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If you have an impact on sales for your business in any way, you’ve heard of lead scoring. You might think of it as a tool for making sense of leads as they flow in.
In many cases, this is only partially true. Traditional lead scoring tools will rank leads by giving them a number, but these rankings are based on attributes that may be arbitrary and are often flawed.
Did you know that lead scoring has advanced beyond a structure for ranking leads into a tool that can truly predict which leads are most likely to close a sale?
Predictive lead scoring is the new norm for sales operations thanks to business applications of artificial intelligence and machine learning.
With truly predictive lead scoring you gain an advantage over your competitors who are misusing their lead data with stagnant traditional lead scoring models.
Applying machine learning to lead scoring optimizes your leads, provides instant and accurate decision support and prioritization, and saves you time and effort that can be directed back into sales ops and selling.
The basics of traditional lead scoring
Without lead scoring, it’s difficult to take the first steps to manage leads that come into your sales funnel. Scoring leads allows sales and marketing teams to quantify whether leads are qualified.
This ideally informs good decisions about how to prioritize, distribute and work each lead to focus on sales-ready leads and increase conversions.
What is lead scoring?
Traditional lead scoring is considered the ranking of leads as they’re generated or purchased and come into your CRM system. They are scored or ranked with the idea that a higher-value lead is more likely to turn into a successful sale.
Through lead scoring, values or points are assigned to each lead, showing how qualified they are — often meaning how likely they are to close a say — based on various criteria.
How lead scoring models have traditionally been used
To score leads, lead data needs to be collected and certain criteria must be chosen by which to evaluate leads.
This is where a lead scoring model provides the structure and strategy for how leads are ranked.
Within a lead scoring model, different criteria are established for assigning values or points to each lead to determine how qualified it is. Criteria usually fall into two common categories:
- Lead behavior: Assess how leads engage with you through website visits, downloads, forms filled, phone calls, emails opened, etc.
- Lead demographics: Assess who leads are by evaluating how qualified they are based on their information including their location, job title, age, industry they work in, income, etc.
To learn more, check out: How to Develop a Lead Scoring Model, and Why it Matters.
Lead scoring limitations
The most challenging aspect of traditional lead scoring revolves around how a lead scoring model is effectively built and maintained. To be effective, it needs to not only evaluate the right lead criteria but also turn that evaluation into actionable insights that bring results.
There are tools available to assist with the data management side of lead scoring, but even the best traditional lead scoring tools are limited by assumptions as to what makes a qualified lead. They may also lack customization and the capacity and resources to continuously analyze large, ever-changing sets of data.
Many traditional lead scoring tools provide sales and marketing teams with a number or value for a lead, leaving the teams to determine what the next best step is. If sales and marketing teams aren’t aligned on how to qualify these values, their efforts and that lead data can seize up.
Without these tools, trying to assess leads manually often halts organizations because it’s just not possible to manually manage, evaluate, and qualify thousands of lead data points with hundreds of new points coming into the system every day.
You can support sales and marketing with the right tools. Learn more: Automate Moving MQLs to Sales Qualified Leads with AI/ML Solutions.
Advantages of predictive lead scoring using AI/ML
Businesses are tackling these challenges by embracing the true potential of lead scoring and understanding it beyond a basic number or grade assigned to each lead.
Artificial intelligence and machine learning have made this possible, offering new production-ready software that takes lead scoring further and improves various aspects of lead management using predictive lead scoring.
What is predictive lead scoring?
Using artificial intelligence and machine learning, predictive lead scoring uses complex algorithms to predict what mix of key lead attributes and behaviors will identify leads that are most likely to close a sale.
Predictive lead scoring machine learning offers the capability to continuously learn from lead data and the results that lead to a closed deal. It then uses this data to quickly output decision support for sales and marketing teams.
How is AI lead scoring better than traditional lead scoring?
Predictive lead scoring allows for the ability to automatically analyze large amounts of lead scoring data within seconds. That analysis uses the same data as traditional lead scoring but comes with more than just an assigned value or score for each lead.
It provides deeper, actionable decision support to guide the next steps of marketing and sales teams, using intelligent predictions for what makes a deal most likely to close.
This helps organizations prioritize which leads to work and can impact areas beyond lead scoring including sales agent performance, a clearer understanding of lead sources, and the effectiveness of various lead engagement strategies.
It can also improve the experience for your customers with better responsiveness to those who are ready to buy.
Dig deeper. Find everything you need to know in our Ultimate Guide to Understanding and Implementing Lead Scoring.
Predictive lead scoring data and the steps needed to get started
So how can AI lead scoring be applied to your organization to bring you results? Let’s get into more detail about what predictive lead scoring machine learning models can do in action.
