Introduction

When digital marketing first became popular, the goal was to produce as many leads as possible and then pass them, en masse, to sales. These leads would keep the sales team busy, and that productivity would cast marketing in a positive light.

But the way businesses conduct online commerce is changing. Productivity is now expected to be more disciplined and more efficient. Sales doesn’t need to talk to every lead; they only need to talk to the right leads. This new focus requires marketers to be more judicious in who they select for sales, which has made lead quality a priority for 74 percent of B2B marketers.1

How does one measure lead quality? You use lead scoring to assign a value to each profile in your database based on certain factors like company size or recent behavior on your website.

Lead scoring software gives you a qualification system that is scalable and automated. In this guide, we’ll help you understand the different types of lead scoring available, how to create executive buy-in for a purchase, how a leading B2B company put lead scoring to work, and how to choose the best lead scoring software for your business.

Types of Lead Scoring

1. Traditional/Rules-Based

Rules-based or “traditional” lead scoring consists of assigning a numerical value to characteristics of and behaviors exhibited by people in your marketing database. These rules are defined by the marketer, with strong input from sales.

Marketo breaks rules-based scoring into two categories based on respective types of data: implicit and explicit.2 Implicit lead scoring refers to tracking and quantifying a prospect’s behavior on your website. The classic example is when someone views your product page versus when someone views your careers page. One behavior implies interest in your product and should trigger an increase in lead score, while the other indicates an interest in working for your company and should trigger a negative score.

Explicit lead scoring refers to the assignment of a score based on information someone volunteers when they fill out a form — the number of employees at their company or their marketing budget, for example.

Rules-based lead scoring has been around for some time, and it’s now a standard feature in most marketing automation platforms. A traditional scoring framework can be pivotal to increasing the quality of leads you pass over to sales, but it is not, strictly speaking, the type of framework you will find in the products termed lead scoring software.

2. Predictive Lead Scoring

As the market stands today, lead scoring software is synonymous with predictive lead scoring. Predictive scoring differs from rules-based scoring in that it relies on predictive modeling to identify which leads are best suited for your sales team to pursue. These point solutions provide a more complex form of scoring based on machine learning and algorithmic prediction.

How, specifically, does this work? The scoring software builds a predictive model using the data in your CRM and marketing automation system to identify which series of behaviors and lead characteristics your closed/won accounts share.

The system then augments this internal data with external sources to construct a more complete lead profile. Infer, a predictive scoring vendor, describes it this way: “Infer uncovers thousands of signals such as employee count, job openings, web presence, social presence, technology vendors, patents, trademarks, and more.”

In theory, predictive lead scoring has several advantages of over rules-based lead scoring.

  • No more guesswork: Anecdotes from sales and small ad-hoc correlations form the foundation of many rules-based frameworks. Predictive scoring builds a statistical model to isolate the most important leads.
  • More complete data: Traditional lead scoring relies only on internal data — the data sales enters into the CRM and the information marketing automation grabs from a person’s onsite behavior and form downloads. Because predictive scoring vendors bake in additional information from a number of outside sources, the predictive model can identify previously hidden factors that contribute to your ideal lead profile.
  • Better scale: Rules-based scoring is difficult to scale, because as the company grows and changes, manual corrections must be made to the framework. With predictive scoring, the model can correct itself based on the changing behavior of your audience.

Are You Equipped for Predictive Lead Scoring

Rules-based lead scoring is often available as part of a larger platform, like marketing automation or even customer relationship management software. What the market now refers to as “lead scoring software” usually indicates software with the predictive capabilities mentioned above.

The market for this software is still small, with only twenty or so products centering their value proposition around predictive lead scoring. These products are standalone applications, which means they need other platforms to supply their predictive models with data. Because lead scoring software functions almost exclusively as an add-on, it’s important to make sure your organization is prepared to fully utilize this technology.

Here a few prerequisites you need:

A Consistent Pipeline

Lead scoring is a means of increasing your sales team’s efficiency. If your sales team has enough time to contact each and every person who makes a request on your website, you don’t have enough volume for lead scoring.

A History of Conversions

A predictive lead scoring model runs on historical data. If you can’t supply a reasonable sample of closed/won accounts, then the model won’t be able to produce a trustworthy forecast.

