Lead scoring is how companies rank leads based on a variety of demographic and behavioural factors to determine which ones have the most sales potential. It's not a new concept, but it's popularity has steadily increased in the last 5 years. 

Sales and marketing teams are beginning to rely more heavily on lead scoring because it saves time and money by helping them focus their time and energy on the most valuable potential customers. 

In the past, lead scoring was a manual process where leads were assessed one by one and assigned a priority.

But as companies and markets grow and vast quantities of customer information have to be analysed and sorted, this becomes more difficult without tools. Changes in the market, as well as customer needs and behaviours, require lead scoring models to be highly adaptable.

Lead scoring software with predictive AI-capable tools can be used to manage these challenges and provide deeper insights.


The Evolution of Lead Scoring Tools


The Beginning: Manual Lead Scoring

Before the more recent developments in software and technology, leads were scored manually. This entailed carefully researching prospective customers, and then logging their information and calculating scores on a spreadsheet.

Although the task was strenuous, it has since proved worth the effort: Organisations in 2012 using lead scoring saw a 77% increase in lead generation ROI than companies who did not score leads—a trend that is bound to continue.


Streamlining the Process: CRM-Assisted Scoring

Since then, customer relationship management (CRM) systems have taken up the task of automating and simplifying the process. Using CRM, companies can gather and input data about each lead, which will then be scored by the software based on the criteria selected.

This data largely comprises demographic and explicit information about potential customers, meaning that a lot of useful implicit data is left out of the equation when using CRM alone.

In this model, sales and marketing staff still need to invest time into researching leads before being able to score and filter out the ones unlikely to make a purchase. While this was seen as an improvement on manual lead scoring, it still took up a significant amount of employees time.


The Next Level: Marketing Automation

Rather than develop an entirely new system for lead scoring, marketing automation was added to the mix. The rise of marketing automation made it possible to track leads’ online behaviour and gather certain types of implicit information. Linking marketing automation to CRM meant that larger quantities and a wider variety of data could be analysed and compared, as processes simultaneously became more automated and streamlined.  

For example, companies could now use marketing automation to track how many times a prospective customer visited a website, use that information to add points to their lead score, and then include that data in the lead’s profile on the CRM system.

Still, while marketing automation could track those actions, it wasn’t necessarily smart enough to distinguish details like whether those visits were the outcome of actual customer interest or if it was someone simply perusing the sight with no intention of making a purchase.

According to a 2016 report, 61% of respondents stated that “misleading buying signals” were among their greatest challenges in lead scoring. This has left room for another newcomer to step into the lead scoring space: predictive lead scoring using artificial intelligence.


Software that Does the Work for You: Predictive Lead Scoring

Salesforce’s State of Marketing report shows that AI is set to have the highest amount of growth among tools or technologies used by marketers over the next two years. This is likely because AI software has been developed to help manage many of the specific challenges that sales and marketing teams face.

The same holds true when it comes to lead scoring: predictive lead scoring has been designed to determine which criteria define a strong lead, create lead scoring models based specifically on your company’s particular needs, and to be adaptable enough to adjust to a changing market.

Let’s take a closer look at a few examples of how AI can enhance lead scoring:


1. Collect and Analyse More Data   

An AI enabled CRM platform has a particularly wide reach when it comes to collecting data: it automatically analyses customer data stored within your CRM, activity-based data (email, calendar, etc.), social data streams, and even any potential IoT data.   

This way, your lead scoring tools can take over the task of sifting through huge quantities of incoming and historical sales data to determine the strongest leads.


2. Create Finely Tuned Lead Scoring Models

Predictive lead scoring utilises algorithms to determine shared traits among the leads that converted and those that did not. Armed with that knowledge, your lead scoring software can then create and test different predictive scoring models, automatically selecting the best one based on a sample data set.

This leaves you with a finely-tuned lead scoring model that is specifically designed for your company, taking the guesswork out of setting criteria.


3. Increase Accuracy and Adaptability

Using machine learning, your lead scoring software will increase accuracy by continually re-analysing data and incorporating new insights to determine lead scores.

It can also use that information to update scoring models at regular intervals to ensure the most accurate predictions for your leads, making it completely adaptable to changes in the market and customer behaviour. 


4. Benefit from Additional Support

Using CRM that incorporates AI technology will also provide you with additional support in a variety of tasks related to lead scoring and lead management. This support ranges from things like account insights and deal predictions to reminders to follow up with clients after a certain event or period of time.

Your AI-enabled software can streamline a number of tasks for you, allowing you to focus your energies on the best leads, minimising response time and increasing customer engagement.  


Close Deals and Build Relationships using AI

Predictive lead scoring has taken over much of the heavy lifting when it comes to analysing data and determining which leads are most likely to convert.

Being able to focus on the strongest leads possible means that sales and marketing staff will be able to close more deals in less time while building stronger relationships with customers.


This post is part of our Navigating the Sales Cycle series. Download the e-book and discover the 7 steps to sales success