmographic, and technographic dimensions. The key to accurate predictive intelligence is getting this piece right. The rest of the modeling tends to fall flat if the ICP is not correct. 
  1. Activity Based Intent Data: the second, and arguably the most directly applied part of predictiv intelligence, lies in activity based intent data. Predominantly, this data consists of website activity and behavior including data points such as bot conversations, browsing history, and time on site. Additionally, it may also include behavioral data related to outbound efforts, such as marketing emails (i.e. click rates or open rates). 
  1. Sales Opportunity Data: both of the above mentioned buckets of data would not be useful if the potential customer was not in the right stage of the sales cycle. Hence, predictive intelligence data can be applied differently based on which stage of the sales cycle the customer might be in. For potential customers where the data points are trending positive, and are in an engaged Opportunity stage, the right call to action might be an email to book a meeting. For those trending negatively, and not in an engaged stage, the right call to action might be to keep them warm until they are in a buying state.

What is Qualified Signals?

Qualified Signals looks at these three sets of data points mentioned above, alongside many others, to give you a data based prediction into when the right time of action would be. We do so by showcasing an intent score, alongside the buying trend coupled with your Salesforce Opportunity data, so you can make the best determination for action. Additionally, we arm you with a series of out-of-the-box use cases, taking the guesswork out of how to use these various data points to your advantage.

Qualified signals uses predictive intelligence to help you:

  • Prioritize and customize sales outreach to get more high-quality leads
  • Accelerate deal cycles and expansion opportunities
  • Intelligently discover how to expand your total addressable market

Predictive Intelligence is no longer a thing of the future, it’s here right now! While closing deals will always remain at the heart of defining success in B2B sales, using predictive intelligence can help substantially optimize and expedite how we close business today. Help your sales team succeed by arming them with the data points they need to close deals efficiently and scale your business.

About Tooba

As the Director of Business Intelligence and Strategy at Qualified, Tooba is currently finishing her Algorithmic Data Science PhD from MIT. She specializes in looking at AI models and how they interact with both big and small data to gain insights.

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Edit this

What is predictive intelligence?

Predictive intelligence uses data to create unique customer experiences based on specific behavior and preferences.

Tooba Durraze
Tooba Durraze
What is predictive intelligence?
Apple Podcast LinkGoogle Podcast LinkSpotify Podcast Link
Apple Podcast LinkGoogle Podcast LinkSpotify Podcast Link


It’s well known the B2B sales field is not immune to the trends around Artificial Intelligence (AI). The primary use cases involve using AI to translate vast amounts of data into information, and ultimately insights in order to develop effective sales strategies and tactics. But that is just one segment of use. 

Another emerging segment is around predictive intelligence — which is typically considered a black box. Predictive Intelligence sounds a lot like science fiction, but in actuality, it’s a straightforward premise. Learn more about what predictive intelligence is and how you can use it.

What is Predictive Intelligence?

Predictive intelligence uses data to create unique customer experiences based on specific behavior and preferences. It is basically about anticipating what needs to happen, with whom, and when, to get the best possible result in a sales cycle — and using AI to help get those insights.

How is Predictive Intelligence Changing B2B Marketing?

Predictive Intelligence is especially helpful in high-growth companies where there are thousands of potential customers, all in various stages of the sales cycle.

Using historical data, it allows you to identify customers who are at the point of purchase, allowing sales teams to run more efficient and focused sales cycles with the highest yield.

Additionally, predictive intelligence is an extremely helpful tool for marketers to be able to create personalized interactions with their customers. According to this Salesforce Marketing Intelligence Report, a data-driven approach to marketing continues to be top-of-mind for most marketers with technologies like AI (58%) and marketing analytics platforms (57%) leading the charge.

How to Get Clean Predictive Intelligence Data

Like all AI applications, predictive intelligence is only as good as the data that goes into it. While the data points may vary with each company or use case, they can broadly be bucketed into three different categories:

  1. Ideal Customer Profile: the first part in predictive intelligence is the ability to focus on the top of the sales funnel. This is done by having a set of criteria to define what the Ideal Customer Profile (ICP) is for the business. A multitude of factors can go into defining this, including tech stack, department with business needs, location, etc. The factors mostly range around the demographic, firmographic, and technographic dimensions. The key to accurate predictive intelligence is getting this piece right. The rest of the modeling tends to fall flat if the ICP is not correct. 
  1. Activity Based Intent Data: the second, and arguably the most directly applied part of predictiv intelligence, lies in activity based intent data. Predominantly, this data consists of website activity and behavior including data points such as bot conversations, browsing history, and time on site. Additionally, it may also include behavioral data related to outbound efforts, such as marketing emails (i.e. click rates or open rates). 
  1. Sales Opportunity Data: both of the above mentioned buckets of data would not be useful if the potential customer was not in the right stage of the sales cycle. Hence, predictive intelligence data can be applied differently based on which stage of the sales cycle the customer might be in. For potential customers where the data points are trending positive, and are in an engaged Opportunity stage, the right call to action might be an email to book a meeting. For those trending negatively, and not in an engaged stage, the right call to action might be to keep them warm until they are in a buying state.

What is Qualified Signals?

