Top 10 Data Science Thought Leaders Who Are Influencing Predictive Scoring

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.

Paul Miller of Cloud Data produced a great 40 minute podcast with Vik Singh. Below we’ve featured three questions that really stood out. 

  • Tell me about Infer?
  • How is sales and marketing changing?
  • How is the industry as a whole shaping up?

Sales and marketing folks have been talking about lead scoring for years, so we often get asked “what’s different” about Infer’s way of doing things. One of the reasons Infer’s models perform so much better than traditional lead scoring is that our system pulls in several thousands of external signals, going well beyond what most organizations track in Salesforce.com or other CRM and marketing automation tools.

Signals

Broadly speaking, Infer gets these signals from three sources: crawling the web, purchasing data, and inferring signals from raw data sources. The last of these is the most subtle, and it’s our equivalent of Google’s secret sauce for web search.

A Sales SLA (Service Level Agreement) ensures that all of the leads marketing generates are followed up on by sales in a timely fashion. Most companies will create a tiered SLA based upon lead type — i.e. Contact Me Requests and Free Trials are top priority, while Webinar attendees and eBook downloaders may only warrant a single phone call.

Tarditional SLA

Cracking New MarketsThis is a question that often comes up in conversations with our prospects and customers. What happens when you want to push into a new market where you haven’t had a track record of success? Does predictive scoring offer any value? Or does it become a self-fulfilling prophecy that limits your potential?

The short answer is yes. Predictive Scoring can be extremely helpful in breaking into new markets. Here are some things to think about…

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.

Get a quick explanation of how Infer works, what goes into the model, and how it is used by sales and marketing.

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.