What’s the Difference Between Traditional and Predictive Behavior Scoring?

This is a question we get asked a lot. 

Instead of manually adding points for a given action, a predictive behavioral score mines the full spectrum of activity-data being collected by your marketing automation platform including every email click, website visit, and social engagement. Machine learning is used to weigh each signal appropriately in order to predict the likelihood of an outcome (e.g. conversion) within a set time period (e.g. next three weeks).

Learn the difference betweem tradition and predictive behavior lead scoring
To help explain these concepts in more detail we’ve put together a new eBook. Inside you’ll learn:

  • The six most common use cases for behavior scoring
  • Best practices for operationalizing your fit and behavior scoring
  • Why predictive behavioral models are more powerful than the scoring you’ll find in traditional marketing automation platforms

Download your copy here >

 

How to Analyze Predictive Models for B2B Sales & Marketing

As companies embark on the predictive journey, the first question most ask is “how can I tell if my model is really working as it should?” In order for your sales reps to trust predictive scores and invest the proper amount of time into the leads you send them, it’s critical to demonstrate the accuracy, efficacy and performance of your model.

Over the years we’ve had the opportunity to work with amazing companies like Tableau, Concur and Box that are part of the movement shaping the future of predictive for sales and marketing. This playbook highlights best practices members of the Infer community have used to evaluate their models.

beginners guide to predictive models for b2b

Inside you’ll learn how to:

  • Understand and compare your conversion rates across lead buckets
  • Calculate multipliers to see how much better buckets perform vs. average
  • Use this simple worksheet to analyze your company’s predictive model

Download your copy here >

This is a predictive playbook we often recommend when companies are just embarking on their predictive journey and want to easily understand if their model is performing as it should. We also have other playbooks for sales prioritization, filtering, net-new leads, nurture, executive dashboards, and campaigns. If you’d like to learn more, contact us and we’d be happy to connect.

Predictive Playbook: Sales Prioritization

Many B2B companies are interested in predictive but need help building a business case. While there are many applications for scoring, sales prioritization is one of the most common. It is easy to set up the ROI story upfront and measure the impact over the first 60 days.

Over the years we’ve had the opportunity to work with amazing companies including Tableau, Optimizely, and Zendesk. This playbook highlights how they’ve used predictive to drive more pipeline with less effort.

Inside you’ll learn how to:

  • Articulate the business challenge
  • Quantify the amount of wasted energy
  • Project the revenue impact of predictive scoring
  • Document your success story

Access your free copy here > 

predictive playbook for sales and marketing - prioritization

How to Calculate the ROI of Predictive Lead Scoring

With content marketing, freemium products, and list-buys, marketers are generating more leads than ever before — but only a fraction of them are good prospects. Predictive scoring solutions like Infer help filter out the noise by programmatically researching every lead and identifying high-potential MQLs. That not only saves sales reps time, but just as important, it gives you an objective way to measure lead quality.

We’re continuously documenting best practices in order to provide a framework for measuring the ROI of a predictive scoring initiative. One approach that lends a lot of clarity to this process is to look at three simple metrics — your number of sales reps, your average cost per rep, and your percentage of bad leads. With this information, you can quantify the cost of wasting effort on bad leads.

ROI_01However, cost savings is only one way to measure the impact of predictive scoring. Most companies want to quantify the top-line impact as well. By looking at your average leads per month, conversation rate and revenue per opportunity, you can understand the potential revenue increase you’re likely to see from predictive scoring.