It doesn’t matter if you’re a B2B or B2C business. Finding leads is non-negotiable if you’re looking to boost your visibility and your margins.
After seeing how many leads you’ve got, though, how many actually convert? That is THE question — because conversions and sales are the goal. And the better you get at forecasting who’s likely to buy what you’re selling, the more you’ll profit, and the less time, money, and sleepless nights will go to needless lead generation.
How do you separate the buyers from the non-buyers and predict who’s a quality lead?
The answer is AI predictive analytics.
Besides unifying offline and online customer journeys, AI is changing how businesses can qualify leads through lead scoring AI and other predictive marketing tools.
Find out what AI predictive analytics is all about and why it might just be the missing ingredient to your lead qualifying process.
What Is Predictive Analytics in Marketing?
Picture yourself having a crystal ball that reveals which of your leads will progress deeper into your sales funnel. That’s what AI predictive analytics does.
AI-powered predictive analytics is designed for making educated and informed forecasts. And by “educated and informed,” we mean decisions based on user-generated data.
There are various types of user-generated data AI can use to qualify your leads for you, including:
- Website behavior (which pages a visitor views, how long they stay)
- Demographic information (industry, company size, location)
- Engagement signals (email clicks, webinar attendance, social activity)
AI predictive analytics takes this information and runs it through models that can identify patterns too complex for humans to spot. So, instead of treating all leads the same, you’ll be able to see who’s warming up and who needs more nurturing.
How AI Enhances Lead Scoring and Forecasting
To get an idea of how beneficial lead scoring AI can be for your business, let’s revisit how things work with traditional lead scoring.
Traditional Lead Scoring
The traditional approach to qualifying leads is based on a points system. Under this approach, you’d assign values to specific leads and the behaviors they exhibit.
For instance, you can assign five points for downloading an eBook or ten points for requesting a demo.
The higher the score, the better the lead. And by “better,” we mean “more likely to buy or convert.”
Why You Need Lead Scoring AI
There’s a place for traditional lead scoring — until you realize you’d have to qualify leads at scale. When thousands of people click on your site and request calls, lead scoring can become tricky and time-consuming. Luckily, AI can help through lead scoring AI.
By analyzing thousands of factors simultaneously, lead scoring AI can identify subtle behaviors that indicate buying intent. For example, a prospect who watches a pricing video twice might be a stronger lead than someone who downloaded three whitepapers.
The Speed Advantage
With AI-powered predictive marketing tools, you’re scoring leads in real time. If a lead suddenly visits your pricing page or responds to an email, the system recalculates instantly.
As a result, your sales team doesn’t wait days or weeks to see who’s ready. For you, this means a sharper focus, allowing you to direct your attention to the leads most likely to convert, rather than spreading your efforts too thin.
Setting Up Predictive Analytics for Your Leads
There’s no need for a data science degree or to drown in data to set up your predictive AI system. All you need to do is start with the data you’ve already got.
If you’ve ever done lead generation, you’ll likely already have lead data stored in a CRM or a spreadsheet (at the very least).
Once you’ve amassed your data, follow these steps:
- Integrate data sources: Connect your website analytics, email engagement, and sales CRM so the AI sees the whole picture.
- Keep it clean: Remove duplicates and fill in missing fields where possible. It’s garbage-in-garbage out in AI predictive analytics, and accurate data leads to accurate forecasts.
- Start small: You don’t need every dataset at once. Even basic signals, like site visits or past purchases, can power an effective model.
But where can you do all this? We at Connection Model have got you.
We guide clients through all the necessary steps to develop their predictive analytics tools and processes. All you need to do is reach out and get a free demo.
AI Predictive Analytics Works — and Here’s Proof!
Some of the biggest names in various industries have leveraged AI to improve conversions and results for their businesses. Here are some of them.
WeWork: Improving Accuracy With Smarter Models
WeWork used AI predictive analytics to identify which prospects were most likely to tour their properties. By analyzing data points such as location, company size, and online activity, they enhanced the accuracy of their forecasts.
The company’s Bayesian models grew so precise that they could predict strong leads with nearly 97% reliability.
U.S. Bank: Driving Conversions at Scale
U.S. Bank integrated lead scoring AI into its sales process through Salesforce’s predictive tools. The results were striking:
U.S. Bank reported a 260% increase in lead conversions and a 300% boost in marketing-qualified leads.
Grammarly: Filtering for Higher-Quality Leads
Grammarly is on every computer these days, so it’s no wonder the brand struggled with too many leads and not enough clarity on which ones would truly pay off.
By applying Salesforce’s predictive marketing tools, the grammar-correction company became more capable of filtering leads more effectively. Instead of passing every lead to sales, Grammarly focused only on the most promising.
The outcome was a higher close rate and less wasted effort. Think of Grammarly the next time you need proof that quality often matters more than quantity.
Measuring and Optimizing Predictive Models
Launching predictive analytics is just the beginning. Like any strategy, you need to measure key metrics (and adjust accordingly):
- Conversion rate by lead score: Do your high-scoring leads actually convert more often?
- Sales cycle length: Are qualified leads moving through the pipeline faster?
- ROI on marketing spend: Are you getting better results from the same or lower investment?
Remember: Buyer behaviors shift. To ensure that the AI learns from the latest patterns and updates based on data, collaboration between your sales and marketing teams is essential.
AI Predictive Analytics: Quality Leads Over the Quantity of Leads
These days, every wasted warm lead is a wasted opportunity. And whoever determines who’s “warmest” is the business that comes out on top.
Want to identify your most promising leads before they convert? Partner with Connection Model to harness AI-powered predictive analytics and boost your sales pipeline.
Written By: David Carpenter
