Infer and InsightSquared Announce Strategic Partnership to Bring Beautiful Reporting to Predictive Marketing and Sales


Press Release: New Predictive Analytics Visualizations Deliver Actionable Insight into a Company’s Lead Generation Programs, Marketing Campaigns, and Sales Pipeline

Infer Inc., a leading provider of predictive technologies that help companies win more customers, and InsightSquared, the top-rated sales performance analytics company, today announced a strategic partnership. Together the companies will deliver new dashboards that visualize key predictive insights to help business-to-business marketers make the leap from being simply data-driven to being fully predictive-driven.

Many companies have only murky visibility into their sales pipeline, and can’t measure marketing campaign performance until it’s too late to make adjustments. With Infer’s statistically accurate customer conversion predictions visualized in several of InsightSquared’s most popular reports, businesses can better monitor the health of their sales and marketing operations. Marketers can use these reports to determine how well their demand generation programs are fueling growth, while sales teams can use them to better align effort to impact through continuous lead management.

Using Predictive to Improve B2B Free Trial Conversion

For many companies, offering a free trial is a critical part of their demand gen strategy. Prospects who sign-up and want to experience the product are inherently more likely to convert than those who downloaded content. The key to maximizing this channel is being able to instantly identify high-value trial users so that you can ensure they get the proper attention from day one.

Over the years we’ve had the opportunity to work with amazing companies like AdRoll, Cloudera and Segment that are part of the movement shaping the future of predictive for sales and marketing. This playbook highlights the best practices Segment is using to optimize support for its free trial users.

Using Predictive Marketing to Improve B2B Free Trial Conversion

Bridging The Sales and Marketing Divide

Many organizations tend to find themselves in cyclical debates around what makes a good lead, but these conflicts can be diffused with a common definition that both sales and marketing trust. Predictive scoring provides an objective, data-driven method for determining if a prospect is a good fit to buy a product and whether they are likely to convert soon, building confidence in the scores – eliminating the finger-pointing that’s so common.

Over the years we’ve had the opportunity to work with amazing companies like Host Analytics, New Relic and Zendesk that are part of the movement shaping the future of predictive for sales and marketing. This playbook highlights best practices that veteran customer Host Analytics has used to bridge the sales and marketing divide through executive dashboards, MQL-based compensation plans, marketing mix optimization, and more.

How to Use Predictive Analytics to Improve Sales & Marketing Alignment

Predictive Meets Inbound Marketing

This week, we’re celebrating all things inbound at the annual HubSpot Inbound conference in Boston, and it’s been great to see the enthusiasm around this exploding breed of marketing.

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What’s even more exciting are the many discussions around the rise of predictive. HubSpot is the latest software player to join the AI Spring, announcing today that it is adding basic predictive features to its product. This is a great way to get started with predictive if you want to minimize your marketing automation configuration burden, but don’t yet have a significant customer base or aren’t ready to jump fully into advanced predictive lead scoring.

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.

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.

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.

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.