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.

MarTech Advisor Q&A with Infer’s Jamie Grenney

In preparation for tomorrow’s Flip My Funnel conference in Atlanta, Ankush Gupta, the managing editor of MarTech Advisor, interviewed our own Jamie Grenney for his views on all things funnel and martech-related.

FlipMyFunnel

Here’s an excerpt from their Q&A:

What is your take on the massive explosion of MarTech companies across so many categories? How do you weigh in on the whole “build vs buy” choice that marketers have?  It is an exciting time to be a marketer. There are more mediums to reach customers, more data sources to draw from, and better tools for optimizing the funnel. While there is an upper limit on the number of vendors your organization can manage, I’m a fan of buy vs. build. With cloud-based applications you get seasonal releases, open APIs, and pre-build connectors. That last piece is key for stitching together best-of-breed applications.

How does one get started with Account Based Marketing since it’s time-consuming and requires considerable effort? Should it only be for high value sales or has marketing technology today achieved enough competence to scale ABM to a larger number of accounts?  While some companies struggle to sift through huge volumes of incoming leads, others depend on outbound sales and account-based marketing to drive growth. In some cases this means a highly concentrated set of accounts, but in others it’s a green field of opportunity. At Infer we help companies rank their cold accounts based on fit for the product and their revenue potential. Our predictive models spot likely b-to-b buyers by taking into account the relevancy of different job titles, plus intent signals derived from individuals’ online behavior. This helps companies cut through the noise and confusion of multiple prospects and surface the right set of individuals to focus on at specific organizations. Having that level of data driven focus from beginning is a great way to jumpstart your efforts.

What do you see as the future of account-based marketing?  I think we’ll see a movement from predictive to prescriptive. Today forward-thinking companies have a predictive score that tells them how good of a fit a prospect is and whether or not they’re likely to buy soon. Those two predictions unlock enormous value because they help align effort with impact. Looking further out, predictive vendors will go beyond a simple score and make specific recommendations regarding what action or what piece of content is most likely to advance a prospect towards a long-term outcome.

 

Check out the full blog post here to read more of Jamie’s perspectives on a variety of topics, swing by Infer’s table at tomorrow’s conference, and learn from Infer’s Sean Zinsmeister during the event’s closing panel on account-based marketing at 3:50pm ET on Tuesday, August 11.

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.