How to use AI for a lead scoring system

AI for lead scoring

Leads will come and go over the course of your business’s lifetime, but the quicker you can identify, sort, and capitalize on your top leads, the greater your chances will be of closing a sale. Lead scoring with AI uses calculated information to make the best decisions on which customers to pursue, which to nurture, and which to throw out altogether. 

Lead scoring is about taking the knowledge of your top sales staff and combining it with the data you’ve collected to help your team know where to concentrate their efforts. Eliminating the need to constantly research leads to validate the top spot not only saves you and your team time but also focuses your efforts in a collective way.  

What is lead scoring?

Lead scoring is a way for sales and marketing teams to determine the quality of their leads. This is usually done by allocating a point value to “score” each lead based on how a customer interacts with the brand. Benefits include:

  • Marketing campaign improvement. By understanding who in your target audience is ready and willing to buy, you can better focus your marketing efforts 
  • Improved revenue. The impact on revenue can be significant when it comes to responding to a lead in a timely manner. By using lead scoring to remove the guesswork, a sales team can contact the best leads first.  
  • Saved time. Much of a sales team’s time is taken up simply verifying leads and following up on those with no immediate intent to purchase. Lead scoring removes this barrier. 

How does lead scoring work?

Lead scoring lets you remove subjectivity from the process by establishing a ranking for your leads. Any given business might have hundreds of thousands of visitors to its website, each with a different intention. Some may be browsing, some may have ended up there by accident and some may be prime shoppers ready to commit and make a purchase. Lead scoring weeds them out.

 When a visitor comes to your website, you can track their actions. You’ll be able to see how they’re interacting, what they’re clicking on, and even how many times they return. For each of these “actions,” you assign points. 

Points translate into information that lets you know exactly how much each visitor has learned about your product or company so far. This information becomes either implicit data or explicit data. 

Explicit data vs. implicit data

Implicit data and explicit data are both important points of information regarding your potential customer. 

Implicit data is information provided by your system tracking regarding the engagement of your potential customer with your business. 

Explicit data is data the user provides you with, such as their name, phone number, email address, and maybe answers to a survey. This information shows you how well your customer fits into your business and their likeness to purchase. 

Both implicit and explicit data translate into points that are used in the lead scoring system to determine where on the spectrum your potential customer sits. The higher the score, the hotter the lead.

How to set up a lead scoring model 

Lead scoring systems will look different for each company because each business is looking for different types of buying potential in their customers. For example, a clothing company aimed at teenage girls targets a much different demographic than that a sporting goods company. Here are the steps to setting up a scoring model:

  1. Identify your ideal customer
  2. Identify the data points to be scored
  3. Create your point values
  4. Determine your threshold

Step 1: Identify your ideal customer

Each brand has an ideal customer. This is the person you’re targeting most of your efforts toward when marketing. For example, for the teenage girls’ clothing store we mentioned earlier, there are a few factors that will make up a target customer:

  • Age between 11 and 17
  • Female
  • Interest in (X, Y, Z)

By creating this ideal persona to focus your efforts on, you’ll know how to create your lead scoring system to sort for the explicit data needed to find your hot leads. 

Step 2: Identify the data points to be scored

Once you’ve nailed down your ideal customer you also need to develop scoring attributions that will be used to assign points. You can break these into two different categories: behavioral scoring and demographic scoring. 

Behavioral scoring points may include:

  • Click-throughs
  • Opened emails
  • Downloads
  • Engagement 
  • How many times have they visited the webpage

Demographic scoring points may include:

  • Age
  • Industry
  • Job title
  • Location

Step 3: Create your value points

Once you’ve identified the data points you want to score, you then need to proceed to rank which points best lead to a sale. For example, using the list above, you could assign scores that look like this:

Behavioral scoring points may include:

  • Click-throughs (5)
  • Opened emails (15)
  • Downloads (50)
  • Engagement (25)
  • How many times have they visited the webpage (40)

Demographic scoring points may include:

  • Age (50)
  • Industry (25)
  • Job title (30)
  • Location (10)

By assigning higher values to the data points that will bring you hotter leads, you can easily separate the average leads from the ones with the greatest potential. But what is the best way to assign these numerical values?

  1. Include your entire team. Don’t rely on one person’s expertise to decide the point allocations. Instead, take advantage of the sales staff and find out where they believe they close the best deals.
  2. Use a data-driven approach. Data is skewed all across your organization and CRM platform. Take advantage of your analytics to uncover where your best leads come from.

Step 4: Determine your threshold

Identifying your ideal customer and assigning point values to your data is only the first string of lead scoring. Next, you need to find the magic number that turns a potential customer into a hot lead that needs immediate attention. 

For example, when first starting a lead scoring pilot project, it may be wise not to implement a threshold to avoid cutting strong leads through a lack of understanding. Instead, give the sales team time to uncover where that threshold lies, using previous buyers and customer analytics. 

Lead scoring and AI

It can take quite a bit of leg work to get a useful lead scoring model in place. But what if there was an easier way that also removed the nuance of human error in the process? This is where AI and machine learning come in. 

Predictive lead scoring software is a tool that integrates naturally with your standing CRM network to review your company’s performance data and uncover your best leads. 

Another great benefit of predictive lead scoring is its growing knowledge base. The more information your company collects, the smarter your lead scoring model becomes. This ultimately accomplishes several things:

  • Saved time
  • Quicker results
  • No human error
  • Improvement over time

Improving your lead scoring with ManyChat

Lead scoring and lead qualification are rendered moot without the proper lead generation required to bring in the customers you need to make a sale. ManyChat offers chat automation that can:

  • Engage with your audience
  • Offer incentives 
  • Gather information
  • Start meaningful conversations

As you’ll see when conducting lead scoring, not all of your leads will be ready to buy right away. This is where lead nurturing comes in. ManyChat enables business owners to nurture those leads through follow-ups and personalized conversations, to keep the ball rolling until trust has been established. 

For more of ManyChat’s excellent tools and services, sign up for free today. 

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The contents of this blog were independently prepared and are for informational purposes only. The opinions expressed are those of the author and do not necessarily reflect the views of ManyChat or any other party. Individual results may vary.