5 ML Lead Scoring Case Studies 2024

Machine learning (ML) is revolutionizing B2B lead scoring in 2024. Here’s how 5 companies are using ML to boost sales:

ProPair: Reviving old leads with 46% more sales
Intelliarts: 90% accuracy in predicting insurance lead quality
Carson Group: 96% accuracy in spotting convertible investment leads
Snowflake: Sorting leads into bronze, silver, and gold tiers
AI WarmLeads: Growing sales pipelines by 30%

Key takeaways:

Clean data is crucial for ML success
ML spots buying signals humans miss
Integration with existing tools is essential
Continuous learning keeps you ahead
Human touch still matters in closing deals

Quick Comparison:

Company	ML Approach	Key Result
ProPair	Reviving old leads	46% more sales
Intelliarts	Predicting insurance lead quality	90% accuracy
Carson Group	Identifying convertible investment leads	96% accuracy
Snowflake	Tiered lead sorting	Improved focus on high-value leads
AI WarmLeads	Website visitor conversion	30% sales pipeline growth

These case studies show ML isn’t just hype – it’s changing how B2B companies find and convert leads in 2024.

ProPair: Making Old Leads Work Again

Ever wonder if those dusty old leads could become gold? ProPair thinks so. They’re using machine learning (ML) to turn forgotten contacts into fresh opportunities.

The Problem: Dead Lead Lists

Most companies have them: long lists of leads that went nowhere. These contacts showed interest but never bought. Many businesses just give up on them. But that’s leaving money on the table.

ML Magic for Lead Sorting

ProPair’s ML system breathes new life into these sleepy leads. Here’s the breakdown:

1. Data Crunching

The ML algorithm digs through tons of old sales data. It looks at lead info and past sales patterns.

2. Smart Scoring

Based on what it finds, the system gives each lead a "predictive value". This shows how likely they are to buy.

3. Perfect Pairing

ProPair’s DECISIONS platform goes a step further. It matches leads with the sales agents most likely to close the deal, based on past performance.

This ML approach kicks guesswork to the curb. It replaces old, static methods with smart, data-driven decisions.

The Proof is in the Numbers

ProPair’s ML lead scoring isn’t just cool tech – it gets results. Check this out:

Q2 2024 study: 46% more sales using ProPair’s system vs. old methods
Conversion rates: 2.5% with ProPair vs. 1.7% the old way
Big sample size: Over 390,000 leads analyzed

Real people are seeing real results. Dan Stevens from NBKC Bank says:

"ProPair has changed everything about lead distribution for us. We look back at our pre-ProPair days and wonder why we relied on gut feelings for such big decisions."

Michael Zerr from BNC National Bank adds:

"We use ProPair’s match-and-rank software in Velocify and it’s been a game-changer. It helps us get the right leads to the right loan officers at the right time."

The big lesson? Don’t write off old leads. With smart ML tools, those forgotten contacts might be your next big sale. As ProPair puts it: "Just because leads are old doesn’t mean they can’t convert."

Intelliarts: Scoring Insurance Leads

Insurance companies live and die by their leads. Intelliarts tackled this head-on, building a machine learning (ML) solution to revolutionize lead scoring for a midsize property insurer.

Data Cleanup

First up: taming the data beast. Intelliarts gathered info from all over:

How leads interacted with the company’s website
Demographics like age and credit scores
Property details (size, age)

They built a custom Python module to fix missing data and weird outliers. Why? Because even the smartest ML model can’t work magic with garbage data.

Finding the Gold

With clean data in hand, Intelliarts dug for the factors that REALLY predict lead quality. They found some surprises:

Suburban leads converted better
Income wasn’t a straight line to success
Some lead sources were WAY better than others

They used this to build a scoring system. Standard Census Framework (SCF) codes below 20% conversion? "Bad." Above? "Good." Simple, but it nailed 91% accuracy in predicting the good ones.

Show Me the Money

The results? Pretty darn impressive:

90%+ accuracy in predicting lead quality
1.5% profit boost by cutting just 6% of bad leads
Best months saw 2.5% profit jumps
High-scoring leads converted 3.5x better than average

Jacob Rodriguez from ActiveProspect put it bluntly:

"In the insurance world, too many bad customers making too many claims can really destroy your loss ratios."

Intelliarts’ solution tackled this head-on.

The insurance company’s VP of Product Management was a fan:

"The workflow was effective. Intelliarts was always available and met with us regularly to discuss progress."

This wasn’t just about numbers. It changed how the company worked. Top agents got the best leads, boosting efficiency AND motivation.

The big lesson? In insurance, all leads are NOT created equal. ML helps insurers work smarter, turning data into dollars and changing the lead scoring game.

Carson Group: Finding Investment Clients

Carson Group, a top financial advisory firm, jumped into machine learning (ML) to change how they find and score investment clients. Their story shows how mixing data sources and building smart lead scoring systems can shake up a business.

Building the System

Carson Group teamed up with Provectus, an AWS Premier Consulting Partner, to create a cutting-edge ML model for lead scoring. Here’s what they did:

They pulled data from all over – Salesforce, impression reports, click data, and spend info. Then, they built a custom ML solution to replace their old, complicated rules. The new system could easily grab fresh data from different sources and send results to the right teams.

Aaron Schaben from Carson Group put it this way:

"Generating unforgettable and unmistakably valuable moments of engagement is what advisors need most, especially when it comes to organic growth."

Training the ML Model

Carson Group’s ML model didn’t pop up overnight. They dug into old sales data to see what made leads convert. They used carefully labeled lead data to teach the model what "good" and "bad" leads look like. And they made sure the system kept learning as new data came in.

This approach let Carson Group move past gut feelings and old rules, embracing a data-driven future for lead scoring.

