Dreamforce ’15

Arguably one of the hottest cloud events of the year is quickly approaching, and like many folks we’re gearing up for another exciting Dreamforce in San Francisco this month.

Where to find us? 

Infer #df15

Want to book some time to connect?

Mike Cabot – VP of Sales @ Infer – Mike heads up Sales at Infer where he joined over three years ago as the first employee (non-founder). Want to learn how Infer leverages predictive internally, hear some customer stories, or just talk shop? Book a time.

Jamie Grenney – VP of Marketing @ Infer – Jamie started as employee number 140 at Salesforce. After 11 years there he jumped to Infer and heads up the marketing team. Want to talk about our product roadmap or marketing strategy? Book a time.

Sean Zinsmeister – Product Marketing @ Infer – former customer and now Director of Product Marketing @ Infer – Interested in learning how Infer’s customers using predictive intelligence? Get an inside look at predictive playbooks with Sean. Book a time.

Andrew Angus – Demand Gen Marketing @ Infer – Hands on strategy to help generate more leads and tips on implementing predictive to get more out of our SDR team. Book a time.

Predictive Playbook: Sales Prioritization

Many B2B companies are interested in predictive but need help building a business case. While there are many applications for scoring, sales prioritization is one of the most common. It is easy to set up the ROI story upfront and measure the impact over the first 60 days.

Over the years we’ve had the opportunity to work with amazing companies including Tableau, Optimizely, and Zendesk. This playbook highlights how they’ve used predictive to drive more pipeline with less effort.

Inside you’ll learn how to:

  • Articulate the business challenge
  • Quantify the amount of wasted energy
  • Project the revenue impact of predictive scoring
  • Document your success story

Access your free copy here > 

predictive playbook for sales and marketing - prioritization

Welcome to the AI Spring: How Predictive Will Permeate the Software Industry

AI-Spring-1LinkedIn’s entry into predictive analytics has sparked an important conversation, both regarding the state of the emerging predictive industry and LinkedIn’s place in the enterprise software world. Given many of the company’s moves – most notably its Bizo and Fliptop acquisitions – it is becoming increasingly clear that LinkedIn intends to be much more than just an online “professional network.” There’s little doubt that it wants its place in the B2B sales and marketing software stack.

The question is, what does this mean for the other big players? LinkedIn’s latest announcement was very likely the first of many moves that we’ll see in the predictive market from folks like Salesforce, Microsoft, Oracle, Marketo, HubSpot and others. In fact, Marc Benioff recently spoke with Fortune Magazine about the ‘AI (artificial intelligence) spring’ saying, “When I look at the next set of technologies that we have to build in Salesforce, it’s all data-science-based technology. We don’t need more cloud. We don’t need more mobile. We don’t need more social. We need more data science.”

If you look at how the AI spring is likely to play out, there are a few logical approaches the big software players will take as they look to bring predictive capabilities into their product portfolios.

Adding Predictive Features
The first of these is to extend their existing apps with basic predictive functions – essentially playing it safe. These predictive features will probably be based on data the vendor already controls and should work with minimal customization. For example, rather than requiring manually assigned point values to arrive at basic lead scores, marketing automation vendors might enhance their lead scoring capabilities by using a handful of variables that are consistent across their customers to start calculating predictive scores.

How to Calculate the ROI of Predictive Lead Scoring

With content marketing, freemium products, and list-buys, marketers are generating more leads than ever before — but only a fraction of them are good prospects. Predictive scoring solutions like Infer help filter out the noise by programmatically researching every lead and identifying high-potential MQLs. That not only saves sales reps time, but just as important, it gives you an objective way to measure lead quality.

We’re continuously documenting best practices in order to provide a framework for measuring the ROI of a predictive scoring initiative. One approach that lends a lot of clarity to this process is to look at three simple metrics — your number of sales reps, your average cost per rep, and your percentage of bad leads. With this information, you can quantify the cost of wasting effort on bad leads.

ROI_01However, cost savings is only one way to measure the impact of predictive scoring. Most companies want to quantify the top-line impact as well. By looking at your average leads per month, conversation rate and revenue per opportunity, you can understand the potential revenue increase you’re likely to see from predictive scoring.

ROI_02To help you justify an investment in predictive lead scoring, we’re excited to introduce our new Predictive ROI Calculator. This new tool will allow you to plug in these few figures and instantly see the impact predictive scoring could have on your business.

