Published on

January 8, 2024


Complete Guide to Using AI Predictive Lead Scoring in Sales

AI is revolutionizing sales, and with good reason. Sales teams can focus on what matters using AI-powered lead scoring.
Craig Wisneski
Co-Founder & Head of G&A, Akkio

Improving your conversion rate can make a huge difference in your business - even more so than improving the number of leads you get. To understand why, it’s important to understand what a conversion actually is.

The simplest definition of your conversion rate is simply how many leads or visitors complete a given goal. But that’s not all there is to the story. You need to know when leads drop off, why they drop off, and how to maximize the number of quality leads that convert.

Conversion Rate = Goal Completions / Visits

Let’s assume that you have a typical 2.3% conversion rate on a website that averages 250 new visitors per day, or 7,500 visitors a month. Your conversion rate is going to be about 5 conversions per day. If you want to get 1,000 sign-ups in one month, you’ll need an astonishing 43,478 visitors for the sign-up goal to be achieved. But if your conversion rate is among the top 10% of sites, with 11% of visitors converting, this will only take about 9,000 visitors in 30 days.

That would mean that your company can still achieve high revenue goals, even with few site visitors. If your site is among the very best, you could see conversion rates as high as 25%, requiring just 4,000 visitors for our fictional site to reach its revenue goals (instead of over 40,000).

There are a variety of ways to increase your website’s conversion rate, such as lead scoring. Read on to find out more about how lead scoring impacts conversion rates and what it means for sales success.

What is Lead Scoring?

Since the dawn of “sales” as a job title, the question of how to make the best use of a salesperson’s time has always been a difficult one. In today’s digital world, where hundreds of leads can come in at any given hour, it can be impossible for a sales team to keep up with a constant stream of opportunities. If your sales team is unable to efficiently manage and close leads, it may be time to introduce lead scoring.

Lead scoring is a methodology that helps salespeople identify the best leads to make the best use of their time. It is particularly useful when you have a large number of leads and need to prioritize the best opportunities in your sales funnel.

By using a scoring system to rank the leads’ importance, engagement with the brand, and how likely they are to buy, you can get your sales pipeline back in shape.

Whether your focus is B2B or B2C, lead scoring is an important tool that will help maximize efficiency and productivity in your sales team. Any salesperson will tell you that having the ability to focus on time-sensitive leads will help them do their job even better. 

Lead scoring also informs what kinds of leads your marketing team should be targeting. By focusing on lead profiles with higher scores in future marketing efforts, you can improve your sales-marketing alignment, and further raise site conversion rates.

Incorporating predictive analytics into your lead generation activities saves time and money, particularly if you’re using paid ads, which are doomed to result in a negative ROI if you’re bringing in a lot of the wrong leads.

Traditional versus predictive lead scoring

Traditional lead scoring is typically organized along a spectrum, where all leads start at zero points. Every lead is then allocated points for various actions they take to engage with your brand. This includes submitting their contact details for things like webinars, engaging with your content on social media, or receiving email newsletters from your company.

These points are then added up, and contacts with a predictive lead score above a certain threshold are deemed worth pursuing.

This manual process can be time-consuming, but it's still the most common way that companies score their leads. One of the biggest disadvantages of traditional lead scoring is that it's often inaccurate. This is because it relies on manual data entry, which is prone to human error. It can also be difficult to keep track of all the different interactions a lead has with your brand, making it hard to assign an accurate score.

At scale, this process also becomes very resource-intensive, leaching time and money away from your marketing team.

Predictive lead scoring uses machine learning algorithms to predict future behavior using historical and current data, which can be continuously improved on receipt of new data. In other words, it’s a lot more nuanced, and can capture both greater data and greater complexity than the traditional scoring process.

Unlike traditional lead scoring, you do not manually decide “what makes a good lead” - a machine learning model does this for you based on your historical conversion data and what it learns from ongoing results. In this way, the machine learning model can identify trends that you otherwise might not have spotted.

