Free ebook: What Is Predictive Lead Scoring?

Guide to Predictive Lead Scoring -Simple explanation of how it worksPath that many companies take -Use cases and ROI models -How to get buy-in at your company Above is a preview of what’s inside. Read it online or print it out. We’ve also included a set of visuals that you can leverage in your PowerPoint presentations.

First 3 Pages

What Is Predictive Lead Scoring? Predictive lead scoring is an automated, data-driven way for businesses to determine which prospects are most likely to convert, and which are going to have the biggest revenue impact. It’s always-on optimization. Scores can be used to filter out bad leads, prioritize follow up efforts, measure marketing effectiveness, and extract more value out of your nurture database. What Is Predictive Lead Scoring? Marketing automation systems like Eloqua, Marketo, and Pardot support lead scoring. Infact, most Infer cus-tomers are already using one of those three applications. So how is Infer’s Predictive Lead Scoring different? Predictive Lead Scoring #1 External Signals #2 Deep Data Science Because predictive lead scoring taps into thousands of external signals, the lead can be scored the instant it is created. This allows you to route it down the right path and get a jump on your competition. Also, it means that you can re-score leads even if they aren’t active on your website, which enables you to light up the best leads in your nurture database.   #3 Automated Execution With traditional lead scoring, like that found in Marketing Automation systems, you manually define point values. Obviously this breaks down when you’re talking about hundreds or thousands of attributes. With the Infer approach, nothing is done manually. We use the most advanced predictive intelligence and ma-chine learning algorithms available. Therefore, you can have confidence your lead scoring is statistically proven to be accurate. #4 Automated Optimization Infer is able to automatically adjust to new signals and changing business dynamics. The model retrains itself learning from new closed deals improving it’s accuracy. This is helpful in changing business environments. For example, you might introduce new products, change your pricing, or see new competitive pressure. Infer is With nothing more than an email address, Infer can go out and grab thousands of signals about the individual and the organization they work for. Things like relevant job postings, employee count, patent filings, social presence, website traffic, and even the technology vendors they use. Below is one simple way of thinking about the impact of predictive lead scoring. When we first build a model and present the results, it is very common to see that the top 30% of leads account for the vast majority of the pipeline (green bars) and the bottom 70% of leads have very little pipeline impact.Infer also provides advanced analytics to measure sales effort. This is tracked by looking at the phone calls being logged and emails being sent out by sales reps. While most companies are using Marketing Automation, they’re still spending lots of cycles on leads that aren’t going to convert. That makes sense. If you don’t have confidence in your scoring, the safe bet is to leave no stone unturned. Aligning Effort with Impact constantly monitoring the data and proactively recommending ways to increase revenue.