Marketing LeadsWhile working with sales development teams, one of the most consistent and surprising gaps I see is in lead prioritization.

With teams I’ve worked with, there is a disconnect between how the data suggests they manage their leads and how they treat their leads in reality.

A study by MarketingSherpa found that 79% of leads never convert to sales opportunities. In a study by Implisit Insights, the data suggests an even smaller amount of conversions, with just 13% of leads converting to opportunities.

Even though data and intuition show that some leads are better than others, sales teams struggle to effectively prioritize good leads over bad ones.

Most teams have some idea of what their best lead sources are. But without any guidance, system or structure, reps actually spread their efforts equally across all leads.

Today, at the height of both the SaaS and big data revolutions, there’s no excuse for any sales team to go without data-driven lead prioritization.

The following points represent proven best practices for prioritizing leads.

Lead Follow-Up


In the landmark InsideSales.com study on Lead Response Management, Dr. James Oldroyd found that a prospect filling out a web form is 100x more likely to answer when called within the first 5 minutes, as opposed to 30 minutes.

Why does immediately calling prospects result in higher contact rates? You connect with them while they’re still on your webpage and while they’re still interested. Yet, even though additional studies across the industry have confirmed Dr. Oldroyd’s findings, it’s extremely difficult for sales teams to execute on this data (the average company takes 38 hours to respond to a web form).

While working with sales teams, I’ve noticed that a common challenge they face is meeting the service-level agreement (SLA) they’ve established with marketing for lead follow-up. With data providing the context and urgency for immediacy, and technology that can automate this process, there is no excuse for breaking a sales and marketing SLA in 2016.

Lead Response Times Chart


The Lead Response Management Study also found that the optimal number of calls per outbound lead was 6. However, on average, reps only make 1.5 calls on each lead before giving up.

In B2B sales, connecting with decision makers means working your way into the busy schedules of executives. One to two, or even six calls, may not be enough for prospects to know they’re on your radar.

For sales teams, persistency is an improvement that can be made immediately, and has tremendous payoffs. Though technology can be applied for automation purposes, teams can enforce processes for increased persistency that require little or no technology, using CRM activities, CRM workflows or manual processes.

The most important factor for persistency is that reps are in the mindset of reaching out to prospects with increased frequency.

High-converting lead sources

Lead to Opp Conversion

Sales operations teams have a tremendous amount of data at their disposal. For a sales development team, one of the best pieces of data to consider is lead-to-opportunity conversion by lead source (depending on how your team is compensated, you may want to look at slightly different data, e.g., demos per lead source or appointments per lead source).

This data shows which lead sources result in the most valuable and successful leads: prospects who are ready to move along in the sales cycle.

For example, if your webinar leads convert 30% of the time, but your trade show leads convert only 5% of the time, you want reps to spend more of their time working webinar leads.

Implisit Insights conducted a study of the most effective lead sources, concluding that website inquiries, customer and employee referrals, and webinars have the highest conversion rates. Email campaigns, lead lists and events were among the lead sources that resulted in the lowest conversion rates.

Though this line of thinking is fairly intuitive, I’ve seen that it’s difficult for sales teams to execute on this data. Ideally, a sales team should use lead prioritization technology to put into place business rules that reflect data-driven best practices.

Sales leaders should implement technology that can help enforce a proven cadence around the highest prioritized leads, ensuring that focus and effort is allocated to the best leads.

Lead Prioritization Model

Artificial intelligence

High-performing sales organizations are recognizing the importance of applying AI to their processes to help them prospect intelligently and sell more.

In order to keep up with intensifying B2B competition and increasingly hyper-aware customers, it’s critical for sales teams to adopt AI as part of their strategy.

There are many helpful lead-scoring tools currently available in the market. However, many of these tools do not update their scores in real-time, and many scores only take into account a handful of data points.

In addition, these tools often do not include new and recent sales activity in their models.

When looking to apply AI to the sales process, sales leaders should seek a data engine that is dynamic, that offers a full view through multiple and varied data sources, and that includes sales activities in its modeling.

In addition to vetting a tool for its AI power, it’s important for sales leaders to understand the current limitations of AI in business generally, and within the sales industry specifically. AI models still often require business logic to provide basic success criteria.

Data-driven prioritization

To be competitive, your team needs to sell like it’s 2016. This means using data-driven best practices, data-driven business logic and artificial intelligence. Don’t let your sales reps spend the same amount of effort on bad leads as they do on good leads.

Use data to drive prioritization and, ultimately, fuel sales.

Find out how to use AI to improve your sales results by grabbing the free guide below. 

Predictive Selling Guide