How predictive lead scoring works
Like the basics of a lead scoring model, predictive lead scoring will measure lead data based on behaviors and basic information.
However, it takes this further by measuring and automatically analyzing this data on a more complex level, assessing various data points and how they impact one another to forecast lead behavior. It’s all done using a model supported by data scientists, but it doesn’t require human intervention to run, update and improve continuously.
These predictive lead scoring machine learning models can make predictions for various goals. In many cases, it’s most effective for lead scoring when used to predict which leads are most likely to convert to sales.
The data itself doesn’t have to be robust or in perfect order for machine learning lead scoring to begin working. An experienced predictive lead scoring provider can help you sort this out. Predictive scoring models use your historic and new incoming data.
As leads continue to flow into your system, bringing more data with them, the model learns over time and becomes more accurate in how it evaluates and ranks various criteria.
This allows for continuous updates to how leads are evaluated and the outcomes that are predicted. As you can guess, this provides an advantage when managing changes to the market, your products and services, marketing campaigns, and even your sales team.
Get results with custom predictive lead scoring
ProPair uses machine learning to offer lead scoring and more. We make the most of your current lead management system, partnering with you to ensure lead scoring efforts bring results, including more closed sales and an improvement to your ROI.
Our predictive decision support software allows you to customize what lead criteria are measured. These customizations are guided by broader categories we’ve built into our machine learning model to evaluate leads.
For example, this includes measuring “touches,” or how you reach a lead (which could include phone calls, emails, texts, etc.) and “hits” for measuring steps a lead has taken (like filling out an application for a loan if you’re a lender).
After you’ve customized these criteria to fit your organization with support from ProPair, our machine learning software does the work to assess these criteria, compare data and provide predictions.
Predictions are delivered to you in ranked groups that we call bins. Each bin has leads with a different level of likeliness to close, helping you prioritize and assess leads.
This is just one example of the application of our machine learning models. We also offer decision support around sales agent performance using equitable tiers to provide insight into performance.
Lead data and sales agent data can also be combined to provide actionable insight into how to get the best leads to the right sales agents at the right time, supporting lead distribution and optimization.
Predictive lead scoring increases sales. Learn more: Why AI Lead Scoring is Essential for Raising Conversion Rates.
Difference between predictions and assumptions for closing deals
Predictive AI lead scoring offers accurate assessment and actionable insights using objective data and complex lead scoring models.
The goal is to rank leads predictive of a deal closing. With traditional lead scoring, lead values are determined by assumptions about what makes an attractive lead. But this is not necessarily based on closing and revenue-generating lead attributes.
ProPair can help you determine factors like how likely leads are to convert based on who they are assigned to and when, as well as when those sales agents make their follow-ups with leads.
Through our machine learning models, we’ve learned that some traditional sales rules, or rather assumptions, that always seemed black and white have a lot more nuance to them and can impact how you work leads.
Breaking down assumed sales rules
Let’s look at this example from ProPair’s insights into sales trends.
The age-old sales training concept that “if you’re not first, you’re last” is wrong when it comes to contacting new leads. Leads that come into your system outside of work days and normal hours need to be worked differently, and organizations perform uniquely with these.
Following up quickly is a great goal, but it’s not the only rule to enforce with brute force. Instead, your actions should depend on a range of factors, including your specifics about your organization, the lead source, when the lead is received, market conditions, the lead’s purpose, region, etc.
Despite how intimidating it might be to consider all of these data points, deciding what to do to optimize interactions with these leads is very manageable. It just requires the right tools to manage it. And this is where machine learning can tell you what actions are most likely to convert these leads.
Read more about implementing predictive AI applications in our Simple Guide to Optimizing AI/ML for Business Operations.
Close more sales with predictive lead scoring machine learning models
Beyond assigning a number to leads, predictive lead scoring gets direct results for these goals:
- Increasing sales
- Reaching KPIs
- Improving ROI
- Supporting sales and marketing alignment
That’s why AI/ML applications are increasingly being adopted by organizations across industries.
With the right set of lead management tools and intelligent support, you can use your current system, team and resources to optimize lead conversions. ProPair works with you to reach the best version of your business, recommending customized AI/ML solutions and ensuring they bring you results.
Learn more about ProPair RANK, MATCH, and MIX:
- RANK: Know exactly which leads to focus on and when with predictive lead scoring.
- MATCH: Know exactly what sales agents to assign leads to.
- MIX: Optimize your entire sales operation by maximizing the potential of every lead and every sales agent.
We’ll run a free 48-hour data analysis using machine learning, where we’ll pull lead management data and show you opportunities to improve lead scoring, distribution, and conversion.