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How does predictive analytics help me? A role-based checklist for marketers and sales professionals

The growth and evolution of predictive analytics is changing the way go-to-market is executed. With the use of big data and machine learning, the ideal customer is now easy to find using the right tool.

June 27, 2021 | 5 minute read


Susan Glenn

Susan Glenn
Senior Content Marketing Manager, Demandbase

Predictive analytics is no secret, but its power continues to grow. Predictive analytics employs big data and machine learning to unearth ideal clients or customers. This allows your B2B teams to better understand where their prospects are in the sales cycle and helps you develop key relationships with those accounts.

What’s the future of predictive analytics?

In addition to continuing to ride the big data and machine learning wave, predictive analytics has been accelerated by the growth of e-commerce and the overall acceptance of cloud innovations. Since the cloud model is cost-effective and highly accessible, it has been one of the leading catalysts of predictive analytics all by itself.

The growth of predictive analytics may seem like it’s in the fast lane now; however, that speed is growing exponentially. It is estimated that the global predictive analytics market was about $7.2 billion (USD) in 2020 and will grow to over $21 billion (USD) in 2025 or nearly a 300 percent increase in five years.

How is predictive analytics used by Marketing, Sales, and Customer Growth teams?

Predictive analytics has the power to provide insight into a myriad of industries, especially B2B. Some of the more common uses of predictive analytics include:

Customer forecasting

Every company would love to understand and anticipate customer movements. Predictive analytics helps you do that. You can now get customer retention data to understand how to increase customer lifetime value with predictive analytics.

Marketing trend insights

Businesses are always looking for new markets and trends within those markets, and now they can turn to predictive analytics. Plus, it benchmarks the health of each account and helps to create more accurate account scores.

Sales cycle turbo boost

Sales teams rely on predictive analytics because it can help find the best prospects and close more deals faster. Sales teams can also use it to rank top accounts and evaluate risks in their pipeline.

Event engagement

Predictive analytics can power the attendance of key virtual and field events. By showing which accounts would be most interested, predictive analytics can be used to increase registration through targeted invitation.

Digital conversions

By personalizing content to clients, businesses can improve conversions. Predictive analytics allows revenue teams to better target campaigns, create efficient ad spends and gain traction through customization.

Account planning

For businesses that have ever wanted to improve account planning, predictive analytics is the right solution. It allows teams to develop heat maps while enhancing Marketing, Sales, and Customer Growth team alignment.

Persona engagement

With predictive analytics, it’s possible to discover key personas and tailor messages based on those very personas.

Improved enablement

Predictive analytics allows teams to enhance outcomes by garnering better feedback and identifying campaign measurements.

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Predictive analytics solutions for your business

The uses mentioned above are no stranger to Demandbase.

We’ve looked into these key areas and have developed comprehensive machine learning-based solutions to help. Demandbase uses predictive analytics to help companies with Fit, Intent, Relationships, Engagement — or FIRE — to identify key accounts by scoring them using our proprietary models.

Check out our solutions that will give you an edge over your competitors.

Pipeline Predict

This model identifies which accounts will likely turn into an opportunity, allowing you to prioritize your Sales and Marketing efforts. Pipeline Predict also helps to control how your score is calculated, so the key metrics are integrated into the model to ensure you’re focused on the right accounts.

Qualification Score

We take account scoring very seriously. Our machine learning model scores accounts on how well they fit your ideal customer profile. Not only do our scores show which accounts may become opportunities, we often uncover accounts not on your radar.

Intent

Our system provides signals and behavioral data of your target audience that are used to discover accounts that are in-market. You can create Keyword Sets for your products, business units, personas, and competitors to train our AI model on key topics for your business.

Account Prioritization

Your team will be able to prioritize target accounts and personas based on intent and engagement. Our heatmaps allow you to see current engagement (persona, product, segment, etc.) and prioritize efforts based on those heatmaps. You can also sync custom scores with Salesforce.

Engagement Minutes

Use Engagement Minutes to track the time your target accounts are spending with you. These minutes cover not only when they respond to your marketing programs, but also when people interact socially, use your product, and talk with the Sales team. Leads and contacts earn minutes of engagement that are aggregated back to the account level.

In conclusion

The growth and evolution of predictive analytics is changing the way go-to-market is executed. With the use of big data and machine learning, the ideal customer is now easy to find using the right tool.

The B2B world needs this as much as B2C, and Demandbase can help transform your team.

Check out Demandbase’s predictive analytics solutions.


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Susan Glenn

Susan Glenn
Senior Content Marketing Manager, Demandbase