Sales Improvement Numbers

The results? Pretty impressive:

The model was 96% accurate in predicting lead conversion.
It correctly spotted 88% of the actual convertible leads.
Two-thirds of the leads it flagged as high-potential actually converted.

These numbers meant big things for Carson Group:

They now work with $33 billion in assets under management (AUM).
They partner with over 140 advisory firms.
In 2023, they made three full acquisitions, including Northwest Capital Management ($5 billion in AUM).

Brent Petty from Northwest Capital was pumped about joining Carson:

"Our affiliation with Carson will allow us to expand our resources considerably, gaining a larger support staff for client service, financial planning, investment research, portfolio analysis, trading and investment monitoring."

The ML approach didn’t just make lead scoring better – it changed Carson Group’s whole business. By focusing on high-potential leads, they’ve made their marketing smarter, cut costs, and boosted conversion rates big time.

This story shows that in investment advisory, using ML for lead scoring isn’t just about keeping up – it’s about getting way ahead of the competition.

Snowflake: Sorting Leads by Type

Snowflake’s cloud-based data storage is changing how companies score leads. Their machine learning (ML) tech helps businesses sort and score leads better than ever.

Here’s how it works:

1. Set up the data warehouse

Companies create a warehouse, database, and schema in Snowflake. This is where all the lead scoring magic happens.

2. Gather the data

Customer info from different places gets pulled into one central system. This includes stuff like how people use your website, who they are, and other important details.

3. Clean up the data

A special Python program fixes any messy or missing data. This step is crucial because even the smartest ML model needs good data to work with.

4. Train the model

The system uses past data to teach the ML model what makes a good lead. One company even made a system that puts customers into bronze, silver, and gold groups based on how much they’ve spent.

5. Score new leads

Once it’s trained, the model can look at new leads and give them a score or put them in a category. This helps sales teams know who to focus on first.

Snowflake’s lead scoring system works well with other tools you might use:

It uses Snowpark, which lets data scientists use Python for complex stuff, all inside Snowflake.
It can connect to Salesforce, so sales teams can see lead scores right where they usually work.
It works with lots of open-source Python tools for machine learning.

How do we know it’s working? Here are some results:

One company’s system got it right 96% of the time when predicting if a lead would convert.
Sales teams say they save a lot of time by focusing on the leads the ML model says are best.
It helps companies make decisions based on data, not just gut feelings. For example, a juice company learned what makes customers rate their products highly by looking at things like price and how acidic the juice is.
As companies grow and get more data, Snowflake can easily grow with them without slowing down.

AI WarmLeads: Converting Website Visitors

AI WarmLeads is shaking up lead scoring by turning random site visitors into hot prospects. It’s a game-changer for businesses aiming to pump up their lead gen and conversion rates.

Finding Leads with AI

AI WarmLeads uses smart tech to spot potential buyers from your site traffic. Here’s the scoop:

It tracks how people navigate your site, which pages they hit, and how long they stick around. It’s like having a super-smart detective watching your visitors non-stop. This AI picks up on tiny hints that someone might be ready to buy, even if they don’t fill out a form.

Say someone spends ages on your pricing page or keeps coming back to your case studies. AI WarmLeads flags them as a hot lead. It’s way smarter than just counting page views.

Working with Sales Tools

AI WarmLeads plays well with others. It syncs up with your sales team’s existing tools, making life easier and more productive.

The system can:

Zap lead info straight to your CRM
Fire off personalized emails to warm leads
Ping your sales team when a big fish is on the site

This automation is a huge time-saver. In fact, AI can free up about an hour each day for your sales reps. That’s more time for closing deals and less time drowning in paperwork.

Customer Growth Results

The numbers don’t lie, and AI WarmLeads is crushing it:

One company’s sales pipeline grew by 30% after using AI for lead qualification.
Another business saw their win rate jump by 20%.

But it’s not just about more leads. It’s about BETTER leads. AI WarmLeads helps you zero in on the people most likely to buy. No more wasting time on dead-end prospects.

"AI helps us analyze user behaviors at scale, letting us deliver custom recommendations and content. This boosts engagement and satisfaction." – Contentsquare

This quote shows that AI isn’t just about finding leads, it’s about really getting to know them.

Check out how AI WarmLeads stacks up against old-school lead scoring:

Metric	Traditional Method	With AI WarmLeads
Lead Conversion Rate	5%	15%
Time Spent on Lead Scoring	10 hours/week	2 hours/week
Sales Team Productivity	Baseline	50% increase

These numbers tell the story: AI WarmLeads isn’t just a small step forward. It’s a complete overhaul of how businesses find and nurture leads.

Key Takeaways

ML is changing the game for lead scoring in 2024. Here’s what we’ve learned:

Good data is everything. ProPair showed that old leads can be gold when ML digs in. But Intelliarts proved you need clean data first.

ML finds what humans miss. Carson Group hit 96% accuracy predicting conversions. ML spots buying signals we can’t see.

It’s gotta work with your tools. Snowflake nailed it by making their ML play nice with Salesforce and Python. That’s how you get people to actually use it.

Keep learning, or get left behind. AI WarmLeads grew sales pipelines 30% with models that adapt on the fly. The market changes, so should your ML.

Robots can’t do it all. AI WarmLeads saved an hour a day for sales reps. But humans still close deals and build relationships.

What’s next? Look out for ML that can:

Tell you when a lead will convert, not just if
Use market data to tweak scores based on what’s happening in the economy
Read emails and chats to score leads based on how they sound

The future of lead scoring? It’s all about data. But you’ve got to use ML smart, keep your data clean, and remember that AI and humans work best as a team.

5 ML Lead Scoring Case Studies 2024

5 ML Lead Scoring Case Studies 2024