The data you have available to use with predictive lead scoring can also be a lot wider - think: device type, web browser type, location from IP address, clickstream data, time spent on each page, and order of page views. 

And some of the richest data is time-based. Taking a collection of actions within a short window of time may signal a much stronger intent than taking the same actions over many months.

Re-marketing is another way that predictive lead scoring could be useful. When prospects visit your website, an algorithm can be deployed to analyze their behavior and assign them a score based on how likely they are to convert. You can then target your ads at that group of qualified leads.

Re-marketing can help you fill your sales pipeline by constantly keeping your brand in front of leads, even if they're not ready to buy right away. You've likely experienced this yourself: you visit a website, browse around for a bit, and then start seeing ads for that site everywhere you go. That's re-marketing in action, and it can be highly effective.

However, if a business fails to deploy re-marketing correctly, it could come across as annoying and push potential customers away. That's why it's so important to combine re-marketing with predictive lead scoring. By carefully targeting ads to leads that are most likely to convert, you can avoid turning off potential customers.

In short, predictive lead scoring and re-marketing are two effective tools that can work together to help you fill your sales pipeline and close more deals.

Advantages of predictive lead scoring over traditional

Effective lead scoring is critical because it informs your sales and marketing efforts.

There are many advantages to using predictive lead scoring over traditional lead scoring. Often, traditional lead scoring can use incorrect assumptions, as they’re based on marketers’ instinct about the perceived value of different lead traits rather than data. What may have worked two years ago may no longer be appropriate now, but “instinct” is slow to change. However, predictive lead scoring relies on concrete data. This can be a key advantage for companies as it allows them to react to changes in the landscape as quickly as new data emerges.

Predictive lead scoring also removes the bias and human error sometimes found in traditional lead scoring, where certain behaviors may be weighted subjectively. This may simply be due to the fact that different people react differently to different stimuli (e.g., a sales email), which can be uncovered by machine learning models.

Perhaps most importantly, predictive lead scoring can be done automatically and at scale. This marketing automation can automatically predict the conversion probability of new leads in real-time, while traditional methods would require additional manual work with every new lead. In addition, because predictive lead scoring relies on data and machine learning, it can be updated and improved over time with new data. This is in contrast to traditional methods, which require time-consuming manual updates.

Predictive lead scoring can help organizations prioritize their sales and marketing efforts, focus on the most promising leads, and save time and resources. By automatically predicting which leads are most likely to convert, predictive lead scoring can help organizations improve their conversion rates and close more business.

This automation frees up time for sales and marketing teams so they can focus on their most important tasks: generating new leads and nurturing relationships. It also allows them to be more strategic in their outreach, since they know which leads are most likely to convert.

Predictive lead scoring can also help organizations identify when a lead is “sales-ready” and predict how long it will take for a lead to convert. This information can be used to optimize sales and marketing strategies, as well as forecast future revenue.


The first consideration of whether lead scoring is right for you is: do you actually have enough leads to make it worthwhile? If you’re still at an early stage of your business journey, you’re still likely to be in the process of working out what constitutes a qualified lead in the first place.

The second consideration is a little more nuanced but equally important. Do you have enough data to make lead scoring work? The more data you have about your leads and how they’ve interacted with your business and your sales team, the more effective your model will be, as clearer trends and indicators of good leads will emerge. Machine learning models trained on your business’s data - not an aggregation of generic data - will perform the best for you.

Even if you have a lot of leads, if all you have is their contact information, then you won’t be able to do meaningful lead scoring. Ideally, you’ll have data that’s potentially indicative of a lead’s likelihood of conversion, such as the lead’s source, their numbers of visits, their level of engagement, their time spent on the site, their location, and so on.

What’s most important is the quality and quantity of your data—no matter what CRM you’re using. While some sales reps prefer Hubspot and others prefer Salesforce, any data source can be used for building models. For larger teams, big data tools like Snowflake can be used to prepare data for incredibly accurate artificial intelligence models.

While smaller companies might need to do some extra legwork to get all the data they need, this could pay off big time.

