- 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
The comparison below highlights why Infer’s Predictive Lead Scoring is a home run initiative. It’s low risk, easy to implement, and the impact is quantifiable upfront.
Time to Value for Automation Projects
With CRM or a Marketing Automation System, this is what a typical implementation looks like:
In tandem with our recognition of the many experts contributing to today’s discussions around CRM, marketing technology and general marketing and sales best practices, we’ve compiled the following list of influencers in the world of data. We always enjoy hearing what these brilliant data science and predictive analytics thought leaders have to say.
- Keith Bigelow, Salesforce SVP – Keith has a wealth of knowledge to contribute from his many years in the business intelligence space, so it’s no surprise that Salesforce has tapped him to lead its big data and analytics group.
- Allen Bonde, Digital Clarity Group – And early proponent of data-driven marketing, Allen is a data scientist turned CMO turned analyst. He now works with clients to gain better market insight through what he calls “Small Data,” i.e. timely, meaningful insights derived from big data.
- Jeff Hammerbacher, Cloudera founder & Chief Scientist – After building the first formal data science team at Facebook, Jeff is now a powerful advocate for bringing consumer Web data innovations like Hadoop into mainstream businesses.
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.
Since we launched Infer, we’ve often been asked whether we’d reveal the awesome signals that go into our predictive lead scoring engine. Over these last three years, we’ve honed our core models and been relentless about finding new sources of data – striking partnerships, crawling the web, and amassing a library with thousands of unique signals. We’ve done all of this primarily to further improve the accuracy of our scoring. However, we recognize that these signals can be applied to lots of other sales and marketing use cases, so today we’re happy to announce the availability of our Infer Smart Signals™ service.
Infer Smart Signals give you a richer, fuller view of your prospects with details from our huge library of signals. You can add more insight into your leads, contacts, or accounts by pulling information that we’ve discovered to be highly predictive – like company size, technology vendors, web traffic, business model, industry, spam detection, etc. – right into your CRM or marketing automation systems.
To maintain their amazing growth trajectory and stay one step ahead of the competition, AdRoll has instilled a culture of data driven decision making. Infer helps them scan thousands of signals from across the web so that they can identify which customers are most likely to convert. At AdRoll they have one set of leads that convert at 4x the baseline, and one set that will never convert. Infer helps them tell the difference between the two. As their Sales Reps think about who to call out to, Infer ensures they’re focused where they’ve got the best shot at winning.
The movie “Glengarry Glen Ross” is famous for its desperate salespeople who fight over access to the premium Glengarry leads, knowing they’ll make the difference between success and failure. “These are the Glengarry leads. And to you they’re gold, and you don’t get them,” Alec Baldwin’s character taunts at one point. This scene may be a work of fiction, but a similar scenario plays out daily inside sales organizations around the world, although with far less transparency.
Leads don’t come in from marketing departments with handy ratings or categorizations that say, this lead has a 75 percent likelihood of turning into a sale, but this lead has only a 15 percent chance… Continue Reading
We’ve got a brand new demo to showcase how companies are using Infer’s Predictive Lead Scoring inside of Salesforce. See how it works, how easy it is to setup, and why it’s a game changer for sales and marketing. For more videos visit or Infer’s Demo Center.
This post originally appeared on the Salesforce Blog
While some companies aggressively pursue every lead that is created, others are leveraging lead scoring to work smarter. If you can programmatically spot your good leads, chances are you’ll be able to increase win rates and conversion.
So what makes a good lead?
A good lead has two key ingredients
Fit Score (also referred to as an explicit score)
Intended to capture how much an incoming prospect resembles a likely buyer. For example you might look at the lead’s company size, geographic location, industry, and job title, to determine if it is a fit. A quick look at its employer’s website might give you other clues regarding their business model or online presence.
Activity Score (also referred to as an implicit score)
Intended to capture how much a prospect is engaged with your company. This could include the lead’s website visits, form completes, email clicks, and maybe even application usage data.