A Reason to Ditch Rules-Based Scoring

If you’re using rules-based lead scoring, how well does it work? There are several important metrics to consider — the conversion rate of marketing qualified leads (MQL) into sales accepted leads (SAL), for example.

If you have a decent conversion rate, there’s probably no need to abandon rules-based scoring. If your conversion rate is low — SiriusDecisions puts a good conversion rate at 32 percent3  — you might have a case for predictive lead scoring.

Marketing Automation and CRM Software

Predictive scoring relies on the data stored in two main repositories: a marketing automation platform and a CRM. If your organization doesn’t have either or both of those, a predictive model would have limited utility.

Creating Executive Buy-In

When Talking to Your CMO

As marketing continues to shoulder more revenue responsibility, CMOs will expect every expenditure to deliver measurable ROI. Make the argument that predictive lead scoring can increase your MQL/SAL conversion rate, which should result in more revenue. To stack the value of lead scoring software, remind your CMO that time is money, and that each hour sales spends talking to the wrong leads is an hour wasted. If used to increase lead quality, scoring software will save sales time and save the company money.

When Talking to Your CEO

Because CEOs often have to report to a board of investors, they are obsessed with hitting numbers. If they report positive growth, they can position the company in a positive light. To persuade the CEO, focus on the big picture. Explain that time chasing bad leads is time wasted, which slows growth. Emphasize that lead qualification is a large part of marketing’s job and that it has a direct impact on growth. Mention the MQL to SAL conversion rate and bring a (conservative) revenue projection that increasing that number could bring to the company. This argument proposes growth through specific, quantitative action,

When Talking to Your CTO

Even if lead scoring software will serve departments outside of IT, it’s still wise to get the CTO on board. This argument is relatively straightforward. Lead scoring software can deliver substantial business value, and it shouldn’t be overly complicated to install. A worthwhile product should easily integrate with your CRM and marketing automation platform. Your CTO will be a valuable resource during the selection process; ask for advice on vendor selection, system requirements, and implementation.

When Talking to Your CFO

Like the CEO, the CFO is going to want to see some numbers. Hype and industry jargon don’t have a place in any discussion with a CFO, so focus on the specific financial benefits lead scoring software can deliver to the company: shorter sales cycles, increased conversion rates, etc.

If you want to go a step further, take the argument about cost-savings based on increased sales efficiency and put some math behind it. Estimate the salary of a salesperson at your company, and calculate how much time is lost on sales development activities for sub-par leads.

Lead Scoring Case Study: InsideView and SalesPredict

Challenge

InsideView is a CRM Intelligence Platform that unites marketing and sales teams by turning the CRM platform into a source of business insight and curated marketing data. Naturally, this data helps marketing and sales deliver more leads and sign more clients.

To help grow their lead database, InsideView put a substantial amount of resources into building a demand generation infrastructure — implementing both Salesforce and Marketo in the process. InsideView then tasked several business analysts with building and maintaining lead scoring criteria using Marketo.

“Marketo’s behavioral lead scoring alone got us pretty far, but eventually we hit a wall in our ability to drive up conversion rates,” said Joe Lucas, director of demand generation and marketing operations.

Solution

When Lucas investigated SalesPredict, he was intrigued. The software’s predictive ability could augment InsideView’s behavioral model by combining external data with the behavioral data Lucas’s team was already using. Further, SalesPredict would be able to create a more accurate model for identifying buying intent, which would increase conversion rates.

Results

After implementing SalesPredict, InsideView realized a 100 percent growth in their sales pipeline within a year, and lead conversion rate increased by 25 percent.

Predictive lead scoring also reduced the amount of time it took for InsideView to qualify leads. Before using SalesPredict, it took 18 days for InsideView to determine the qualify of a lead. Now it takes only two days — a 90 percent reduction in qualification time.

Choosing the Best Lead Scoring Software

TechnologyAdvice helps businesses connect with the best technology for their needs. We’ve compiled product information, reviews, case studies, features lists, video walkthroughs, and research articles on lead scoring software vendors to make the buying process more straightforward for decision-makers like you.

If you’re curious about any of the tools listed in this guide, we’d love to talk. Call one of our in-house specialists for a free consultation, or use the Product Selection Tool above to get a custom recommendation based on your industry and feature requirements.

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