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.

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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.

Jerry Yang on His Investment in Infer

Jerry YangI’ve been very lucky during my career to work with some brilliant mentors, one of whom is the founder of Yahoo!, Jerry Yang. I worked with Jerry at Yahoo!, where he helped me push an ambitious product called BOSS (which stands for “Build your Own Search Service”). We’ve stayed in touch since then and he’s been incredibly supportive of Infer’s approach. Many Yahoo! products related to web search, content optimization, etc. live and die on data science, so he’s passionate about the opportunity to bring this rigor into enterprise software.

Following a recent Business Insider profile, we asked Jerry to answer a few specific questions about why he decided to invest in our company, and in the predictive analytics space in general. Here’s what he shared:

Why do you think there’s such a big opportunity in predictive analytics? Yahoo! through its development of Hadoop witnessed what predictive analytics could do with the massive scale of user data. Now it’s exciting to see companies like Infer bringing this technology to other vertical industries that can benefit from it. There’s definitely a huge opportunity for businesses to transform their operations and decision making by using data.

Why did you invest in Infer? For me, I place a very high value on the entrepreneur and founder. In the case of Vik Singh, we go way back to working closely together while we were at Yahoo. Vik was working on Yahoo! BOSS, a search product that quickly became strategically important to the company. We had a great relationship that continued beyond Yahoo!, and it’s a pleasure to support Vik in this venture.

SmartBug Media Q&A with Infer’s Sean Zinsmeister

Inbound marketing agency SmartBug Media does a great job of educating its clients on new ways to increase marketing ROI and grow their business. Recently, the company’s director of marketing, Dolly Howard, sat down with our own Sean Zinsmeister to talk about the value HubSpot customers can enjoy by adding predictive scoring to their technology stack

What is predictive lead scoring

 

Here’s a an excerpt from their Q&A:

How is predictive lead scoring different than custom lead scoring in HubSpot? There are two big differences between predictive lead scoring and the kind of custom lead scoring in marketing automation platforms (MAPs). The first is that predictive uses both internal data from your CRM and MA systems plus thousands of external signals from a variety of data sources outside your company. The second major distinction is that predictive scoring solutions use machine learning to look at all kinds of combinations in the data that humans could never grok on their own. Whereas MAPs require you to manually come up with points-based calculations formed through your gut instincts, predictive solutions take the guesswork out of the equation and do all that work for you in order to better predict higher converting leads.