What Are Predictive Analytics?

4 minutes
May 26, 2022
Data ManagementPlanning & ForecastingPredictive Analytics

By the nature of their jobs, marketers need to be very good educated guessers. They need to have a sense — a sixth sense, if you will — for knowing...

By the nature of their jobs, marketers need to be very good educated guessers. They need to have a sense — a sixth sense, if you will — for knowing which accounts to target, where accounts are in the buyers’ journey, what kind of messaging will resonate, and so on.

With experience and knowledge, marketers often become skilled at making educated guesses, but the problem is that they’re still guesses. 

Enter predictive analytics: a way to extract information from data to predict trends and buyer behavior patterns. 

Predictive insights are generated through a complex combination of data-modeling, machine learning, data-mining and AI. With the right data, it can accurately anticipate new opportunities, potential upsells, and even detect customer churn.

Let’s take a closer look at the three key pieces of data that feed into 6sense’s predictive models.

Intent Data

Predictive analytics collects and looks at two types of intent data: first-party and third-party.

First-party intent data comes from your organization’s own properties, and includes things like website visits, video views, engagement with content, and CRM records. This is data that you also have access to, which makes it easy to use.

Third-party intent data comes from anonymous user behavior occuring all over the internet. This includes visits to websites outside your organization’s domain, as well as branded and generic keyword searches. 

According to Forrester, buyers of B2B solutions conduct 70% of their research in online resources that are considered third-party data sources. Not only are they mostly educating themselves beyond where your marketing and sales reps can reach them, but they’re also making many of their key buying decisions before they even engage with a company.

Having a technology platform that can detect these hidden buyer intent signals, assign them to an account, and feed the data into a predictive model can help marketing and sales uncover potential opportunities and proactively reach out before a buying decision is made.

Historical Data

The second important piece critical to generating predictive insights is historical data. Using machine learning and AI, platforms  (like 6sense) analyzes massive amounts of data to look for indications of patterns in buyer behavior. 

Predictive models look at which behaviors led up to sales in the past — like content engagement, interactions with reps, or webpages eventual buyers showed interest in. Then the algorithms determine which actions are indicative of an intent to buy, and match that data against opportunities currently in your pipeline.  

Ideal Customer Profile

The third and final critical component of predictive analytics is the firmographic and technographic data that makes up your organization’s ideal customer profile. 

We’ve covered ideal customer profiles in the past, but to summarize, your ICP is a set of features that your closed-won customers have in common, and describes what makes a company an ideal customer for your solution. An ICP includes elements such as:

  • Industry
  • Company size
  • Geographic location
  • Number of employees
  • Revenue
  • Technology stack
  • Previous experience with a competitor
  • And many other commonalities your customers may share 

By knowing what your customers have in common, your revenue team has an easier time identifying target accounts that will be an ideal fit for your solution and are more likely to result in a successful sale.

Putting It All Together

With all the pieces in place, this is how predictive analytics works: When intent data is layered on top of historical data, then filtered through your organization’s ideal customer profile, the result is a target list of accounts that show intent to buy and are a good fit for your solution.

Targeting accounts based on predictive insights reduces the time and resources revenue teams spend chasing down accounts that are either not ready to buy, not a great fit as a customer, or both. 

Most importantly, revenue teams that use predictive modeling to identify accounts can stop relying on educated guesswork. Instead, they can prioritize their efforts on reaching out to companies that they know will benefit from their solution, based on their ICP.

Predictions founded upon historical data shows they are ready to make a buying decision, and armed with intent data, revenue teams can reach them with messaging that resonates with what their wants and needs.