Qualified Signals looks at these three sets of data points mentioned above, alongside many others, to give you a data based prediction into when the right time of action would be. We do so by showcasing an intent score, alongside the buying trend coupled with your Salesforce Opportunity data, so you can make the best determination for action. Additionally, we arm you with a series of out-of-the-box use cases, taking the guesswork out of how to use these various data points to your advantage.

Qualified signals uses predictive intelligence to help you:

  • Prioritize and customize sales outreach to get more high-quality leads
  • Accelerate deal cycles and expansion opportunities
  • Intelligently discover how to expand your total addressable market

Predictive Intelligence is no longer a thing of the future, it’s here right now! While closing deals will always remain at the heart of defining success in B2B sales, using predictive intelligence can help substantially optimize and expedite how we close business today. Help your sales team succeed by arming them with the data points they need to close deals efficiently and scale your business.

About Tooba

As the Director of Business Intelligence and Strategy at Qualified, Tooba is currently finishing her Algorithmic Data Science PhD from MIT. She specializes in looking at AI models and how they interact with both big and small data to gain insights.

Explore the Qualified+ Library
Category

Stay up to date with weekly drops of fresh B2B marketing and sales content.

Edit this

What is predictive intelligence?

Predictive intelligence uses data to create unique customer experiences based on specific behavior and preferences.

What is predictive intelligence?
Tooba Durraze
Tooba Durraze
|
January 6, 2022
|
X
min read
Apple Podcast LinkGoogle Podcast LinkSpotify Podcast Link
Apple Podcast LinkGoogle Podcast LinkSpotify Podcast Link


It’s well known the B2B sales field is not immune to the trends around Artificial Intelligence (AI). The primary use cases involve using AI to translate vast amounts of data into information, and ultimately insights in order to develop effective sales strategies and tactics. But that is just one segment of use. 

Another emerging segment is around predictive intelligence — which is typically considered a black box. Predictive Intelligence sounds a lot like science fiction, but in actuality, it’s a straightforward premise. Learn more about what predictive intelligence is and how you can use it.

What is Predictive Intelligence?

Predictive intelligence uses data to create unique customer experiences based on specific behavior and preferences. It is basically about anticipating what needs to happen, with whom, and when, to get the best possible result in a sales cycle — and using AI to help get those insights.

How is Predictive Intelligence Changing B2B Marketing?

Predictive Intelligence is especially helpful in high-growth companies where there are thousands of potential customers, all in various stages of the sales cycle.

Using historical data, it allows you to identify customers who are at the point of purchase, allowing sales teams to run more efficient and focused sales cycles with the highest yield.

Additionally, predictive intelligence is an extremely helpful tool for marketers to be able to create personalized interactions with their customers. According to this Salesforce Marketing Intelligence Report, a data-driven approach to marketing continues to be top-of-mind for most marketers with technologies like AI (58%) and marketing analytics platforms (57%) leading the charge.

How to Get Clean Predictive Intelligence Data

Like all AI applications, predictive intelligence is only as good as the data that goes into it. While the data points may vary with each company or use case, they can broadly be bucketed into three different categories:

  1. Ideal Customer Profile: the first part in predictive intelligence is the ability to focus on the top of the sales funnel. This is done by having a set of criteria to define what the Ideal Customer Profile (ICP) is for the business. A multitude of factors can go into defining this, including tech stack, department with business needs, location, etc. The factors mostly range around the demographic, firmographic, and technographic dimensions. The key to accurate predictive intelligence is getting this piece right. The rest of the modeling tends to fall flat if the ICP is not correct. 
  1. Activity Based Intent Data: the second, and arguably the most directly applied part of predictiv intelligence, lies in activity based intent data. Predominantly, this data consists of website activity and behavior including data points such as bot conversations, browsing history, and time on site. Additionally, it may also include behavioral data related to outbound efforts, such as marketing emails (i.e. click rates or open rates). 
  1. Sales Opportunity Data: both of the above mentioned buckets of data would not be useful if the potential customer was not in the right stage of the sales cycle. Hence, predictive intelligence data can be applied differently based on which stage of the sales cycle the customer might be in. For potential customers where the data points are trending positive, and are in an engaged Opportunity stage, the right call to action might be an email to book a meeting. For those trending negatively, and not in an engaged stage, the right call to action might be to keep them warm until they are in a buying state.

What is Qualified Signals?

Qualified Signals looks at these three sets of data points mentioned above, alongside many others, to give you a data based prediction into when the right time of action would be. We do so by showcasing an intent score, alongside the buying trend coupled with your Salesforce Opportunity data, so you can make the best determination for action. Additionally, we arm you with a series of out-of-the-box use cases, taking the guesswork out of how to use these various data points to your advantage.

Qualified signals uses predictive intelligence to help you:

  • Prioritize and customize sales outreach to get more high-quality leads
  • Accelerate deal cycles and expansion opportunities
  • Intelligently discover how to expand your total addressable market

Predictive Intelligence is no longer a thing of the future, it’s here right now! While closing deals will always remain at the heart of defining success in B2B sales, using predictive intelligence can help substantially optimize and expedite how we close business today. Help your sales team succeed by arming them with the data points they need to close deals efficiently and scale your business.

About Tooba

As the Director of Business Intelligence and Strategy at Qualified, Tooba is currently finishing her Algorithmic Data Science PhD from MIT. She specializes in looking at AI models and how they interact with both big and small data to gain insights.

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