Part 2: The Sales Use Cases of Predictive Intelligence

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The first blog of our two-part series tackled the marketing use cases of predictive intelligence. We discussed how predictive-powered account-based digital advertising and account-based lead generation allows B2B marketing teams to target audiences in active buying cycles, improve conversion rates and optimize how they budget.

In this post we’ll explore the way sales teams can incorporate predictive intelligence insights into their workflows and campaigns to focus their attention and efforts on accounts that are most likely to convert into paying clients.

  1. Prioritizing Engaged Leads – Removing False Positives

While we often talk about leading marketing teams using predictive intelligence to generate demand, sales teams need a tremendous amount of information to effectively turn interest into closed business. Enterprise and mid-market teams are often dealing with an overwhelming amount of inbound engagement that often comes to them in the form of noisy data.

This data is so hard to effectively parse that many sales ops and sales managers opt to have leads followed up on in chronological order with little to no prioritization. This approach distributes sales resource equally regardless of whether it is a budget-holder doing research for an active project or a junior employee filling out a contact-me form.

With the high cost of telemarketing and inside sales teams, working dead-end leads negatively effects your ability to generate pipeline, increases your average customer acquisition cost and hurts the bottom line. Ensuring that your sales team focuses its efforts on in-market accounts is integral to improving their capacity management and effectiveness in turning inbound interest into closed business.

Business Challenge:

  1. Effectively prioritize inbound leads.
  2. Waste of sales development, telemarketing and inbound sales efforts on accounts that are not in active buying cycles.
  3. Limited outreach capacity for inbound lead volumes.

Goals of Implementation:

  1. Route leads to high-touch call teams or into scalable nurture campaigns depending on their buying stage.
  2. Manage your call capacity and target it at accounts with a high propensity for conversion.
  3. Remove false positives from your pipeline.

Use Case:

Most marketing teams rely on simple heuristic models to score and prioritize inbound leads for follow up. The decision-making of these models is based on the demographics of the lead, the firmographics of the account and the activity captured by the company’s marketing automation platform.

Integrating predictive intelligence models into inbound prioritization processes allows marketing teams to incorporate 2 new facets that substantially improve outcomes. First, predictive models assign weights to various activities based on their historical impact on the propensity for conversion. Second, predictive intelligence adds a treasure trove of 3rd-party data that prioritizes accounts based on their activity across the broader B2B web, not just your website.

This allows marketing teams to uncover prospects in their database that may appear dormant as well as prioritize inbound leads based on their buying stage, routing some to nurture campaigns and others directly to sales. These new insights focus sales efforts on accounts that are likely to convert and remove false positives from their workflows.

  1. Account-Based Outbound Prospecting

For many companies, a key part of their growth strategy is driven by outbound sales efforts that directly deliver their message and value proposition to targeted audience. Outbound efforts drive pipeline, help displace competitors and at their best, get teams into deals early. The complication for inside sales teams is that often the world they can prospect into is so large that they end up pursuing accounts at random.

Experienced outbound sales teams have learned to prospect based on look-alike models, target verticals and triggers like fund-raising rounds, revenue reports and general news. However, the results of this approach often fall short of the target. Adding predictive intelligence that can funnel outbound sales activity at accounts in active buying cycles can have a tremendous impact on the sales team’s ability to generate their own opportunity pipeline.

Business Challenge:

  1. Not enough marketing-sourced leads to hit revenue targets.
  2. Not getting into deals early enough against established competition.
  3. Engaging the right accounts after a buying decision has been made.

Goals of Implementation:

  1. Give reps insight into accounts in active buying cycles.
  2. Engage with accounts at the earliest signs of them entering an active research cycle for your product.
  3. Uncover need to prospect into accounts even if no account leads have made it through marketing.

Use Case:

The Blue Jeans Networks sales team plays a key role in helping the videoconferencing provider deliver their message and break into a crowded field. Their challenge was identifying which accounts to pursue as the target list of companies that can leverage video conferencing technology is very broad.

Blue Jeans Networks chose to incorporate predictive insights into the Salesforce dashboards of their call team. This allowed the team to identify accounts who may have appeared dormant in their database (were not engaging with Blue Jeans content) or were net-new to Blue Jeans, but were exhibiting research behaviors indicative of being in an active buying cycle.

The results of enabling outbound prospecting with predictive intelligence have been dramatic. Blue Jeans outbound sales team increased their average deal size by 40% and cut its sales cycle in half by getting into deals early. In other terms, the team now requires 1/3rd the amount of touches it took before to open an opportunity with a targeted account. To date, the team has built $30MM in pipeline with predictive sourced accounts.

To learn more about how Blue Jeans Networks approached predictive, check out their video case study: How to Build $30MM In Pipeline with Predictive Intelligence.

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