If you don’t already have the data in place, you can use lead enrichment tools like Clearbit or FullContact. These tools let you find additional data about a lead, given just their email address. You’ll get all kinds of useful information, like their role, seniority, Linkedin profile, job change notifications, as well as firmographic data like employee count, technologies used, and industry classification.

All this additional data, from demographic information to behavioral data, can help fuel more accurate predictive models. Ultimately, customer data is the fuel for improving your lead scoring and sales process.

Akkio: A powerful predictive lead-scoring solution

One of the many AI use-cases made accessible by Akkio is augmented lead scoring. As we’ve explored, lead scoring is a method of rating the attractiveness of a lead as a potential customer. 

Akkio connects directly with CRMs and platforms such as Salesforce, Hubspot, Snowflake, BigQuery, and automation tools like Zapier - no matter where your data is coming from, or where it needs to go, you can connect it to Akkio to get a prediction.

A guide on using Akkio for Augmented Lead Scoring

With Akkio, augmented lead scoring can be done in minutes instead of months. How? It’s simple: Just upload or connect to a historical dataset of leads, including a column on whether or not the lead converted, and then select that column.

Akkio’s proprietary model training technology will automatically learn to predict conversion for new data points, including the likelihood of that outcome, thereby providing a lead score.

To get started, you can copy the Akkio lead scoring flow here. As you can see, it starts with connecting a historical dataset of leads, which includes a column on whether or not that lead converted.

A screenshot of the Akkio demo flow titled “Augmented Lead Scoring,” showing a customer dataset.

The demo flow goes on to show that you can integrate and merge data from different sources, but let’s move ahead to the “Predict” step, as you need just one dataset to get started.

Now, we simply select the column we’d like to predict. In this case, it’s called “subscribed.”

A screenshot of the Akkio demo flow titled “Augmented Lead Scoring,” showing that a predictive lead scoring model was made.

Finally, it’s time to deploy our model. The demo shows how we can deploy the predictive model as a web app, which gives us a shareable URL that lets anyone make predictions with our model, even without having an Akkio account.

With an Akkio web app, you can make a prediction by inputting the requested data, such as the lead’s contact type, campaign source, or job, and hitting “Predict.” It’s as easy as that! You can also make predictions in bulk by uploading a CSV, such as a dataset of new leads that you want to score.

You’ll get back both a prediction of the outcome and the percentage likelihood. You can use the percentage likelihood to take different actions on different groups of leads. For instance, you might actively engage your top leads, nurture those below it, and ignore ones that rank below a certain threshold.

A screenshot of the Akkio demo flow titled “Augmented Lead Scoring,” showing the settings to deploy a web app.

It’s possible to deploy your model in a number of other ways, including directly in a CRM or platform like Salesforce, Hubspot, Snowflake, Google Sheets, and BigQuery. Smaller sales teams can make do with a simple solution like Google Sheets, while larger teams tend to use special-purpose tools like Salesforce.

Even if you don’t use any of these tools, you can still easily integrate lead scoring models in two other ways: With Akkio’s API or with Zapier.

With the Akkio API, teams can serve predictions in practically any setting, and with Zapier, teams can serve predictions through thousands of different integrations, without writing any code.


Using no-code AI lead scoring is a crucial step in a modern sales strategy because it helps improve your sales conversion rate—a key metric for any business. 

Making use of this type of system will allow you to target high-quality prospective customers and allocate sales resources where they're needed most. Implementing predictive lead scoring can be an excellent way to increase your success in lead routing, lead generation, and lead nurturing.

The benefit for salespeople is clear: More time spent on important leads and less time wasted on leads that’ll never convert. 

The list of benefits goes on, but here are just a few reasons why it's worth the investment:

  • Increased marketing ROI with shorter campaign life cycles and less wasted ad spend
  • Improved and accurate targeting
  • Automated cross-sells and upsells
  • Saving time for sales staff

Whether you're looking for a better lead generation strategy, ways to improve customer experience, or strategies to increase customer lifetime value, AI for lead scoring will increase your chances of success. Sign up for a free trial of Akkio to get started.

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