Signal to Noise: How Jamie Grenney and Infer are Uncovering Your Best Leads
Earlier this month, I chatted with Jamie Grenney, VP of Marketing at Infer about the state of marketing automation ahead of Marketoās Markating Nation Summit. Jamie is uniquely positioned to talk about the sales & marketing funnel. As a long-time Salesforce veteran, Jamieās seen the CRM industry change significantly over the past 10 years. Here were our takeaways.
On Salesforce, Infer and the Evolution of Sales & Marketing
When I joined Salesforce in 2002, the question was, āwhy isnāt enterprise software as easy to use as Amazon.com.ā That simple idea gave rise to a billion dollar business.Ā The question we should be asking today is āWhy canāt every business operate with the same data driven intelligence as a Google or Amazon.com.āĀ
The truth is most companies canāt do this internally because data science is hard. This is where Infer comes in. There are thousands of signalsāinternal and externalāthat help companies distinguish leads. Inferās machine learning algorithm helps you sift through these signals to help you see which leads are a good fit. With so many inbound leads coming into the funnel from social and online, being able to effectively score these leads is going to becoming increasingly important.
Thereās an arms race for companies to not just fill their funnel with leads, but good leads. Our customers love us because weāre focused on only one thing: helping them identify the best leads.
On Data Signals and Data Science
Most companies are actually data-poor. The reason is intuitive; most web forms have been simplified to increase conversion, which means you may have only the leadās name, contact, title, company and its source. In the past, this meant a salesperson would have to manually look up the lead online and fill in custom fields on Salesforce.
With Infer, this manual step is not only eliminated, itās optimized. Infer collects thousands of data signals already from the web. We can tell you how credible the leadās domain is, its hiring patterns, ad spend or how innovative it is based on patent or trademark filings. Our algorithm determines which of theses signals is a better fit with your companyās product and weighs it accordingly.
On Fit vs. Activity Based Scoring
Most companies use activity based scoring to determine the strength of a leadāif a lead opened an email or clicked on a call-to-action button. They do it because itās easy to āacquireā these leads. There are a lot of problems with activity based scoring though. For example, some leads can be stuck in a funnel because they havenāt triggered the right activities to move them down. More importantly, activity based scoring doesnāt take into account buyer persona.
Some leads are a poor fit for your company and product. They could be in the wrong space, wrong size or may not have the authority. Infer can tell you which of your leads are actually fit for your product. This is your explicit lead score whereas activity is more implicit. The fit leads are more likely to convert, which means better sales efficiency and more accurate forecasting.
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