Evaluating Marketing Automation Software? See Why You Might Consider Predictive First

Some interesting research came out recently from softwareadvice.com analyzing what drives organizations to purchase marketing automation systems. Their key findings included:

  • Improved lead management was the #1 reason for evaluating marketing automation
  • Lead nurturing topped the list of desired capabilities
  • 91% of buyers were evaluating marketing automation for the first time
  • 48% were currently managing marketing activities in their CRM application

Top 20 Sales and Marketing Thought Leaders Who Are Influencing Predictive Scoring


As we’ve been building Infer’s predictive lead scoring engine over the past three years, we’ve also been following many brilliant thought leaders who are contributing to a range of discussions near and dear to our hearts. So in the spirit of Valentine’s Day, we thought it’d be fun to survey our team on their favorite influencers in the space, and send some love their way. We’ve compiled part of that list below — spanning experts in the realms of CRM, marketing technology and general marketing and sales best practices. Our list of top data science and predictive analytics experts is published here.

Three Ways Infer Increases Rep Productivity

Every company has good sales reps and not-so-good sales reps. While much about sales success relates to a rep’s personality, network, or other subjective attributes, some smart companies are starting to figure out ways they can more objectively evaluate and increase their reps’ success rates. To provide a quick illustration of how we’ve seen Infer create lift (increased win rates and conversion) for our customers, let’s look at some sample Salesforce dashboards. Below we’ll illustrate three ways predictive intelligence can help increase sales rep productivity.

1) Send them your hottest leads 

The first layer of benefit comes from knowing which leads to route to sales and which should be kept in a nurture program until they heat up. Infer uses machine learning to accurately predict which leads will convert. In this example, 97.3% of the revenue came from the top 3 tiers of hot leads Infer identified. Depending on your distribution of tiers, that means you could potentially reduce the inside sales team’s workload by 70% and still capture the majority of the revenue opportunities.

Introducing Infer: Partying on Business Data

After nearly three years of development, today, my team and I are extremely excited to finally launch our company Infer. Our goal is to help companies significantly win more customers by providing applications inspired by the deep data science and simplicity of the consumer web.

Why we’re doing this

My co-founders and I had the great opportunity to work on large scale data products at consumer web companies like Google, Microsoft, and Yahoo! There we witnessed first hand the impact cutting-edge data science and systems infrastructure have on making properties like Google Web Search so great and relevant. The rigor that takes place behind the scenes here is truly unbelievable.

However, when you compare the scientific approach that underpins popular consumer facing properties to how companies internally leverage their own data for important business decision making, it’s astoundingly poor in comparison.