ROI_03

Congrats to CRM Market Elite Winner

It’s great to see the smart folks over at Concur be recognized for their impressive results with our predictive lead scoring. CRM’s 2015 Market Elite Customer Company winners all demonstrate how technology can impact operating costs and efficiency, and Concur is no exception. Here’s an excerpt from the company’s profile in this month’s issue of CRM Magazine:

Infer’s predictive lead scoring helps Concur close more deals more quickly

The Infer solution helps Concur identify and prioritize the marketing-qualified leads that are most likely to convert to closed deals. It pulls in thousands of external signals, going well beyond what most organizations track in their basic CRM and marketing automation tools.

Greg Forrest - Concur“We’re finding leads a lot quicker and getting them into the pipeline a lot faster,” Forrest states proudly, “and we’re closing at a much higher rate.”

Based on the results Concur has seen in the first six months, the company is expanding the Infer solution to its cross-selling and upselling activities, account scoring, and direct marketing.

REAL RESULTS

  • Five thousand marketing-qualified leads were uncovered in its database, leading to a dramatic run-rate increase.
  • The number of leads converted to closed deals tripled.
  • Conversion rates increased by 150 percent, from 0.8 percent to 2 percent.
  • Closed deals for new solutions were boosted by 76 percent.

For more details, check out the magazine’s full story on Concur.

Infer Partners with Top Data Providers Like InsideView to Predict a Company’s Top Net-New Leads

Press Release: New Infer Solution Blends Powerful Market Intelligence with Advanced Predictive Analytics to Increase Conversion Rates for Cold Accounts

Infer Inc., a leading provider of predictive applications that help companies win more customers, today announced a new offering, which generates net new leads for sales and marketing teams. By partnering with top data providers such as InsideView, and using personalized predictive models to identify a company’s best-fit leads, Infer provides a list of the new accounts that are most likely to buy its products. Infer’s Net-New Leads solution saves businesses time and money by pushing only the very top pre-qualified contacts into their Salesforce databases.

Net-New Leads

“We’re excited to add Infer’s Net-New scoring to our predictive marketing capabilities,” said Scott Broomfield, CMO at Xactly. “Instead of purchasing tens of thousands of leads and manually slugging through them, Infer helps us programmatically evaluate list quality upfront and only pay for the best leads. Not only will this save us time and energy, it will also assure that our sales team can focus on the right prospects.”

Q&A with Bombora Co-Founder Mike Burton

There’s a lot of buzz in the marketing community around “intent data” — and our friends over at Bombora are at the forefront of this exciting new movement. As described in our recent eBook, external intent data is collected by networks of B2B publishers that track the pages a contact or IP address visits, content they download, their site searches and potentially comments they left on an article or video.

We recently penned a guest article on intent data in VentureBeat to help spark a conversation, and are pleased to continue the discussion here. Read on for the first in our Q&A series with sales and marketing experts…

Mike Burton, Co-Founder and SVP of Data Sales, Bombora

It seems like the place that 3rd-party intent data has gotten the most early traction is with email personalization and targeted advertising. Can you explain that use case?

Mike BurtonEnabling programmatic targeting is a big part of Bombora’s business for sure, and compared to email it’s relatively simple. We build targetable sets of cookies that have very high consumption against specific B2B topics. We also create account-based segments, which plays very well together with predictive. Email is trickier, because Marketing Automation is built to market to contacts, and the monitoring of the B2B web mostly takes place on a company + location level. So, we created a product called “Frog DNA” (a Jurassic Park Reference), which looks at each contact’s company, location, and department and appends fresh intent data to every record every month. If we cannot use company level data we’ll fill in the gaps with overall B2B topics that are surging across broad topics like tech, sales/marketing, etc. Marketers then use that data to reactivate leads, alter nurture paths, etc. Probably my favorite use case is aggregating all of the most popular topics and using it to create relevant new content with a data-driven approach.

Can you share a customer success story that highlights how companies can measure the ROI of intent data?

Does “Intent” Data Live Up to the Hype?

Originally posted on VentureBeat

With all the talk about predictive-driven sales and marketing, a new question is emerging – which data is most valuable? Many B2B businesses are achieving unprecedented customer insight by leveraging all kinds of external demographic and firmographic data to see if a company is a good fit for their product. Some are pairing that with signals from their marketing automation systems and web analytics to predict whether a prospect might be ready to buy soon.

2015-07-14_15-51-31

Now, another breed of “intent data” has emerged. External data providers like Bombora, The Big Willow, IDG and TechTarget are aggregating information about web visitors on B2B publisher networks to help businesses figure out when certain prospects might be in the market for their product. This kind of insight presents an exciting new frontier for data-driven marketing.